Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone

Abstract

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n = 30) with similar presentations who experienced very different treatment responses (platinum free intervals of either ≤ 6 months or ≥ 18 months), we used whole slide images (WSI, n = 205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology.

Application of Deep Learning on the Prognosis of Cutaneous Melanoma Based on Full Scan Pathology Images

“…At present, CNN-related research is mainly based on learning and predicting the skin surface of CM patients, such as the research of Yang et al and Haenssle et al [ 27 , 28 ]. There are only a few studies and reviews on pathological pictures, such as Wang et al's article and Laury et al's article [ 13 , 29 ]. There are very few studies based on CNN to predict the prognosis of CM patients by learning from pathological scans.…”

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

“…The median age was 63 years (min: 40, max: 82) at diagnosis. Surgery outcome was R0 (no residual tumor) for 21 patients and R > 0 (residual tumor after surgery) for 74 patients 27 . The patients’ median progression free interval (PFI) from last treatment administration until disease progression or end of follow up was 10 months (min: 0, max: 165) and the median overall survival time from diagnosis until last follow up or death was 54 months (min: 10, max: 170).…”

Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis

“…Histopathology through laparoscopic or laparotomy biopsy and cytopathology of malignant ascites remains the gold standard for the diagnosis of PC [ 46 , 47 ]. Recently, DL image analysis has used to identify morphology-based representations caused by molecular alterations in some tumors, including colorectal cancer, melanoma, breast cancer, prostate Cancer, and lung cancer [ [48] , [49] , [50] ]. Digital pathology has made significant progress due to the following advantages, including immediate availability of cases, remote diagnosis, and more convenient consultation with pathologists [ 51 ].…”

“…Digital pathology has made significant progress due to the following advantages, including immediate availability of cases, remote diagnosis, and more convenient consultation with pathologists [ 51 ]. Faster slide scanning, cheaper data storage, and increased computing power have enabled the development of large-scale slide scanning as well as image analysis, and promoted the application of ML to address biological issues through combining tumor morphology with histology [ 48 , 52 ]. Machine learning is also associated with higher accuracy in error-prone tasks such as metastasis identification [ 51 ].…”