Showing papers in "European Radiology Experimental in 2020"

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

Abstract: Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited. We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets. Using different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. When trained on a diverse routine dataset (n = 36), a standard approach (U-net) yields a higher DSC (0.97 ± 0.05) compared to training on public datasets such as the Lung Tissue Research Consortium (0.94 ± 0.13, p = 0.024) or Anatomy 3 (0.92 ± 0.15, p = 0.001). Trained on routine data (n = 231) covering multiple diseases, U-net compared to reference methods yields a DSC of 0.98 ± 0.03 versus 0.94 ± 0.12 (p = 0.024). The accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. Efforts in developing new datasets and providing trained models to the public are critical. By releasing the trained model under General Public License 3.0, we aim to foster research on lung diseases by providing a readily available tool for segmentation of pathological lungs.

Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach

Abstract: Here, we summarise the unresolved debate about p value and its dichotomisation. We present the statement of the American Statistical Association against the misuse of statistical significance as well as the proposals to abandon the use of p value and to reduce the significance threshold from 0.05 to 0.005. We highlight reasons for a conservative approach, as clinical research needs dichotomic answers to guide decision-making, in particular in the case of diagnostic imaging and interventional radiology. With a reduced p value threshold, the cost of research could increase while spontaneous research could be reduced. Secondary evidence from systematic reviews/meta-analyses, data sharing, and cost-effective analyses are better ways to mitigate the false discovery rate and lack of reproducibility associated with the use of the 0.05 threshold. Importantly, when reporting p values, authors should always provide the actual value, not only statements of “p < 0.05” or “p ≥ 0.05”, because p values give a measure of the degree of data compatibility with the null hypothesis. Notably, radiomics and big data, fuelled by the application of artificial intelligence, involve hundreds/thousands of tested features similarly to other “omics” such as genomics, where a reduction in the significance threshold, based on well-known corrections for multiple testing, has been already adopted.

Imaging of tumour response to immunotherapy

Abstract: A wide range of cancer immunotherapy approaches has been developed including non-specific immune-stimulants such as cytokines, cancer vaccines, immune checkpoint inhibitors (ICIs), and adoptive T cell therapy. Among them, ICIs are the most commonly used and intensively studied. Since 2011, these drugs have received marketing authorisation for melanoma, lung, bladder, renal, and head and neck cancers, with remarkable and long-lasting treatment response in some patients. The novel mechanism of action of ICIs, with immune and T cell activation, leads to unusual patterns of response on imaging, with the advent of so-called pseudoprogression being more pronounced and frequently observed when compared to other anticancer therapies. Pseudoprogression, described in about 2–10% of patients treated with ICIs, corresponds to an increase of tumour burden and/or the appearance of new lesions due to infiltration by activated T cells before the disease responds to therapy. To overcome the limitation of response evaluation criteria in solid tumors (RECIST) to assess these specific changes, new imaging criteria—so-called immune-related response criteria and then immune-related RECIST (irRECIST)—were proposed. The major modification involved the inclusion of the measurements of new target lesions into disease assessments and the need for a 4-week re-assessment to confirm or not confirm progression. The RECIST working group introduced the new concept of “unconfirmed progression”, into the irRECIST. This paper reviews current immunotherapeutic approaches and summarises radiologic criteria to evaluate new patterns of response to immunotherapy. Furthermore, imaging features of immunotherapy-related adverse events and available predictive biomarkers of response are presented.

Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome.

Abstract: Computed tomography (CT) enables quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, helping in outcome prediction From 1 to 22 March 2020, patients with pneumonia symptoms, positive lung CT scan, and confirmed SARS-CoV-2 on reverse transcription-polymerase chain reaction (RT-PCR) were consecutively enrolled Clinical data was collected Outcome was defined as favourable or adverse (ie, need for mechanical ventilation or death) and registered over a period of 10 days following CT Volume of disease (VoD) on CT was calculated semi-automatically Multiple linear regression was used to predict VoD by clinical/laboratory data To predict outcome, important features were selected using a priori analysis and subsequently used to train 4 different models A total of 106 consecutive patients were enrolled (median age 635 years, range 26–95 years; 41/106 women, 387%) Median duration of symptoms and C-reactive protein (CRP) was 5 days (range 1–30) and 494 mg/L (range 01–283), respectively Median VoD was 2495 cm3 (range 99–1505) and was predicted by lymphocyte percentage (p = 0008) and CRP (p < 0001) Important variables for outcome prediction included CRP (area under the curve [AUC] 077), VoD (AUC 075), age (AUC 072), lymphocyte percentage (AUC 070), coronary calcification (AUC 068), and presence of comorbidities (AUC 066) Support vector machine had the best performance in outcome prediction, yielding an AUC of 092 Measuring the VoD using a simple CT post-processing tool estimates SARS-CoV-2 burden CT and clinical data together enable accurate prediction of short-term clinical outcome

