Deep Learning Generates Custom-made Logistic Regression Models for Explaining how Breast Cancer Subtypes are Classified

Abstract Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis but does not explain each subtype’s mechanism. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. To reveal the mechanisms embedded in the PAM50 subtypes, we developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. We developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. Logistic regression is familiar to physicians, and we can use it to analyze which genes are important for prediction. The custom-made logistic regression models generated by the point-wise linear model used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. Analyzing the point-wise linear model’s inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways.

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Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified

10.1101/2021.05.10.443518 ◽

2021 ◽

Author(s):

Takuma Shibahara ◽

Chisa Wada ◽

Yasuho Yamashita ◽

Kazuhiro Fujita ◽

Masamichi Sato ◽

...

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Deep Learning ◽

Regression Model ◽

Linear Model ◽

Regression Models ◽

Logistic Regression Model ◽

Learning Approach ◽

Logistic Regression Models ◽

Custom Made

Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.

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BrC-MCDLM: breast Cancer detection using Multi-Channel deep learning model

Multimedia Tools and Applications ◽

10.1007/s11042-021-11199-y ◽

2021 ◽

Author(s):

Jitendra V. Tembhurne ◽

Anupama Hazarika ◽

Tausif Diwan

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Cancer Detection ◽

Learning Model ◽

Breast Cancer Detection ◽

Deep Learning Model

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Deep Learning Model for Classification of Breast Cancer

Artificial Intelligence in Breast Cancer Early Detection and Diagnosis ◽

10.1007/978-3-030-59208-0_6 ◽

2020 ◽

pp. 93-100

Author(s):

Khalid Shaikh ◽

Sabitha Krishnan ◽

Rohit Thanki

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Learning Model ◽

Deep Learning Model

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Deep learning model for fully automated breast cancer detection system from thermograms

PLoS ONE ◽

10.1371/journal.pone.0262349 ◽

2022 ◽

Vol 17 (1) ◽

pp. e0262349

Author(s):

Esraa A. Mohamed ◽

Essam A. Rashed ◽

Tarek Gaber ◽

Omar Karam

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Cancer Detection ◽

Detection System ◽

Diagnostic Technique ◽

Learning Model ◽

The Body ◽

Breast Cancer Detection ◽

Benchmark Database ◽

Deep Learning Model

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

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Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

Scientific Reports ◽

10.1038/s41598-017-04075-z ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 111

Author(s):

Zhongyi Han ◽

Benzheng Wei ◽

Yuanjie Zheng ◽

Yilong Yin ◽

Kejian Li ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Learning Model ◽

Histopathological Images ◽

Multi Classification ◽

Deep Learning Model

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Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

Frontiers in Oncology ◽

10.3389/fonc.2021.623506 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xianyu Zhang ◽

Hui Li ◽

Chaoyun Wang ◽

Wen Cheng ◽

Yuntao Zhu ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Molecular Subtypes ◽

Learning Model ◽

Breast Ultrasound ◽

Ultrasound Images ◽

Test Set ◽

External Test ◽

Deep Learning Model

Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

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Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study

Neurosurgical FOCUS ◽

10.3171/2018.8.focus18243 ◽

2018 ◽

Vol 45 (5) ◽

pp. E12 ◽

Cited By ~ 18

Author(s):

Victor E. Staartjes ◽

Carlo Serra ◽

Giovanni Muscas ◽

Nicolai Maldaner ