Biomarkers for disease-specific survival (DSS) in earlystage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction.Experimental Design: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT). A receiver operating characteristic (ROC) curve was generated on the basis of vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS).Results: Area under the curve (AUC) in the YSM patients was 0.905 (P < 0.0001). AUC in the GHS patients was 0.880 (P < 0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (P < 0.0001).Conclusions: The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

BACKGROUND AND PURPOSE: Brain parenchymal hyperdensity on postthrombectomy CT in patients with acute stroke can be due to hemorrhage and/or contrast staining. We aimed to determine whether iodine concentration within contrast-stained parenchyma compared with an internal reference in the superior sagittal sinus on dual-energy CT could predict subsequent intracerebral hemorrhage.MATERIALS AND METHODS: Seventy-one patients with small infarct cores (ASPECTS $ 7) and good endovascular recanalization (modified TICI 2b or 3) for anterior circulation large-vessel occlusion were included. Brain parenchymal iodine concentration as per dual-energy CT and the percentage of contrast staining relative to the superior sagittal sinus were recorded and correlated with the development of intracerebral hemorrhage using Mann-Whitney U and Fisher exact tests.RESULTS: Forty-three of 71 patients had parenchymal hyperdensity on initial dual-energy CT. The median relative iodine concentration compared with the superior sagittal sinus was significantly higher in those with subsequent intracerebral hemorrhage (137.9% versus 109.2%, P 4 .007). By means of receiver operating characteristic analysis, a cutoff value of 100% (iodine concentration relative to the superior sagittal sinus) enabled identification of patients going on to develop intracerebral hemorrhage with 94.75% sensitivity, 43.4% specificity, and a likelihood ratio of 1.71.CONCLUSIONS: Within our cohort of patients, the relative percentage of iodine concentration at dual-energy CT compared with the superior sagittal sinus was a reliable predictor of intracerebral hemorrhage development and may be a useful imaging biomarker for risk stratification after endovascular treatment. ABBREVIATIONS: DECT 4 dual-energy CT; ICH 4 intracerebral hemorrhage; SSS 4 superior sagittal sinus

Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards. ADTA was applied to a training cohort (n = 80) and a cutoff value was defined based on a Receiver Operating Curve. ADTA was then applied to a validation cohort (n = 145) and the previously determined cutoff value was used to stratify high and low risk patients, as demonstrated by Kaplan–Meier analysis (p ≤ 0.001). Multivariable Cox proportional hazards analysis was performed using ADTA, depth, and ulceration as co-variables and showed that ADTA contributed to DSS prediction (HR: 4.18, CI 1.51–11.58, p = 0.006). ADTA provides an effective and attainable assessment of TILs and should be further evaluated in larger studies for inclusion in staging algorithms.