Computational pathology: A survey review and the way forward

Hosseini, Mahdi S.; Bejnordi, Babak Ehteshami; Trinh, Vincent Quoc-Huy; Chan, Lyndon; Hasan, Danial; Li, Xingwen; Yang, Stephen; Kim, Taehyo; Zhang, Haochen; Wu, Theodore; Chinniah, Kajanan; Maghsoudlou, Sina; Zhang, Ryan; Zhu, Jiadai; Khaki, Samir; Buin, Andrei; Chaji, Fatemeh; Salehi, Ala; Nguyen, Bich Ngoc; Samaras, Dimitris; Plataniotis, Konstantinos N.

doi:10.1016/j.jpi.2023.100357

Cited by 20 publications

(1 citation statement)

References 585 publications

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“…Deep learning technology represents a milestone in this transformation, with numerous deep learning architectures applied to pathology-focused research. Various modeling objectives have been pursued, and recent studies demonstrate the application of deep learning in pathology aiming to predict conventional diagnostic features used in pathology practice (such as distinguishing between diseases and normal tissues, defining tumor grades, and differentiating cancer types), leading to new insights into diseases ( 25 , 26 ).…”

Section: Discussionmentioning

confidence: 99%

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Yuan,

Shen,

Shan

et al. 2024

Front. Oncol.

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BackgroundPathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain.MethodsThe annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.ResultsA total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms “deep learning”, “tumor microenvironment”, “biomarkers”, “image analysis”, “immunotherapy”, and “survival prediction”, etc. are hot keywords in this field.ConclusionIn future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.

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Section: Discussionmentioning

confidence: 99%

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Yuan,

Shen,

Shan

et al. 2024

Front. Oncol.

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Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning

Wei,

Li,

Feng

et al. 2024

The Journal of Pathology CR

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Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad‐CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor‐node‐metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

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Implementation of Vision Transformers on SPECT Heart Dataset: A Comparative Study

Verma,

Tripathi,

Pant

2024

Lecture Notes in Networks and Systems

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Computational pathology: A survey review and the way forward

Cited by 20 publications

References 585 publications

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning

Implementation of Vision Transformers on SPECT Heart Dataset: A Comparative Study

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