Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study

Abstract

Background The aim of this work is to combine radiological and pathological information of tumor to develop a signature for pretreatment prediction of discrepancies of pathological response at several centers and restage patients with locally advanced rectal cancer (LARC) for individualized treatment planning. Patients and Methods A total of 981 consecutive patients with evaluation of response according to tumor regression grade (TRG) who received nCRT were retrospectively recruited from four hospitals (primary cohort and external validation cohort 1–3); both pretreatment multiparametric MRI (mp-MRI) and whole slide image (WSI) of biopsy specimens were available for each patient. Quantitative image features were extracted from mp-MRI and WSI and used to construct a radiopathomics signature (RPS) powered by an artificial-intelligence model. Models based on mp-MRI or WSI alone were also constructed for comparison. Results The RPS showed overall accuracy of 79.66–87.66% in validation cohorts. The areas under the curve of RPS at specific response grades were 0.98 (TRG0), 0.93 (≤ TRG1), and 0.84 (≤ TRG2). RPS at each grade of pathological response revealed significant improvement compared with both signatures constructed without combining multiscale tumor information (P < 0.01). Moreover, RPS showed relevance to distinct probabilities of overall survival and disease-free survival in patients with LARC who underwent nCRT (P < 0.05). Conclusions The results of this study suggest that radiopathomics, combining both radiological information of the whole tumor and pathological information of local lesions from biopsy, could potentially predict discrepancies of pathological response prior to nCRT for better treatment planning.

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study

“…In a preliminary study, we confirmed that integration of radiomics MRI and biopsy slides enhanced the prediction of tumour regression grade to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. 14 However, these results…”

“…These findings are consistent with those of our previous study, in which we developed a radiopathomics model to predict tumour regression grade for locally advanced rectal cancer. 14 In the present study, RAPIDS was constructed using a modified modelling method with nine rMRI features, 12 pathomics nucleus features, and 18 pathomics microenvironment features. These selected features were not redundant but complementary, as shown in the heat maps analysis (appendix p 14).…”

Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer

“…At present, the basic function of radiomics is to quantitatively analyze tumor regions of interest through a large number of radiomics characteristics, which can provide valuable diagnostic, prognostic or predictive information. Its purpose is to explore and use these information resources to develop diagnostic, predictive, or prognostic imaging omics models to support personalized clinical decision-making and improve individualized treatment options [26] – [28] . Histopathology is the gold standard of clinical tumor diagnosis, directly related to the development of treatment and the evaluation of prognosis.…”

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

“…In recent years, leveraging AI and digital pathology has shown significant promise in cancer diagnosis ( 38 , 49 ). Remarkably, there have been attempts to integrate pathological section data and radiological data ( 96 ), which is called a new term as radopathomics. The fusion signature combining information from distinct dimensions could better predict discrepancies of treatment response.…”