Chest x-ray severity score in COVID-19 patients on emergency department admission: a two-centre study.

Abstract: Integration of imaging and clinical parameters could improve the stratification of COVID-19 patients on emergency department (ED) admission. We aimed to assess the extent of COVID-19 pulmonary abnormalities on chest x-ray (CXR) using a semiquantitative severity score, correlating it with clinical data and testing its interobserver agreement. From February 22 to April 8, 2020, 926 consecutive patients referring to ED of two institutions in Northern Italy for suspected SARS-CoV-2 infection were reviewed. Patients with reverse transcriptase-polymerase chain reaction positive for SARS-CoV-2 and CXR images on ED admission were included (295 patients, median age 69 years, 199 males). Five readers independently and blindly reviewed all CXRs, rating pulmonary parenchymal involvement using a 0–3 semiquantitative score in 1-point increments on 6 lung zones (range 0–18). Interobserver agreement was assessed with weighted Cohen’s κ, correlations between median CXR score and clinical data with Spearman’s ρ, and the Mann-Whitney U test. Median score showed negative correlation with SpO2 (ρ = -0.242, p < 0.001), positive correlation with white cell count (ρ = 0.277, p < 0.001), lactate dehydrogenase (ρ = 0.308, p < 0.001), and C-reactive protein (ρ = 0.367, p < 0.001), being significantly higher in subsequently dead patients (p = 0.003). Considering overall scores, readers’ pairings yielded moderate (κ = 0.449, p < 0.001) to almost perfect interobserver agreement (κ = 0.872, p < 0.001), with better interobserver agreement between readers of centre 2 (up to κ = 0.872, p < 0.001) than centre 1 (κ = 0.764, p < 0.001). Proposed CXR pulmonary severity score in COVID-19 showed moderate to almost perfect interobserver agreement and significant but weak correlations with clinical parameters, potentially furthering CXR integration in patients’ stratification.

Integrating radiomics into holomics for personalised oncology: from algorithms to bedside.

Abstract: Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other “omics” data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other “omics” into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called “holomics”, will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists.

PET/CT imaging for tumour response assessment to immunotherapy: current status and future directions

Abstract: Recent immunotherapeutic approaches have evolved as powerful treatment options with high anti-tumour responses involving the patient’s own immune system. Passive immunotherapy applies agents that enhance existing anti-tumour responses, such as antibodies against immune checkpoints. Active immunotherapy uses agents that direct the immune system to attack tumour cells by targeting tumour antigens. Active cellular-based therapies are on the rise, most notably chimeric antigen receptor T cell therapy, which redirects patient-derived T cells against tumour antigens. Approved treatments are available for a variety of solid malignancies including melanoma, lung cancer and haematologic diseases. These novel immune-related therapeutic approaches can be accompanied by new patterns of response and progression and immune-related side-effects that challenge established imaging-based response assessment criteria, such as Response Evaluation Criteria in Solid tumours (RECIST) 1.1. Hence, new criteria have been developed. Beyond morphological information of computed tomography (CT) and magnetic resonance imaging, positron emission tomography (PET) emerges as a comprehensive imaging modality by assessing (patho-)physiological processes such as glucose metabolism, which enables more comprehensive response assessment in oncological patients. We review the current concepts of response assessment to immunotherapy with particular emphasis on hybrid imaging with 18F-FDG-PET/CT and aims at describing future trends of immunotherapy and additional aspects of molecular imaging within the field of immunotherapy.

Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Abstract: Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too.

PRIMAGE project : predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

Abstract: PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.

Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method.

Abstract: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method. Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method. Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system. A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.

Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases

Abstract: Radiomics is expected to improve the management of metastatic colorectal cancer (CRC). We aimed at evaluating the impact of liver lesion contouring as a source of variability on radiomic features (RFs). After Ethics Committee approval, 70 liver metastases in 17 CRC patients were segmented on contrast-enhanced computed tomography scans by two residents and checked by experienced radiologists. RFs from grey level co-occurrence and run length matrices were extracted from three-dimensional (3D) regions of interest (ROIs) and the largest two-dimensional (2D) ROIs. Inter-reader variability was evaluated with Dice coefficient and Hausdorff distance, whilst its impact on RFs was assessed using mean relative change (MRC) and intraclass correlation coefficient (ICC). For the main lesion of each patient, one reader also segmented a circular ROI on the same image used for the 2D ROI. The best inter-reader contouring agreement was observed for 2D ROIs according to both Dice coefficient (median 0.85, interquartile range 0.78–0.89) and Hausdorff distance (0.21 mm, 0.14–0.31 mm). Comparing RF values, MRC ranged 0–752% for 2D and 0–1567% for 3D. For 24/32 RFs (75%), MRC was lower for 2D than for 3D. An ICC > 0.90 was observed for more RFs for 2D (53%) than for 3D (34%). Only 2/32 RFs (6%) showed a variability between 2D and circular ROIs higher than inter-reader variability. A 2D contouring approach may help mitigate overall inter-reader variability, albeit stable RFs can be extracted from both 3D and 2D segmentations of CRC liver metastases.

Patient organ and effective dose estimation in CT: comparison of four software applications

Abstract: Radiation dose in computed tomography (CT) has become a topic of high interest due to the increasing numbers of CT examinations performed worldwide. Hence, dose tracking and organ dose calculation software are increasingly used. We evaluated the organ dose variability associated with the use of different software applications or calculation methods. We tested four commercial software applications on CT protocols actually in use in our hospital: CT-Expo, NCICT, NCICTX, and Virtual Dose. We compared dose coefficients, estimated organ doses and effective doses obtained by the four software applications by varying exposure parameters. Our results were also compared with estimates reported by the software authors. All four software applications showed dependence on tube voltage and volume CT dose index, while only CT-Expo was also dependent on other exposure parameters, in particular scanner model and pitch caused a variability till 50%. We found a disagreement between our results and those reported by the software authors (up to 600%), mainly due to a different extent of examined body regions. The relative range of the comparison of the four software applications was within 35% for most organs inside the scan region, but increased over the 100% for organs partially irradiated and outside the scan region. For effective doses, this variability was less evident (ranging from 9 to 36%). The two main sources of organ dose variability were the software application used and the scan region set. Dose estimate must be related to the process used for its calculation.

Ultra-high-field 7-T MRI in multiple sclerosis and other demyelinating diseases: from pathology to clinical practice

Abstract: Magnetic resonance imaging (MRI) is essential for the early diagnosis of multiple sclerosis (MS), for investigating the disease pathophysiology, and for discriminating MS from other neurological diseases. Ultra-high-field strength (7-T) MRI provides a new tool for studying MS and other demyelinating diseases both in research and in clinical settings. We present an overview of 7-T MRI application in MS focusing on increased sensitivity and specificity for lesion detection and characterisation in the brain and spinal cord, central vein sign identification, and leptomeningeal enhancement detection. We also discuss the role of 7-T MRI in improving our understanding of MS pathophysiology with the aid of metabolic imaging. In addition, we present 7-T MRI applications in other demyelinating diseases. 7-T MRI allows better detection of the anatomical, pathological, and functional features of MS, thus improving our understanding of MS pathology in vivo. 7-T MRI also represents a potential tool for earlier and more accurate diagnosis.

