Browse AI research papers authored by hadar hezi. Found 2 papers.

Treatment approaches for colorectal cancer (CRC) are highly dependent on the molecular subtype, as immunotherapy has shown efficacy in cases with microsatellite instability (MSI) but is ineffective for the microsatellite stable (MSS) subtype. There is promising potential in utilizing deep neural networks (DNNs) to automate the differentiation of CRC subtypes by analyzing Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). Due to the extensive size of WSIs, Multiple Instance Learning (MIL) techniques are typically explored. However, existing MIL methods focus on identifying the most representative image patches for classification, which may result in the loss of critical information. Additionally, these methods often overlook clinically relevant information, like the tendency for MSI class tumors to predominantly occur on the proximal (right side) colon. We introduce `CIMIL-CRC', a DNN framework that: 1) solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches, and 2) integrates clinical priors, particularly the tumor location within the colon, into the model to enhance patient-level classification accuracy. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort, contrasting it with a baseline patch-level classification, MIL-only approach, and Clinically-informed patch-level classification approach. Our CIMIL-CRC outperformed all methods (AUROC: $0.92pm0.002$ (95% CI 0.91-0.92), vs. $0.79pm0.02$ (95% CI 0.76-0.82), $0.86pm0.01$ (95% CI 0.85-0.88), and $0.87pm0.01$ (95% CI 0.86-0.88), respectively). The improvement was statistically significant.

## Overview

- Colorectal cancer (CRC) can be categorized into different molecular subtypes, which significantly impacts treatment approaches.
- Immunotherapy is effective for the microsatellite instability (MSI) subtype but not the microsatellite stable (MSS) subtype.
- Deep neural networks (DNNs) have the potential to automate the differentiation of CRC subtypes by analyzing whole-slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tissue.
- Multiple Instance Learning (MIL) techniques are often used to handle the large size of WSIs, but existing methods may result in the loss of critical information.
- Clinically relevant information, such as the location of the tumor within the colon, is often overlooked in these methods.

## Plain English Explanation

Colorectal cancer (CRC) can be divided into different [types](https://aimodels.fyi/papers/arxiv/exploring-interplay-between-colorectal-cancer-subtypes-genomic) based on molecular changes in the tumor cells. This is important because the type of CRC determines the best course of [treatment](https://aimodels.fyi/papers/arxiv/deep-learning-based-classification-breast-cancer-molecular). For example, a type called microsatellite instability (MSI) responds well to a type of therapy called immunotherapy, but another type called microsatellite stable (MSS) does not.

Researchers are exploring the use of [deep learning](https://aimodels.fyi/papers/arxiv/camil-context-aware-multiple-instance-learning-cancer) techniques, specifically deep neural networks (DNNs), to automatically identify the CRC subtype by analyzing images of the tumor tissue. These images, called whole-slide images (WSIs), are very large and complex, so researchers often use a technique called Multiple Instance Learning (MIL) to handle them.

However, existing MIL methods focus on finding the most representative parts of the image for classification, which can lead to important information being lost. Additionally, these methods often do not consider clinically relevant information, such as the location of the tumor within the colon, which could be helpful in accurately identifying the CRC subtype.

## Key Findings

- The researchers introduced a new DNN framework called "CIMIL-CRC" that:
  1. Efficiently combines a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all image patches, rather than just the most representative ones.
  2. Integrates clinical information about the tumor location within the colon to enhance the accuracy of patient-level CRC subtype classification.
- CIMIL-CRC outperformed other methods, including a baseline patch-level classification, a MIL-only approach, and a clinically-informed patch-level classification approach, in a 5-fold cross-validation experiment using the TCGA-CRC-DX dataset.
- The improvement in performance was statistically significant, with CIMIL-CRC achieving an average area under the curve (AUC) of 0.92 ± 0.002 (95% CI 0.91-0.92), compared to 0.79 ± 0.02 (95% CI 0.76-0.82), 0.86 ± 0.01 (95% CI 0.85-0.88), and 0.87 ± 0.01 (95% CI 0.86-0.88) for the other approaches, respectively.

