Hypersensitivity pneumonitis is a pulmonary disease with symptoms of cough and dyspnea resulting from the inhalation of an antigen to which the subject has been previously sensitized,such as microbes,animal or plant proteins and certain chemicals that form haptens.The radiologic findings include diffuse ground-glass opacification,small centrilobular nodules,air trapping,fibrosis and emphysema.Bronchoalveolar lavage fluid reveals a lymphocyte alveolitis with CD4/CD8 decrease.The histopathologic process consists of chronic inflammation of the bronchi and peribronchiolar tissue,often with poorly defined granulomas.The diagnosis usually rests on a variable combination of findings from history,clinical manifestation,radiography,serology,bronchoalveolar lavage fluid and lung biopsy.Treatment includes avoiding the allergen and systemic corticosteroids.The long-term prognosis is usually good.

Key words: 
Hypersensitivity pneumonitis; Etiology; Clinical presentation; Diagnosis; Treatment

Hypersensitivity pneumonitis

Photothermal therapy (PTT) has attracted great attention due to its noninvasive and effective use against cancer. Various photothermal agents (PTAs) including organic and inorganic PTAs have been developed in the last decades. Organic PTAs based on small-molecule dyes exhibit great potential for future clinical applications considering their good biocompatibility and easy chemical modification or functionalization. In this review, we discuss the recent progress of organic PTAs based on small-molecule dyes for enhanced PTT. We summarize the strategies to improve the light penetration of PTAs, methods to enhance their photothermal conversion efficiency, how to optimize PTAs' delivery into deep tumors, and how to resist photobleaching under repeated laser irradiation. We hope that this review can rouse the interest of researchers in the field of PTAs based on small-molecule dyes and help them to fabricate next-generation PTAs for noninvasive cancer therapy.

Recent Development of Photothermal Agents (PTAs) Based on Small Organic Molecular Dyes.

To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model and expert radiologists. This study included 525 patients (309 women; median, 62 years) to develop models, and an independent cohort of 101 patients (57 women; median, 66 years) was used for validation. A size-based logistic model and deep learning models using 2.5-dimension (2.5D) and three-dimension (3D) CT images were developed to discriminate IAC from less invasive pathologies. Overall performance, discrimination, and calibration were assessed. Diagnostic performances of the three thoracic radiologists were compared with those of the deep learning model. The overall performances of the deep learning models (Brier score, 0.122 for the 2.5D DenseNet and 0.121 for the 3D DenseNet) were superior to those of the size-based logistic model (Brier score, 0.198). The area under the receiver operating characteristic curve (AUC) of the 2.5D DenseNet (0.921) was significantly higher than that of the 3D DenseNet (0.835; p = 0.037) and the size-based logistic model (0.836; p = 0.009). At equally high sensitivities of 90%, the 2.5D DenseNet showed significantly higher specificity (88.2%; all p < 0.05) and positive predictive value (97.4%; all p < 0.05) than other models. Model calibration was poor for all models (all p < 0.05). The 2.5D DenseNet had a comparable performance with the radiologists (AUC, 0.848–0.910). The 2.5D DenseNet model could be used as a highly sensitive and specific diagnostic tool to differentiate IACs among SSNs for surgical candidates. • The deep learning model developed using 2.5D DenseNet showed higher overall performance and discrimination than the size-based logistic model for the differentiation of invasive adenocarcinomas among subsolid nodules for surgical candidates.
 • The 2.5D DenseNet demonstrated a thoracic radiologist–level diagnostic performance and had higher specificity (88.2%) at equal sensitivities (90%) than the size-based logistic model (specificity, 52.9%).
 • The 2.5D DenseNet could be used to reduce potential overtreatment for the indolent subsolid nodules or to select candidates for sublobar resection instead of the standard lobectomy.

CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists

INTRODUCTION There is sparse evidence in the literature in relation to the chest computed tomography (CT) findings among adult Indigenous Australians with chronic respiratory conditions. METHODS In this retrospective study, patients who underwent chest CT between 2012 and 2020 among those referred to undergo lung function tests (spirometry) were assessed for the prevalence of abnormal chest CT radiological findings. RESULTS Of the 402 patients (59% female) included in this study, 331 (82%) had an abnormality identified on chest CT. Most abnormalities occurred alongside one (25%) or multiple (46%) other CT abnormalities. Airway disease ((AD) (including, emphysema, airway wall thickening and small airway disease) (35%), atelectasis: segmental or lobar collapse (27%), inflammatory opacities (24%) and bronchiectasis (23%) were the most common findings. AD and bronchiectasis were also the most common concurrent abnormalities in 40-50%. Other CT abnormalities noted in isolation or in combination with other CT findings were lung nodules (19%), lymph node enlargement (17%), consolidation or mass (17%), followed by lung cysts, ground-glass opacity, lung parenchymal architectural distortion, cavitating lung lesions and chronic pleural effusion were observed in ≤10%. Predictive models for odds of abnormality and outcomes showed age, smoking and underweight were associated with AD, and male sex and very remote residence were associated with bronchiectasis. CONCLUSION This study has illustrated that Indigenous Australian adults have a high prevalence of multiple chest CT abnormalities that may impose unprecedented diagnostic and therapeutic challenges in this population. Further studies are warranted to determine the long-term implications and prognostic significance of the CT findings as demonstrated in this study.

Chest computed tomography findings among adult Indigenous Australians in the Northern Territory of Australia.

Pure ground-glass nodules (pGGNs) with pleural contact (P-pGGNs) comprise not only invasive adenocarcinoma (IAC), but also minimally invasive adenocarcinoma (MIA). Radiomics recognizes complex patterns in imaging data by extracting high-throughput features of intra-tumor heterogeneity in a non-invasive manner. In this study, we sought to develop and validate a radiomics signature to identify IAC and MIA presented as P-pGGNs. In total, 100 patients with P-pGGNs (69 training samples and 31 testing samples) were retrospectively enrolled from December 2012 to May 2018. Imaging and clinical findings were also analyzed. In total, 106 radiomics features were extracted from the 3D region of interest (ROI) using computed tomography (CT) imaging. Univariate analyses were used to identify independent risk factors for IAC. The least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation was used to generate predictive features to build a radiomics signature. Receiver-operator characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy of the radiomics signature. Decision curve analyses (DCA) were also conducted to evaluate whether the radiomics signature was sufficiently robust for clinical practice. Univariate analysis showed significant differences between MIA (N = 47) and IAC (N = 53) groups in terms of patient age, lobulation signs, spiculate margins, tumor size, CT values and relative CT values (all P < 0.05). ROC curve analysis showed, when MIA was identified from IAC, that the critical value of tumor length diameter (TLD) was1.39 cm and the area under the ROC curve (AUC) was 0.724 (sensitivity = 0.792, specificity = 0.553). The critical CT value on the largest axial plane (CT-LAP) was − 597.45 HU, and the AUC was 0.666 (sensitivity = 0.698, specificity= 0.638). The radiomics signature consisted of seven features and exhibited a good discriminative performance between IAC and MIA, with an AUC of 0.892 (sensitivity = 0.811, specificity 0.719), and 0.862 (sensitivity = 0.625, specificity = 0.800) in training and testing samples, respectively. Our radiomics signature exhibited good discriminative performance in differentiating IAC from MIA in P-pGGNs, and may offer a crucial reference point for follow-up and selective surgical management.