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

Abstract: Automatically detecting and quantifying pneumothorax on chest computed tomography (CT) may impact clinical decision-making. Machine learning methods published so far struggle with the heterogeneity of technical parameters and the presence of additional pathologies, highlighting the importance of stable algorithms. A deep residual UNet was developed and evaluated for automated, volume-level pneumothorax grading (i.e., labelling a volume whether a pneumothorax was present or not), and pixel-level classification (i.e., segmentation and quantification of pneumothorax), on a retrospective series of routine chest CT data. Ground truth annotations were provided by radiologists. The fully automated pixel-level pneumothorax segmentation method was trained using 43 chest CT scans and evaluated on 9 chest CT scans with pixel-level annotation basis and 567 chest CT scans on a volume-level basis. This method achieved a receiver operating characteristic area under the curve (AUC) of 0.98, an average precision of 0.97, and a Dice similarity coefficient (DSC) of 0.94. This segmentation performance resulted to be similar to the inter-rater segmentation accuracy of two radiologists, who achieved a DSC of 0.92. The comparison of manual and automated pneumothorax quantification yielded a Pearson correlation coefficient of 0.996. The volume-level pneumothorax grading accuracy was evaluated on 567 chest CT scans and yielded an AUC of 0.98 and an average precision of 0.95. We proposed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data that may facilitate the automated triage of urgent examinations and enable treatment decision support.

Image-guided laser ablation in the treatment of recurrence of renal tumours: technique and preliminary results.

Abstract: Abdominal recurrences of renal cell carcinoma (RCC) after surgery might represent a challenge for treatment, often requiring difficult surgeries or anticipated systemic therapy. Our aim is to illustrate a novel application of laser ablation for the treatment of abdominal recurrences of RCC. Patients with abdominal recurrences of renal cancer were treated under ultrasound/computed tomography guidance with a diode laser inserted into the lesion through a thin 21-G needle. A fixed 3-W power protocol was used, changing the illumination time according to lesion dimension and shape. Also, technical success, technical efficacy, local tumour progression, and major and minor complications were retrospectively analysed. Three patients were treated with image-guided laser ablation for abdominal recurrences of RCC. In all cases, it was possible to perform ablation as preoperatively planned and all three nodules (size of 6, 8, and 12 mm) were completely ablated with no evidence of residual enhancement after 6 weeks at contrast-enhanced CT. No minor or major complications were observed. No local tumour progression was reported up to 12 months from ablation. Image-guided laser ablation holds the potential to offer a minimally invasive treatment to patients with abdominal recurrence of RCC. Further studies are needed to evaluate the clinical role of this technique.

Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data.

Abstract: To investigate the potential of semiquantitative time-intensity curve parameters compared to textural radiomic features on arterial phase images by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for early prediction of breast cancer neoadjuvant therapy response. A retrospective study of 45 patients subjected to DCE-MRI by public datasets containing examination performed prior to the start of treatment and after the treatment first cycle (‘QIN Breast DCE-MRI’ and ‘QIN-Breast’) was performed. In total, 11 semiquantitative parameters and 50 texture features were extracted. Non-parametric test, receiver operating characteristic analysis with area under the curve (ROC-AUC), Spearman correlation coefficient, and Kruskal-Wallis test with Bonferroni correction were applied. Fifteen patients with pathological complete response (pCR) and 30 patients with non-pCR were analysed. Significant differences in median values between pCR patients and non-pCR patients were found for entropy, long-run emphasis, and busyness among the textural features, for maximum signal difference, washout slope, washin slope, and standardised index of shape among the dynamic semiquantitative parameters. The standardised index of shape had the best results with a ROC-AUC of 0.93 to differentiate pCR versus non-pCR patients. The standardised index of shape could become a clinical tool to differentiate, in the early stages of treatment, responding to non-responding patients.

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

Abstract: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method (“training with input selection and testing”) was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs). A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87–100%), a specificity of 37/41 (90%, 95% CI 77–97%), and an accuracy of 64/68 (94%, 95% CI 86–98%). This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.

Computed tomography for myocardial characterization in ischemic heart disease: a state-of-the-art review.

Abstract: This review provides an overview of the currently available computed tomography (CT) techniques for myocardial tissue characterization in ischemic heart disease, including CT perfusion and late iodine enhancement. CT myocardial perfusion imaging can be performed with static and dynamic protocols for the detection of ischemia and infarction using either single- or dual-energy CT modes. Late iodine enhancement may be used for the analysis of myocardial infarction. The accuracy of these CT techniques is highly dependent on the imaging protocol, including acquisition timing and contrast administration. Additionally, the options for qualitative and quantitative analysis and the accuracy of each technique are discussed.

