Cancer medicine · 2019
Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Kuiyuan, Xia Weixiong, Qiang Mengyun, Chen Xi, Liu Jia, Guo Xiang, Lv Xing
Аннотация
BACKGROUND: To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning.
METHODS: The pathological microscopic features were extracted using the software QuPath (version 0.1.3. Queen's University) in the training cohort (Guangzhou training cohort, n = 843). We used the neural network DeepSurv to analyze the pathological microscopic features (DSPMF) and then classified patients into high-risk and low-risk groups through the time-dependent receiver operating characteristic (ROC). The prognosis accuracy of the pathological feature was validated in a validation cohort (n = 212). The primary endpoint was progression-free survival (PFS).
RESULTS: We found 429 pathological microscopic features in the H&E image. Patients with high-risk scores in the training cohort had shorter 5-year PFS (HR 10.03, 6.06-16.61; P < .0001). The DSPMF (C-index: 0.723) had the higher C-index than the EBV DNA (C-index: 0.612) copies and the N stage (C-index: 0.593). Furthermore, induction chemotherapy (ICT) plus concomitant chemoradiotherapy (CCRT) had better 5-year PFS to those received CCRT (P < .0001) in the high-risk group.
CONCLUSION: The DSPMF is a reliable prognostic tool for survival risk in patients with NPC and might be able to guide the treatment decision.
METHODS: The pathological microscopic features were extracted using the software QuPath (version 0.1.3. Queen's University) in the training cohort (Guangzhou training cohort, n = 843). We used the neural network DeepSurv to analyze the pathological microscopic features (DSPMF) and then classified patients into high-risk and low-risk groups through the time-dependent receiver operating characteristic (ROC). The prognosis accuracy of the pathological feature was validated in a validation cohort (n = 212). The primary endpoint was progression-free survival (PFS).
RESULTS: We found 429 pathological microscopic features in the H&E image. Patients with high-risk scores in the training cohort had shorter 5-year PFS (HR 10.03, 6.06-16.61; P < .0001). The DSPMF (C-index: 0.723) had the higher C-index than the EBV DNA (C-index: 0.612) copies and the N stage (C-index: 0.593). Furthermore, induction chemotherapy (ICT) plus concomitant chemoradiotherapy (CCRT) had better 5-year PFS to those received CCRT (P < .0001) in the high-risk group.
CONCLUSION: The DSPMF is a reliable prognostic tool for survival risk in patients with NPC and might be able to guide the treatment decision.
Предметные рубрики MeSH
AdultAntineoplastic Combined Chemotherapy ProtocolsChemoradiotherapyChinaCisplatinDeep LearningEndemic DiseasesFemaleFluorouracilHumansImage Processing, Computer-AssistedInduction ChemotherapyKaplan-Meier EstimateMaleMiddle AgedNasopharyngeal NeoplasmsNasopharynxNeoplasm StagingPrognosisProgression-Free SurvivalRadiotherapy, Intensity-ModulatedRetrospective StudiesRisk Assessment