Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma

Abstract

Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear cell renal cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor intensive and time consuming. Here, we extracted quantitative features of hematoxylin‐eosin‐stained images using CellProfiler and performed machine learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC values of 97.0% and 81.4% in the Cancer Genome Atlas (TCGA) cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease‐free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P < .0001) and validation cohort (hazard ratio = 9.50, P = .0091). The integrated nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC.

Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost

“…Machine learning (ML), a branch of artificial intelligence and computer science, focusing on simulating the way the human brain learns using statistics and algorithms to improve accuracy, has had an outstanding performance in disease diagnosis, prognosis prediction, antitumor drug response, and treatment response assessment [20][21][22][23][24][25]. However, studies evaluating tumor response in LARC after NAT using ML algorithms are limited.…”

Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring

“…This issue becomes particularly important if non-pathologist personnel such as trainees and technicians are used as key operators. Of note, more sophisticated AI-algorithms are being developed that only count neoplastic cells 47 – 49 and become more operator-independent. One potential solution that also has been employed is to utilize double-stained slides (e.g., Ki-67 and synaptophysin) with deep learning algorithms to improve the accuracy of Ki-67 index quantification 50 – 53 .…”

Construction of an individualized clinical prognostic index based on ubiquitination-associated lncRNA in clear cell renal cell carcinoma patients

“…DEGs related to ubiquitination may provide a superior prediction value than those related to ARGs. Interestingly, Siteng Chen et al [ 33 ] developed a machine learning histopathological image signature to predict ccRCC diagnosis and survival. This image signature could accurately distinguish ccRCC from other cancer pathological types with an average AUC of approximately 90%.…”