Development of multiple <scp>AI</scp> pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&amp;E‐stained tissues

Shen, Bin; Saito, Akira; Ueda, Atsushi; Fujita, Koji; Nagamatsu, Yui; Hashimoto, M.; Kobayashi, Masaharu; Mirza, Aashiq H.; Graf, Hans Peter; Cosatto, Eric; Hazama, Shoichi; Nagano, Hiroaki; Satô, Eiichi; Matsubayashi, Jun; Nagao, Toshitaka; Cheng, Esther; Hoda, Syed Af; Ishikawa, Takashi; Kuroda, Masahiko

doi:10.1002/cjp2.314

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…Response Prediction: A study by Shen et al [12] developed multiple AI pipelines to predict the response of breast cancer to neoadjuvant chemotherapy using H&E-stained tissues. This approach used a combination of CNN, SVM, and random forest models.…”

Section: Literature Review Multiple Ai Pipelines For Neoadjuvant Chem...mentioning

confidence: 99%

Advancing Breast Cancer Subtype Prediction and Mutation Analysis: Integrating Deep Learning and Machine Learning Techniques in Genomic Research

Gadamsetty,

Angayarkanni

2024

icisna

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Breast cancer, a heterogeneous disease, can be classified into several subtypes, each associated with distinct genetic mutations and clinical outcomes. As per the research article by National Institutes of Health it was stated that 25% of hereditary cases are due to the mutation of highly penetrant genes which leads to 80% lifetime risk of breast cancer [15]. This study aims to apply advanced deep learning and machine learning algorithms to predict breast cancer subtypes and to identify key genetic mutations contributing to the disease using a comprehensive gene expression dataset. We analyzed a dataset comprising 1904 samples, encompassing 331 genes and 175 gene mutations, sourced from a public platform and including PAM50 and Claudin low categorizations. Due to limited observations, SMOTE was employed for data augmentation, and Principal Component Analysis (PCA) was used to assess data variance. Several machine learning models, including Random Forest Classifier, Support Vector Machine, K-Nearest Neighbor, XGBoost, and Stacked models, were applied alongside deep learning techniques like Convolutional Neural Network and Multi-Layer Perceptron. The Stacked model demonstrated superior performance with an accuracy of 0.955, outperforming other models. The deep learning models achieved accuracies of 0.911 (CNN) and 0.936 (MLP). KNN analysis revealed potential clusters based on gene and mutation data, with the silhouette metric identifying "siah1_mut," "nras_mut," and "hras_mut" as significant mutations. The optimal clustering achieved a silhouette score of 0.997 for two clusters. These mutations may play pivotal roles in breast cancer pathogenesis and could serve as targets for therapeutic interventions. Our findings demonstrate the effectiveness of integrating stacked algorithms and deep learning models in predicting breast cancer subtypes. The identification of key mutations through clustering techniques provides valuable insights into the genetic underpinnings of breast cancer, which could guide future research and the development of targeted therapies. This study highlights the potential of advanced computational approaches in elucidating the complex landscape of breast cancer genomics and paves the way for personalized medicine in oncology.

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Section: Literature Review Multiple Ai Pipelines For Neoadjuvant Chem...mentioning

confidence: 99%

Advancing Breast Cancer Subtype Prediction and Mutation Analysis: Integrating Deep Learning and Machine Learning Techniques in Genomic Research

Gadamsetty,

Angayarkanni

2024

icisna

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“…Breast cancer treatment has evolved, with neoadjuvant chemotherapy (NAC) now commonly used, especially for locally advanced cases. With the help of AI, it has become possible to predict the effect of neoadjuvant chemotherapy [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Predicting the effectiveness of NAC prior to administration is crucial to avoid unnecessary treatments. A promising approach was conducted by Shen et al [ 9 ] This study aims to predict the effectiveness of NAC in BC patients using AI analysis of H&E images of prechemotherapy needle biopsies. A novel pipeline system has been developed, consisting of three independent models focusing on different cancer atypia characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…This process is labor-intensive and time-consuming [ 4 ]. In addition, the multiple-pipeline AI system to predict the efficacy of NAC relies partially on manual sampling of ROIs from annotated cancer regions [ 9 ]. Additionally, automatic annotations for ductal carcinoma (DCIS) in situ were not available through using Deep Grade method to categorize the breast NHG2 tumors and method of HASHI to detect the invasive part.…”

Section: Introductionmentioning

confidence: 99%

“…This requires implementing advanced automation techniques within the AI system, enabling it to analyze and interpret complex nuclear features from histopathological images or other relevant data sources. By achieving full automation, the AI system would streamline the process of identifying distinct nuclear phenotypes associated with various cancer subtypes, potentially leading to more accurate diagnoses and personalized treatment strategies [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence’s impact on breast cancer pathology: a literature review

Soliman,

Li,

Parwani

2024

Diagn Pathol

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This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI’s full potential in BC pathology. Despite the existing hurdles, AI’s multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI’s transformative capabilities in breast cancer diagnosis and assessment. Graphical Abstract

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Application of quantitative histomorphometric features in computational pathology

Shi,

Hu,

et al. 2024

Interdisciplinary Medicine

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Computer vision has facilitated the execution of various computer‐aided diagnostic tasks. From a methodological perspective, these tasks are primarily implemented using two dominant strategies: end‐to‐end Deep learning (DL)‐based methods and traditional feature engineering‐based methods. DL methods are capable of automatically extracting, analyzing, and filtering features, leading to final decision‐making from whole slide images. However, these methods are often criticized for the “black box” issue, a significant limitation of DL. In contrast, traditional feature engineering‐based methods involve well‐defined quantitative input features. But it was considered as less potent than DL methods. Advances in segmentation technology and the development of quantitative histomorphometric (QH) feature representation have propelled the evolution of feature engineering‐based methods. This review contrasts the performance differences between the two methods and traces the development of QH feature representation. The conclusion is that, with the ongoing progress in QH feature representation and segmentation technology, methods based on QH features will leverage their advantages—such as explainability, reduced reliance on large training datasets, and lower computational resource requirements—to play a more significant role in some clinical tasks. They may even replace DL methods somewhat or be used in conjunction with them to achieve accurate and understandable results.

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Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E‐stained tissues

Cited by 6 publications

References 31 publications

Advancing Breast Cancer Subtype Prediction and Mutation Analysis: Integrating Deep Learning and Machine Learning Techniques in Genomic Research

Advancing Breast Cancer Subtype Prediction and Mutation Analysis: Integrating Deep Learning and Machine Learning Techniques in Genomic Research

Artificial intelligence’s impact on breast cancer pathology: a literature review

Application of quantitative histomorphometric features in computational pathology

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