Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis.

Rationale: The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis and may obviate the need for invasive surgical biopsy. Few machine-learning studies have investigated the classification of interstitial lung disease (ILD) on CT imaging, but none have used histopathology as a reference standard.Objectives: To predict histopathologic UIP using deep learning of high-resolution computed tomography (HRCT).Methods: Institutional databases were retrospectively searched for consecutive patients with ILD, HRCT, and diagnostic histopathology from 2011 to 2014 (training cohort) and from 2016 to 2017 (testing cohort). A blinded expert radiologist and pulmonologist reviewed all training HRCT scans in consensus and classified HRCT scans based on the 2018 American Thoracic Society/European Respriatory Society/Japanese Respiratory Society/Latin American Thoracic Association diagnostic criteria for idiopathic pulmonary fibrosis. A convolutional neural network (CNN) was built accepting 4 × 4 × 2 cm virtual wedges of peripheral lung on HRCT as input and outputting the UIP histopathologic pattern. The CNN was trained and evaluated on the training cohort using fivefold cross validation and was then tested on the hold-out testing cohort. CNN and human performance were compared in the training cohort. Logistic regression and survival analyses were performed.Results: The CNN was trained on 221 patients (median age 60 yr; interquartile range [IQR], 53-66), including 71 patients (32%) with UIP or probable UIP histopathologic patterns. The CNN was tested on a separate hold-out cohort of 80 patients (median age 66 yr; IQR, 58-69), including 22 patients (27%) with UIP or probable UIP histopathologic patterns. An average of 516 wedges were generated per patient. The percentage of wedges with CNN-predicted UIP yielded a cross validation area under the curve of 74% for histopathological UIP pattern per patient. The optimal cutoff point for classifying patients on the training cohort was 16.5% of virtual lung wedges with CNN-predicted UIP and resulted in sensitivity and specificity of 74% and 58%, respectively, in the testing cohort. CNN-predicted UIP was associated with an increased risk of death or lung transplantation during cross validation (hazard ratio, 1.5; 95% confidence interval, 1.1-2.2; P = 0.03).Conclusions: Virtual lung wedge resection in patients with ILD can be used as an input to a CNN for predicting the histopathologic UIP pattern and transplant-free survival.

Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.

OBJECTIVE: To investigate whether pretreatment MRI-based radiomics of locally advanced rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and tumor regression grade (TRG). METHODS: One hundred thirty-two consecutive patients with LARC who underwent neoadjuvant chemoradiation and total mesorectal excision (TME) were retrospectively collected from 2 centers in the USA and Italy. The primary tumor and surrounding MC were segmented on the best available T2-weighted sequence (axial, coronal, or sagittal). Three thousand one hundred ninety radiomic features were extracted using a python package. The most salient radiomic features as well as MRI parameter and clinical-based features were selected using recursive feature elimination. A logistic regression classifier was built to distinguish between any 2 binned categories in the considered endpoints: pCR, NAR, and TRG. Repeated k-fold validation was performed and AUCs calculated. RESULTS: There were 24, 87, and 21 T4, T3, and T2 LARCs, respectively (median age 63 years, 32 to 86). For NAR and TRG, the best classification performance was obtained using both the tumor and MC segmentations. The AUCs for classifying NAR 0 versus 2, pCR, and TRG 0/1 versus 2/3 were 0.66 (95% CI, 0.60-0.71), 0.80 (95% CI, 0.74-0.85), and 0.80 (95% CI, 0.77-0.82), respectively. CONCLUSION: Radiomics of pretreatment MRIs can predict pCR, TRG, and NAR score in patients with LARC undergoing neoadjuvant treatment and TME with moderate accuracy despite extremely heterogenous image data. Both the tumor and MC contain important prognostic information. KEY POINTS: • Machine learning of rectal cancer on images from the pretreatment MRI can predict important patient outcomes with moderate accuracy. • The tumor and the tissue around it both contain important prognostic information.

Quantitative Analysis of Immune Infiltrates in Primary Melanoma.

Novel methods to analyze the tumor microenvironment (TME) are urgently needed to stratify melanoma patients for adjuvant immunotherapy. Tumor-infiltrating lymphocyte (TIL) analysis, by conventional pathologic methods, is predictive but is insufficiently precise for clinical application. Quantitative multiplex immunofluorescence (qmIF) allows for evaluation of the TME using multiparameter phenotyping, tissue segmentation, and quantitative spatial analysis (qSA). Given that CD3+CD8+ cytotoxic lymphocytes (CTLs) promote antitumor immunity, whereas CD68+ macrophages impair immunity, we hypothesized that quantification and spatial analysis of macrophages and CTLs would correlate with clinical outcome. We applied qmIF to 104 primary stage II to III melanoma tumors and found that CTLs were closer in proximity to activated (CD68+HLA-DR+) macrophages than nonactivated (CD68+HLA-DR-) macrophages (P < 0.0001). CTLs were further in proximity from proliferating SOX10+ melanoma cells than nonproliferating ones (P < 0.0001). In 64 patients with known cause of death, we found that high CTL and low macrophage density in the stroma (P = 0.0038 and P = 0.0006, respectively) correlated with disease-specific survival (DSS), but the correlation was less significant for CTL and macrophage density in the tumor (P = 0.0147 and P = 0.0426, respectively). DSS correlation was strongest for stromal HLA-DR+ CTLs (P = 0.0005). CTL distance to HLA-DR- macrophages associated with poor DSS (P = 0.0016), whereas distance to Ki67- tumor cells associated inversely with DSS (P = 0.0006). A low CTL/macrophage ratio in the stroma conferred a hazard ratio (HR) of 3.719 for death from melanoma and correlated with shortened overall survival (OS) in the complete 104 patient cohort by Cox analysis (P = 0.009) and merits further development as a biomarker for clinical application. Cancer Immunol Res; 6(4); 481-93. ©2018 AACR.