From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology

Jyoti Verma; Archana Sandhu; Renu Popli; Rajeev Kumar; Vikas Khullar; Isha Kansal; Ashutosh Sharma; Kanwal Garg; Neeru Kashyap; Khursheed Aurangzeb

doi:10.1515/biol-2022-0777

From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology

Open Life Sci. 2023 Dec 13;18(1):20220777. doi: 10.1515/biol-2022-0777. eCollection 2023.

Authors

Affiliations

¹ Department of Computer Science and Engineering, Punjabi University, Patiala, India.
² MM Institute of Computer Technology and Business Management Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala, Haryana, 134007, India.
³ Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
⁴ Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India.
⁵ Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, 136119, Haryana, India.
⁶ Department of ECE, M.M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Ambala, Haryana 134007, India.
⁷ Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.

Abstract

Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on histopathological images of human colorectal cancer. We present a retrospective multicenter study utilizing a dataset of 100,000 nonoverlapping image patches from hematoxylin & eosin-stained histological images of CRC and normal tissue. The dataset includes diverse tissue classes such as adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, and colorectal adenocarcinoma epithelium. To perform survival prediction, we employ various deep learning architectures, including convolutional neural network, DenseNet201, InceptionResNetV2, VGG16, VGG19, and Xception. These architectures are trained on the dataset using a multicenter retrospective analysis approach. Extensive preprocessing steps are undertaken, including image normalization using Macenko's method and data augmentation techniques, to optimize model performance. The experimental findings reveal promising results, demonstrating the effectiveness of deep learning models in prognostic survival prediction. Our models achieve high accuracy, precision, recall, and validation metrics, showcasing their ability to capture relevant histological patterns associated with prognosis. Visualization techniques are employed to interpret the models' decision-making process, highlighting important features and regions contributing to survival predictions. The implications of this research are manifold. The accurate prediction of survival outcomes in CRC can aid in personalized medicine and clinical decision-making, facilitating tailored treatment plans for individual patients. The identification of important histological features and biomarkers provides valuable insights into disease mechanisms and may lead to the discovery of novel prognostic indicators. The transparency and explainability of the models enhance trust and acceptance, fostering their integration into clinical practice. Research demonstrates the potential of deep learning models for prognostic survival prediction in human colorectal cancer histology. The findings contribute to the understanding of disease progression and offer practical applications in personalized medicine. By harnessing the power of deep learning and histopathological analysis, we pave the way for improved patient care, clinical decision support, and advancements in prognostic prediction in CRC.

Keywords: colorectal cancer; deep learning; hematoxylin & eosin staining; histopathological analysis; image patches; prognostic survival prediction; retrospective multicenter study.