/pdf/radiomic-signature-based-on-ct-imaging-to-distinguish-47q5wsb66m.pdf

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

OBJECTIVE. The purpose of this study is to comprehensively investigate the role of multiple features seen on thin-section CT (TSCT) in the differential diagnosis of ground-glass nodules (GGNs) measuring 5-10 mm in diameter as invasive adenocarcinoma (IAC). MATERIALS AND METHODS. The TSCT features of 313 surgically diagnosed GGNs from 288 patients were retrospectively reviewed. A logistic regression model was applied, and the AUC values for the model and the size and attenuation of the lesions were compared using ROC curve analysis. RESULTS. A total of 247 lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) (hereafter referred to as the AIS-MIA group) and 66 invasive adenocarcinomas (IACs) were identified. Compared with the AIS-MIA group, the IAC groups were significantly larger in size and had higher attenuation values, a higher frequency of mixed GGNs (all p < 0.001), bubblelike appearance, spiculation, pleural indentation, different locations, and a lower frequency of clear tumor-lung interface (all p < 0.05). The logistic model included size and attenuation (both p < 0.001; odds ratio [OR], 1.872 and 1.009, respectively) as well as tumor-lung interface (p = 0.001; OR, 0.242), bubblelike appearance (p < 0.05; OR, 2.205), and type of nodule. The AUC value for the logistic model was 0.847 (sensitivity, 80.3%; specificity, 81.0%) and was significantly higher than that for size or attenuation (both p < 0.01). CONCLUSION. Radiologic features could help in the differential diagnosis of a GGN that was 5-10 mm in diameter as IAC versus AIS or MIA. GGNs larger than 8.12 mm and with attenuation greater than -449.52 HU were more likely to be IAC.

https://www.ajronline.org/doi/pdf/10.2214/AJR.18.21008

Attenuation and Morphologic Characteristics Distinguishing a Ground-Glass Nodule Measuring 5-10 mm in Diameter as Invasive Lung Adenocarcinoma on Thin-Slice CT.

Aim To investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN). Materials and Methods A total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model. Results A total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99-1.00] and validation set (AUC, 0.99; 95% CI, 0.98-1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings. Conclusion The radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.

/pdf/clinical-and-ct-radiomics-nomogram-for-preoperative-33q8pxx6te.pdf

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule.

Purpose To develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter. Materials and methods This retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts. Results Five radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion This study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.

/pdf/development-and-validation-a-nomogram-incorporating-ct-1od9tg8z1z.pdf

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter.

Arginine-glycine-aspartic acid (RGD) peptide sequences exist in a variety of biological extracellular matrices and can specifically bind the cell-surface integrin αvβ3, which is overexpressed in cancer cells and plays important roles in tumor growth and invasion. Quantum dots (QDs) have been applied in the field of cell biology and can be physically conjugated to the surface of cancer cells for imaging. In this research, we developed QDs-RGD nanoparticles and investigated its application in pancreatic cancer cell imaging and its influence on the biological behavior of pancreatic cancer cells. The results of flow cytometric analysis showed that the αvβ3 receptor was markedly overexpressed on pancreatic cancer cells. In cellular uptake studies, the fluorescence signal of QDs-RGD nanoparticles in pancreatic cancer cells was higher than that of QDs without RGD conjugation, as determined by an inverted fluorescence microscope. Furthermore, the biological behavior of pancreatic cancer cells was affected by QDs-RGD nanoparticles, which inhibited proliferation, migration and invasion and induced G2-phase cell cycle arrest. With integrin αvβ3 as a target, QDs-RGD nanoparticles can generate high-quality images of pancreatic cancer cells and have immense potential for use in the targeted diagnosis and therapy of pancreatic cancer.

Yi Zhan

Papers

Attenuation and Morphologic Characteristics Distinguishing a Ground-Glass Nodule Measuring 5-10 mm in Diameter as Invasive Lung Adenocarcinoma on Thin-Slice CT.

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule.

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter.

Targeted Fluorescence Imaging and Biological Effects of Peptide Conjugated Quantum Dots on Pancreatic Cancer Cells.

Advances in differential diagnosis of pulmonary ground glass opacity on high resolution computed tomography and histopathology