## Technical Explanation

The researchers developed a [deep learning](https://aimodels.fyi/papers/arxiv/micromil-graph-based-contextual-multiple-instance-learning) framework called "CIMIL-CRC" to automate the differentiation of CRC subtypes (MSI vs. MSS) using Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs).

To handle the large size of the WSIs, the researchers used a Multiple Instance Learning (MIL) approach. However, instead of focusing only on the most representative image patches for classification, as is common in existing MIL methods, CIMIL-CRC efficiently aggregates information from all patches using a pre-trained feature extraction model and principal component analysis (PCA).

Additionally, CIMIL-CRC integrates clinically relevant information, specifically the location of the tumor within the colon, to enhance the patient-level classification accuracy. This is important because MSI class tumors tend to predominantly occur on the proximal (right side) colon.

The researchers evaluated their CIMIL-CRC method using a 5-fold cross-validation experimental setup on the TCGA-CRC-DX dataset. They compared CIMIL-CRC's performance to a baseline patch-level classification approach, a MIL-only approach, and a clinically-informed patch-level classification approach.

CIMIL-CRC outperformed all the other methods, achieving an average area under the curve (AUC) of 0.92 ± 0.002 (95% CI 0.91-0.92), compared to 0.79 ± 0.02 (95% CI 0.76-0.82), 0.86 ± 0.01 (95% CI 0.85-0.88), and 0.87 ± 0.01 (95% CI 0.86-0.88), respectively. The improvement in performance was statistically significant.

## Implications for the Field

The findings of this study demonstrate the potential of utilizing deep learning techniques, particularly the CIMIL-CRC framework, to automate the differentiation of CRC subtypes based on H&E-stained WSIs. This is significant because the CRC subtype is a crucial determinant of the most effective treatment approach, and the ability to reliably and efficiently identify the subtype can greatly improve patient outcomes.

By incorporating clinically relevant information, such as tumor location, in addition to the visual features extracted from the WSIs, CIMIL-CRC shows how integrating multi-modal data can enhance the accuracy of CRC subtype classification. This approach could be further extended to incorporate other relevant clinical, genomic, or molecular data to improve the overall diagnostic and prognostic capabilities for CRC management.

## Critical Analysis

The researchers have provided a robust and well-designed study, with a clear focus on addressing the limitations of existing MIL methods for CRC subtype classification. The use of a large, well-characterized dataset (TCGA-CRC-DX) and a rigorous 5-fold cross-validation experimental setup lend credibility to the reported results.

However, the study does not explicitly discuss potential limitations or caveats. For example, it would be valuable to know how the performance of CIMIL-CRC compares to other state-of-the-art methods beyond the specific approaches used in this paper, as well as the potential impact of factors such as tissue quality, staining variability, or image resolution on the model's performance.

Additionally, the researchers could have explored the interpretability of the CIMIL-CRC model, as understanding the specific features and clinical factors that drive the classification decisions could provide valuable insights for clinicians and researchers.

## Conclusion

This study introduces the CIMIL-CRC framework, a deep learning-based approach that efficiently aggregates information from H&E-stained whole-slide images and integrates clinically relevant tumor location data to accurately differentiate between the microsatellite instability (MSI) and microsatellite stable (MSS) subtypes of colorectal cancer.

The superior performance of CIMIL-CRC, demonstrated through rigorous experimentation, highlights the potential of this approach to streamline and enhance the diagnostic capabilities for CRC management. By automating the identification of CRC subtypes, CIMIL-CRC could help guide the selection of the most appropriate treatment strategies, ultimately improving patient outcomes.

The integration of multi-modal data, combining visual features from histopathological images and clinical information, showcases the value of a holistic approach to disease classification. Further research could explore the incorporation of additional relevant data sources, such as genomic or molecular profiles, to refine the diagnostic and prognostic capabilities of the CIMIL-CRC framework.

CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847$pm$0.01 vs. 0.787$pm$0.03, p$=$0.01, AP: 0.68$pm$0.02 vs. 0.64$pm$0.05).

## Overview

- Colorectal cancer (CRC) has different molecular subtypes, which significantly impact treatment decisions.
- Convolutional neural networks (CNNs) have been used to automate CRC subtype identification from histopathological images.
- The relationship between CRC subtype genomic variants and their corresponding cellular morphology expressed in imaging phenotypes is not fully explored.

## Plain English Explanation

Colorectal cancer (CRC) is a type of cancer that starts in the colon or rectum. Researchers have found that CRC can be divided into different [molecular subtypes](https://aimodels.fyi/papers/arxiv/exploring-interplay-between-colorectal-cancer-subtypes-genomic), and the specific subtype can greatly influence the best treatment approach.

In recent years, scientists have started using a type of artificial intelligence called [convolutional neural networks (CNNs)](https://aimodels.fyi/papers/arxiv/histopathological-image-classification-cell-morphology-aware-deep) to automatically identify the CRC subtype from microscopic images of tumor tissue samples. However, the exact relationship between the genetic changes (genomic variants) underlying each CRC subtype and the corresponding patterns in the cellular structure (morphology) seen in the images has not been fully understood.

The goal of this study was to explore this connection between the genomic and imaging characteristics of CRC subtypes, and use that information to improve the accuracy of CRC subtype classification from microscopic images.

## Technical Explanation

The researchers used the publicly available [TCGA-CRC-DX dataset](https://aimodels.fyi/papers/arxiv/data-driven-nucleus-subclassification-colon-hande-using), which contains whole slide images from 360 CRC patients, along with information about their CRC subtype classification and genomic variations.

They trained [CNN models](https://aimodels.fyi/papers/arxiv/deep-learning-based-prediction-breast-cancer-tumor) that incorporated the potential correlation between the genomic variations within CRC subtypes and their corresponding cellular morphology patterns. The researchers assessed the interplay between CRC subtypes' genomic variations and cellular morphology by evaluating the CRC subtype classification accuracy of different models using a 5-fold cross-validation approach.

The results showed that combining the CNN models to account for variations in cancer-associated gene methylation (CIMP) and single nucleotide polymorphisms (SNPs) further improved the classification accuracy compared to models that did not consider this genomic information (AUROC: 0.847 ± 0.01 vs. 0.787 ± 0.03, p=0.01, AP: 0.68 ± 0.02 vs. 0.64 ± 0.05).

## Critical Analysis

The study provides valuable insights into the relationship between the genomic and imaging characteristics of CRC subtypes. By incorporating genomic data into the CNN models, the researchers were able to improve the accuracy of CRC subtype classification from histopathological images.

However, the study is limited to a specific dataset and may not be generalizable to all CRC cases. Additionally, the researchers only examined two types of genomic variations (CIMP and SNPs) and there may be other genetic factors that influence the cellular morphology patterns.

Further research is needed to [explore more explainable AI techniques](https://aimodels.fyi/papers/arxiv/exploring-explainable-ai-techniques-improved-interpretability-lung) that can provide a deeper understanding of the underlying mechanisms linking genomic variations to imaging phenotypes in CRC. This could lead to more accurate and clinically relevant subtype classification, ultimately guiding personalized treatment decisions for CRC patients.

## Conclusion

This study demonstrates the potential of incorporating genomic data into CNN models for improved [CRC subtype classification from histopathological images](https://aimodels.fyi/papers/arxiv/histopathological-image-classification-cell-morphology-aware-deep). By exploring the interplay between genomic variants and cellular morphology patterns, the researchers were able to enhance the accuracy of CRC subtype identification, which is crucial for guiding personalized treatment decisions. Further research in this direction could lead to more robust and interpretable AI-powered tools for cancer diagnosis and management.

hadar hezi

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