Ultrasound-guided interventions with augmented reality in situ visualisation: a proof-of-mechanism phantom study.

Abstract: Ultrasound (US) images are currently displayed on monitors, and their understanding needs good orientation skills. Direct overlay of US images onto the according anatomy is possible with augmented reality (AR) technologies. Our purpose was to explore the performance of US-guided needle placement with and without AR in situ US viewing. Three untrained operators and two experienced radiologists performed 200 US-guided punctures: 100 with and 100 without AR in situ US. The punctures were performed in two different phantoms, a leg phantom with soft tissue lesions and a vessel phantom. Time to puncture and number of needle passes were recorded for each puncture. Data are reported as median [range] according to their non-normal distribution. AR in situ US resulted in reduced time (median [range], 13 s [3–101] versus 14 s [3–220]) and number of needle passes (median [range], 1 [1–4] versus 1 [1–8]) compared to the conventional technique. The initial gap in performance of untrained versus experienced operators with the conventional US (time, 21.5 s [3–220] versus 10.5 s [3–94] and needle passes 1 [1–8] versus 1 [1, 2]) was reduced to 12.5 s [3–101] versus 13 s [3–100] and 1 [1–4] versus 1 [1–4] when using AR in situ US, respectively. AR in situ US could be a potential breakthrough in US applications by simplifying operator’s spatial orientation and reducing experience-based differences in performance of US-guided interventions. Further studies are needed to confirm these preliminary phantom results.

Monitoring treatment effects in lung cancer-bearing mice: clinical CT and clinical MRI compared to micro-CT

Abstract: Compared to histology-based methods, imaging can reduce animal usage in preclinical studies. However, availability of dedicated scanners is limited. We evaluated clinical computed tomography (CT) and magnetic resonance imaging (MRI) in comparison to dedicated CT (micro-CT) for assessing therapy effects in lung cancer-bearing mice. Animals received cisplatin (n = 10), sham (n = 12), or no treatment (n = 9). All were examined via micro-CT, CT, and MRI before and after treatment. Semiautomated tumour burden (TB) calculation was performed. The Bland-Altman, receiver operating characteristic (ROC), and Spearman statistics were used. All modalities always allowed localising and measuring TB. At all modalities, mice treated with cisplatin showed a TB reduction (p ≤ 0.012) while sham-treated and untreated individuals presented tumour growth (p < 0.001). Mean relative difference (limits of agreement) between TB on micro-CT and clinical scanners was 24.7% (21.7–27.7%) for CT and 2.9% (−4.0–9.8%) for MRI. Relative TB changes before/after treatment were not different between micro-CT and CT (p = 0.074) or MRI (p = 0.241). Mice with cisplatin treatment were discriminated from those with sham or no treatment at all modalities (p ≤ 0.001). Using micro-CT as reference standard, ROC areas under the curves were 0.988–1.000 for CT and 0.946–0.957 for MRI. TB changes were highly correlated across modalities (r ≥ 0.900, p < 0.001). Clinical CT and MRI are suitable for treatment response evaluation in lung cancer-bearing mice. When dedicated scanners are unavailable, they should be preferred to improve animal welfare.

Percutaneous afferent lymphatic vessel sclerotherapy for postoperative lymphatic leakage after previous ineffective therapeutic transpedal lymphangiography.

