A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade

Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features

Simple Summary Contrast-enhanced mammography (CEM) is a dual-energy technique where low- and high-energy images are acquired for each mammographic view after contrast agent administration, and are then recombined to enhance potential contrast uptake. As CEM is increasingly used for both screening and diagnostic applications in breast imaging, but its associated radiation dose has been investigated only by single-center studies, we aimed to evaluate the CEM per-patient radiation dose on a large population in a bicentric setting, pooling data from two prospective studies employing the same model of mammography units. The CEM radiation dose showed a 6.2% difference between the two centers, mainly attributable to the study populations’ characteristics and to manufacturing differences between the two systems. The CEM dose was about 30% higher than that of standard digital mammography. Such an increment was close to the dose increase reported for digital breast tomosynthesis, which is already used in both screening and clinical settings. Thus, considering the extensively demonstrated diagnostic gain granted by CEM over these non-contrast-enhanced techniques, radiation dose concerns should not hinder ever-wider clinical implementations of CEM. Abstract The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective studies, using the same type of equipment. The CEM mean glandular dose (MGD) was computed for low energy (LE) and high energy (HE) images and their sum was calculated for each view. MGD and related parameters (entrance dose, breast thickness, compression, and density) were compared between the two centers using the Mann–Whitney test. Finally, per-patient MGD was calculated by pooling the two datasets and determining the contribution of LE and HE images. A total of 348 CEM examinations were analyzed (228 from Center 1 and 120 from Center 2). The median total MGD per view was 2.33 mGy (interquartile range 2.19–2.51 mGy) at Center 1 and 2.46 mGy (interquartile range 2.32–2.70 mGy) at Center 2, with a 0.15 mGy median difference (p < 0.001) equal to 6.2%. LE-images contributed between 64% and 77% to the total patient dose in CEM, with the remaining 23–36% being associated with HE images. The mean radiation dose for a two-view bilateral CEM exam was 4.90 mGy, about 30% higher than for digital mammography.

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Radiation Dose of Contrast-Enhanced Mammography: A Two-Center Prospective Comparison

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

https://www.mdpi.com/2075-4418/12/11/2644/pdf?version=1667267634

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience

Abstract Objectives The aim of this single-centre, observational, retrospective study is to find a correlation using Radiomics between the analysis of CT texture features of primary lesion of neuroendocrine (NET) lung cancer subtypes (typical and atypical carcinoids, large and small cell neuroendocrine carcinoma), Ki-67 index and the presence of lymph nodal mediastinal metastases. Methods Twenty-seven patients (11 males and 16 females, aged between 48 and 81 years old—average age of 70,4 years) with histological diagnosis of pulmonary NET with known Ki-67 status and metastases who have performed pre-treatment CT in our department were included. All examinations were performed with the same CT scan (Sensation 16-slice, Siemens). The study protocol was a baseline scan followed by 70 s delay acquisition after administration of intravenous contrast medium. After segmentation of primary lesions, quantitative texture parameters of first and higher orders were extracted. Statistics nonparametric tests and linear correlation tests were conducted to evaluate the relationship between different textural characteristics and tumour subtypes. Results Statistically significant ( p &lt; 0.05) differences were seen in post-contrast enhanced CT in multiple first and higher-order extracted parameters regarding the correlation with classes of Ki-67 index values. Statistical analysis for direct acquisitions was not significant. Concerning the correlation with the presence of metastases, one histogram feature (Skewness) and one feature included in the Gray-Level Co-occurrence Matrix (ClusterShade) were significant on contrast-enhanced CT only. Conclusions CT texture analysis may be used as a valid tool for predicting the subtype of lung NET and its aggressiveness. 

https://link.springer.com/content/pdf/10.1007/s11547-022-01494-5.pdf

Radiomics in pulmonary neuroendocrine tumours (NETs)

Single Center Evaluation of Comparative Breast Radiation dose of Contrast Enhanced Digital Mammography (CEDM), Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT).

S. Bettarini

Papers

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade

Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience

Radiomics in pulmonary neuroendocrine tumours (NETs)

Single Center Evaluation of Comparative Breast Radiation dose of Contrast Enhanced Digital Mammography (CEDM), Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT).

FLORA software: semi-automatic LGE-CMR analysis tool for cardiac lesions identification and characterization