Background Endometrial cancer can be molecularly classified into POLEmut , mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to... Show moreBackground Endometrial cancer can be molecularly classified into POLEmut , mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication. Methods This combined analysis included diagnostic haematoxylin and eosin-stained slides and molecular and clinicopathological data from 2028 patients with intermediate-to-high-risk endometrial cancer from the PORTEC-1 (n=466), PORTEC-2 (n=375), and PORTEC-3 (n=393) randomised trials and the TransPORTEC pilot study (n=110), the Medisch Spectrum Twente cohort (n=242), a case series of patients with POLEmut endometrial cancer in the Leiden Endometrial Cancer Repository (n=47), and The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma cohort (n=395). PORTEC-3 was held out as an independent test set and a four-fold cross validation was performed. Performance was measured with the macro and class-wise area under the receiver operating characteristic curve (AUROC). Whole-slide images were segmented into tiles of 360 & mu;m resized to 224 x 224 pixels. im4MEC was trained to learn tile-level morphological features with self-supervised learning and to molecularly classify whole-slide images with an attention mechanism. The top 20 tiles with the highest attention scores were reviewed to identify morpho-molecular correlates. Predictions of a nuclear classification deep learning model serve to derive interpretable morphological features. We analysed 5-year recurrence-free survival and explored prognostic refinement by molecular class using the Kaplan-Meier method. Findings im4MEC attained macro-average AUROCs of 0 & BULL;874 (95% CI 0 & BULL;856-0 & BULL;893) on four-fold cross-validation and 0 & BULL;876 on the independent test set. The class-wise AUROCs were 0 & BULL;849 for POLEmut (n=51), 0 & BULL;844 for MMRd (n=134), 0 & BULL;883 for NSMP (n=120), and 0 & BULL;928 for p53abn (n=88). POLEmut and MMRd tiles had a high density of lymphocytes, p53abn tiles had strong nuclear atypia, and the morphology of POLEmut and MMRd endometrial cancer overlapped. im4MEC highlighted a low tumour-to-stroma ratio as a potentially novel characteristic feature of the NSMP class. 5-year recurrence-free survival was significantly different between im4MEC predicted molecular classes in PORTEC-3 (log-rank p<0 & BULL;0001). The ten patients with aggressive p53abn endometrial cancer that was predicted as MMRd showed inflammatory morphology and appeared to have a better prognosis than patients with correctly predicted p53abn endometrial cancer (p=0 & BULL;30). The four patients with NSMP endometrial cancer that was predicted as p53abn showed higher nuclear atypia and appeared to have a worse prognosis than patients with correctly predicted NSMP (p=0 & BULL;13). Patients with MMRd endometrial cancer predicted as POLEmut had an excellent prognosis, as do those with true POLEmut endometrial cancer. Interpretation We present the first interpretable deep learning model, im4MEC, for haematoxylin and eosin-based prediction of molecular endometrial cancer classification. im4MEC robustly identified morpho-molecular correlates and could enable further prognostic refinement of patients with endometrial cancer. Copyright & COPY; 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Show less
Triest, B. van; Rasing, M.; Velden, J. van der; Hullu, J. de; Witteveen, P.O.; Beukema, J.C.; ... ; Jurgenliemk-Schulz, I. 2021
Objective. To evaluate feasibility of chemoradiation as alternative for extensive surgery in patients with locally advanced vulvar cancer and to report on locoregional control, toxicity and... Show moreObjective. To evaluate feasibility of chemoradiation as alternative for extensive surgery in patients with locally advanced vulvar cancer and to report on locoregional control, toxicity and survival. Methods. In a multicenter, prospective phase II trial patients with locally advanced vulvar cancer were treated with locoregional radiotherapy combined with sensitizing chemotherapy (capecitabine). Treatment feasibility, percentage locoregional control, survival and toxicity were evaluated. Results. 52 patients with mainly T2/T3 disease were treated according to the study protocol in 10 centers in the Netherlands from 2007 to 2019. Full dose radiotherapy (tumor dose of 64.8Gy) was delivered in 92% and full dose capecitabine in 69% of patients. Most prevalent acute >_ grade 3 toxicities were regarding skin/ mucosa and pain (54% and 37%). Late >_grade 3 toxicity was reported for skin/mucosa (10%), fibrosis (4%), GI incontinence (4%) and stress fracture or osteoradionecrosis (4%). Twelve weeks after treatment, local clinical complete response (cCR) and regional control (RC) rates were 62% and 75%, respectively. After 2 years, local cCR persisted in 22 patients (42%) and RC was 58%. Thirty patients (58%) had no evidence of disease at end of follow-up (median 35 months). In 9 patients (17%) extensive surgery with stoma formation was needed. Progression free survival was 58%, 51% and 45% and overall survival was 76%, 66%, 52% at 1,2, and 5 years. Conclusions. Definitive capecitabine-based chemoradiation as alternative for extensive surgery is feasible in locally advanced vulvar cancer and results in considerable locoregional control with acceptable survival rates with manageable acute and late toxicity. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). Show less
Tan, L.T.; Tanderup, K.; Kirisits, C.; Leeuw, A. de; Nout, R.; Duke, S.; ... ; Potter, R. 2019