Abstract: To demonstrate the efficacy of percutaneous computed tomography (CT)-guided afferent lymphatic vessel sclerotherapy (ALVS) in the treatment of postoperative lymphatic leakage (LL) after ineffective therapeutic transpedal lymphangiography (TL). A retrospective review in this institute involving 201 patients was conducted from May 2011 to September 2018. Patients diagnosed with postoperative LL undergoing ineffective therapeutical TL before the performance of percutaneous CT-guided ALVS were involved. Technical success and clinical success of TL and ALVS were established. The technical success and efficacy of ALVS in the treatment of postoperative LL after ineffective therapeutic TL were assessed. The clinical success rate of ALVS is also assessed, and the complications are reviewed. In total, nine patients were involved including three patients (33.3%) presented with chylothorax, three patients (33.3%) presented with inguinal lymphatic fistula/lymphocele, and three patients (33.3%) presented with lymphatic fistula in the thigh; 27 ± 18 days (mean ± standard deviation) after surgery, therapeutic TL was successfully performed and showed definite afferent lymphatic vessel and leakage site in all the patients. Due to clinical failure after TLs, the following ALVS was performed with a mean interval of 12 ± 8 days after TL. The technical success rate was 9/9 (100.0%, 95% confidence interval [CI] 63.1–100.0%). An average of 2.7 ± 1.3 mL 95% ethanol as sclerosant agent was injected during the procedure. The clinical success was observed in 8 of the 9 patients (88.9%, 95% CI 51.8–99.7%) with a time between ALVS and the LL cure of 8 ± 6 days. No complications were reported. Our results showed the role of percutaneous CT-guided ALVS as a safe, feasible, and effective salvage treatment for postoperative LL after ineffective TL.

Image-based biomechanical models of the musculoskeletal system.

Abstract: Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described.

Quantitative imaging: systematic review of perfusion/flow phantoms

Abstract: We aimed at reviewing design and realisation of perfusion/flow phantoms for validating quantitative perfusion imaging (PI) applications to encourage best practices. A systematic search was performed on the Scopus database for “perfusion”, “flow”, and “phantom”, limited to articles written in English published between January 1999 and December 2018. Information on phantom design, used PI and phantom applications was extracted. Of 463 retrieved articles, 397 were rejected after abstract screening and 32 after full-text reading. The 37 accepted articles resulted to address PI simulation in brain (n = 11), myocardial (n = 8), liver (n = 2), tumour (n = 1), finger (n = 1), and non-specific tissue (n = 14), with diverse modalities: ultrasound (n = 11), computed tomography (n = 11), magnetic resonance imaging (n = 17), and positron emission tomography (n = 2). Three phantom designs were described: basic (n = 6), aligned capillary (n = 22), and tissue-filled (n = 12). Microvasculature and tissue perfusion were combined in one compartment (n = 23) or in two separated compartments (n = 17). With the only exception of one study, inter-compartmental fluid exchange could not be controlled. Nine studies compared phantom results with human or animal perfusion data. Only one commercially available perfusion phantom was identified. We provided insights into contemporary phantom approaches to PI, which can be used for ground truth evaluation of quantitative PI applications. Investigators are recommended to verify and validate whether assumptions underlying PI phantom modelling are justified for their intended phantom application.

Musculoskeletal MRI at 7 T: do we need more or is it more than enough?

Abstract: Ultra-high field magnetic resonance imaging (UHF-MRI) provides important diagnostic improvements in musculoskeletal imaging. The higher signal-to-noise ratio leads to higher spatial and temporal resolution which results in improved anatomic detail and higher diagnostic confidence. Several methods, such as T2, T2*, T1rho mapping, delayed gadolinium-enhanced, diffusion, chemical exchange saturation transfer, and magnetisation transfer techniques, permit a better tissue characterisation. Furthermore, UHF-MRI enables in vivo measurements by low-γ nuclei (23Na, 31P, 13C, and 39K) and the evaluation of different tissue metabolic pathways. European Union and Food and Drug Administration approvals for clinical imaging at UHF have been the first step towards a more routinely use of this technology, but some drawbacks are still present limiting its widespread clinical application. This review aims to provide a clinically oriented overview about the application of UHF-MRI in the different anatomical districts and tissues of musculoskeletal system and its pros and cons. Further studies are needed to consolidate the added value of the use of UHF-MRI in the routine clinical practice and promising efforts in technology development are already in progress.

