HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening

Pataki, Bálint Ármin; Olar, Alex; Ribli, Dezső; Pesti, Adrián; Kontsek, Endre; Gyöngyösi, Benedek; Bilecz, Agnes; Kovacs, T; Kovács, Kristóf; Kramer, Zsófia; Kiss, András; Szócska, Miklós; Pollner, Péter; Csabai, István

doi:10.1038/s41597-022-01450-y

Cited by 14 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The obtained digital pathology images were in SDPC format and were converted to SVS format. Subsequently, the tumor regions in the pathological WSIs were annotated using QuPath software 24 . QuPath is an open‐source software specialized for digital pathology image analysis (version 0.4.3, https://qupath.github.io/), used to annotate tumor regions in pathological WSIs 25 .…”

Section: Methodsmentioning

confidence: 99%

“…QuPath is an open‐source software specialized for digital pathology image analysis (version 0.4.3, https://qupath.github.io/), used to annotate tumor regions in pathological WSIs 25 . These annotated WSIs were then divided into 512 × 512‐sized patches 24 . The same process was applied to both HE and Ki67 slides.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, the tumor regions in the pathological WSIs were annotated using QuPath software. 24 QuPath is an open-source software specialized for digital pathology image analysis (version 0.4.3, https:// qupath. github.…”

Section: Image Acquisition and Annotationmentioning

confidence: 99%

See 2 more Smart Citations

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

Tan,

Liu,

Xue

et al. 2024

Cancer Medicine

View full text Add to dashboard Cite

ObjectiveThis retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin–Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI‐assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments.MethodsThis study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA).ResultsA total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model.ConclusionThe combined model and nomogram, which integrate pathomics multi‐modal data and clinical‐pathological variables, demonstrated superior performance in distinguishing between Stage I–II and Stage III colorectal cancer. This provides valuable support for clinical decision‐making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

Tan,

Liu,

Xue

et al. 2024

Cancer Medicine

View full text Add to dashboard Cite

show abstract

“…Imaging biomarkers are based on the anatomical, histological, or radiographic characteristics of samples. Histology slides are a valuable source in clinical practice for screening cancer biomarkers such as angiogenesis, tumor growth, and metastasis [28] , [29] . Manual evaluation of histological slides ( Fig.…”

Section: Deep Learning-assisted Medical Imaging and Multi-omics Data ...mentioning

confidence: 99%

Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

Mathema

Sen

Lamichhane

et al. 2023

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

“…Histopathology analysis is the gold standard method for cancer diagnosis and prognosis. Experienced pathologists can provide accurate analysis of biopsy specimens based on whole slide image (WSI), i.e., the high-resolution digitalization of the entire histology slide (Khened et al 2021;Pataki et al 2022). However, analyzing the WSIs is timeconsuming and laborious due to the massive size of the WSIs and the complex colors and patterns of different tissue structures.…”

Section: Introductionmentioning

confidence: 99%

MulGT: Multi-Task Graph-Transformer with Task-Aware Knowledge Injection and Domain Knowledge-Driven Pooling for Whole Slide Image Analysis

Zhao

Wang

Yeung

et al. 2023

AAAI

View full text Add to dashboard Cite

Whole slide image (WSI) has been widely used to assist automated diagnosis under the deep learning fields. However, most previous works only discuss the SINGLE task setting which is not aligned with real clinical setting, where pathologists often conduct multiple diagnosis tasks simultaneously. Also, it is commonly recognized that the multi-task learning paradigm can improve learning efficiency by exploiting commonalities and differences across multiple tasks. To this end, we present a novel multi-task framework (i.e., MulGT) for WSI analysis by the specially designed Graph-Transformer equipped with Task-aware Knowledge Injection and Domain Knowledge-driven Graph Pooling modules. Basically, with the Graph Neural Network and Transformer as the building commons, our framework is able to learn task-agnostic low-level local information as well as task-specific high-level global representation. Considering that different tasks in WSI analysis depend on different features and properties, we also design a novel Task-aware Knowledge Injection module to transfer the task-shared graph embedding into task-specific feature spaces to learn more accurate representation for different tasks. Further, we elaborately design a novel Domain Knowledge-driven Graph Pooling module for each task to improve both the accuracy and robustness of different tasks by leveraging different diagnosis patterns of multiple tasks. We evaluated our method on two public WSI datasets from TCGA projects, i.e., esophageal carcinoma and kidney carcinoma. Experimental results show that our method outperforms single-task counterparts and the state-of-theart methods on both tumor typing and staging tasks.

show abstract

HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening

Cited by 14 publications

References 20 publications

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables

Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

MulGT: Multi-Task Graph-Transformer with Task-Aware Knowledge Injection and Domain Knowledge-Driven Pooling for Whole Slide Image Analysis

Contact Info

Product

Resources

About