Impact of iodine concentration and scan parameters on image quality, contrast enhancement and radiation dose in thoracic CT

Abstract: We investigated the impact of varying contrast medium (CM) densities and x-ray tube potentials on contrast enhancement (CE), image quality and radiation dose in thoracic computed tomography (CT) using two different scanning techniques. Seven plastic tubes containing seven different CM densities ranging from of 0 to 600 HU were positioned inside a commercial chest phantom with padding, representing three different patient sizes. Helical scans of the phantom in single-source mode were obtained with varying tube potentials from 70 to 140 kVp. A constant volume CT dose index (CTDIvol) depending on phantom size and automatic dose modulation was tested. CE (HU) and image quality (contrast-to-noise ratio, CNR) were measured for all combinations of CM density and tube potential. A reference threshold of CE and kVp was defined as ≥ 200 HU and 120 kVp. For the medium-sized phantom, with a specific CE of 100–600 HU, the diagnostic CE (200 HU) at 70 kVp was ~ 90% higher than at 120 kVp, for both scan techniques (p < 0.001). Changes in CM density/specific HU together with lower kVp resulted in significantly higher CE and CNR (p < 0.001). When changing only the kVp, no statistically significant differences were observed in CE or CNR (p ≥ 0.094), using both dose modulation and constant CTDIvol. For thoracic CT, diagnostic CE (≥ 200 HU) and maintained CNR were achieved by using lower CM density in combination with lower tube potential (< 120 kVp), independently of phantom size.

Inverse Laplace transform and multiexponential fitting analysis of T2 relaxometry data: a phantom study with aqueous and fat containing samples.

Abstract: The inverse Laplace transform (ILT) is the most widely used method for T2 relaxometry data analysis. This study examines the qualitative agreement of ILT and a proposed multiexponential (Mexp method) regarding the number of T2 components. We performed a feasibility study for the voxelwise characterisation of heterogeneous tissue with T2 relaxometry. Eleven samples of aqueous, fatty and mixed composition were analysed using ILT and Mexp. The phantom was imaged using a 1.5-T system with a single slice T2 relaxometry 25-echo Carr-Purcell-Meiboom-Gill sequence in order to obtain the T2 decay curve with 25 equidistant echo times. The adjusted R2 goodness of fit criterion was used to determine the number of T2 components using the Mexp method on a voxel-based analysis. Comparison of mean and standard deviation of T2 values for both methods was performed by fitting a Gaussian function to the ILT resulting vector. Phantom results showed pure monoexponential decay for acetone and water and pure biexponential behaviour for corn oil, egg yolk, and 35% fat milk cream, while mixtures of egg whites and yolks as well as milk creams with 12–20% fatty composition exhibit mixed monoexponential and biexponential behaviour at different fractions. The number of T2 components by the Mexp method was compared to the ILT-derived spectrum as ground truth. Mexp analysis with the adjusted R2 criterion can be used for the detection of the T2 distribution of aqueous, fatty and mixed samples with the added advantage of voxelwise mapping.

Emergency tips recanalisation and gastroesophageal varices embolisation with an ethylene vinyl alcohol copolymer agent (Squid) and detachable coils.

Abstract: Transjugular intrahepatic portosystemic shunt (TIPS) is currently indicated as first therapeutic option in the main complications of portal hypertension, including bleeding gastroesophageal varices and refractory ascites. In case of bleeding gastroesophageal varices, an adjuvant embolisation within TIPS can be useful to prevent rebleeding. In the present technical note, the management in emergency of a patient with haemorrhagic shock due to bleeding gastroesophageal varices and occluded TIPS is reported. TIPS recanalisation with an adjunctive stent and high-pressure balloon angioplasty and gastroesophageal varices embolisation using detachable coils and a non-adhesive liquid embolic agent were performed during the same emergent procedure. After the procedure, clinical stabilisation of the patient was achieved, with blood transfusions suspension and Blakemore tube removal. At 6 months, regular TIPS patency at colour Doppler and no rebleeding episodes were recorded. To our knowledge, whilst coils are routinely used for varices embolisation, non-adhesive liquid embolic agents have been never mentioned. Liquid embolic agents seem to provide a stable plug strengthening the embolising action of the coils. Further studies involving a cohort of patients with long-term follow-up will be necessary to confirm whether this association can be more effective than coils alone in gastroesophageal varices embolisation.

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

Abstract: To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). A total of 259 patients who underwent diagnostic abdominal CT (unenhanced, contrast-enhanced arterial, and venous phases) were included in this retrospective study. Child-Pugh scores were determined based on laboratory and clinical parameters. Linear regression (LR), Random Forest (RF), and convolutional neural network (CNN) algorithms were used to predict the Child-Pugh class. Their performances were compared to the prediction of experienced radiologists (ERs). Spearman correlation coefficients and accuracy were assessed for all predictive models. Additionally, a binary classification in low disease severity (Child-Pugh class A) and advanced disease severity (Child-Pugh class ≥ B) was performed. Eleven imaging features exhibited a significant correlation when adjusted for multiple comparisons with Child-Pugh class. Significant correlations between predicted and measured Child-Pugh classes were observed (ρLA = 0.35, ρRF = 0.32, ρCNN = 0.51, ρERs = 0.60; p < 0.001). Significantly better accuracies for the prediction of Child-Pugh classes versus no-information rate were found for CNN and ERs (p ≤ 0.034), not for LR and RF (p ≥ 0.384). For binary severity classification, the area under the curve at receiver operating characteristic analysis was significantly lower (p ≤ 0.042) for LR (0.71) and RF (0.69) than for CNN (0.80) and ERs (0.76), without significant differences between CNN and ERs (p = 0.144). The performance of a CNN in assessing Child-Pugh class based on multiphase abdominal CT images is comparable to that of ERs.

Effective dose and image optimisation of lateral lumbar spine radiography: a phantom study

Abstract: To investigate lateral lumbar spine radiography technical parameters for reduction of effective dose whilst maintaining image quality (IQ). Thirty-six radiograms of an anthropomorphic phantom were acquired using different exposure parameters: source-to-detector distance (SDD) (100, 130 or 150 cm), tube potential (75, 85 or 95 kVp), tube current × exposure time product (4.5, 9, 18 mAs) and additional copper (Cu) filter (no filter, 0.1-, 0.2-, or 0.3-mm thickness. IQ was assessed using an objective approach (contrast-to-noise-ratio [CNR] calculation and magnification measurement) and a perceptual approach (six observers); ED was estimated using the PCXMC 2.0 software. Descriptive statistics, paired t test, and intraclass correlation coefficient (ICC) were used. The highest ED (0.022 mSv) was found with 100 cm SSD, 75 kVp, 18 mAs, and without Cu filter, whilst the highest CNR (7.23) was achieved at 130 cm SSD, 75 kVp, 18 mAs, and without Cu filter. The lowest ED and CNR were generated at 150 cm SDD, 95 kVp, 4.5 mAs, and 0.3-mm Cu filter. All observers identified the relevant anatomical structures on all images with the lowest ED and IQ. The intra-observer (0.61–0.79) and inter-observer (0.55–0.82) ICC ranged from moderate to excellent. All relevant anatomical structures were identified on the lateral lumbar spine radiographs despite using low-dose protocols. The lowest ED (0.002 mSv) was obtained with 150 cm SDD, 95 kVp, 4.5 mAs, and 0.3-mm Cu filter. Further technical and clinical studies are needed to verify these preliminary findings.

Breast ultrasound: automated or hand-held? Exploring patients' experience and preference.

Abstract: Our aim was to compare women’s experience with automated breast ultrasound (ABUS) versus breast hand-held ultrasound (HHUS) and to evaluate their acceptance rate. After ethical approval, from October 2017 to March 2018, 79 consecutive patients were enrolled in this prospective study. On the same day, patients underwent HHUS followed by ABUS. Each patient’s experience was assessed using the modified testing morbidities index (TMI) (the lower the score, the better is the experience). Nine items were assessed for both techniques: seven directly related to the examination technique (pain or discomfort immediately before (preparation), during and after testing, fear or anxiety immediately before (preparation) and during testing, physical and mental function after testing) and two indirectly related to the examination technique (embarrassment during testing and overall satisfaction). Finally, we asked patients to choose between the two techniques for a potential next breast examination. Wilcoxon signed ranks test was used. The median TMI score for the seven items was found to be significantly better for HHUS (8, interquartile range [IQR] 7–11) compared to ABUS (9, IQR 8–12) (p = 0.003). The item ‘pain/discomfort during the test’ (p < 0.001) was significantly higher for ABUS compared to HHUS. Instead, the item ‘fear/anxiety before the test’ was higher for HHUS (p = 0.001). Overall, 40.5% of the patients chose HHUS, 29.1% chose ABUS, and 30.4% were unable to choose. ABUS and HHUS exams were well tolerated and accepted. However, HHUS was perceived to be less painful than ABUS.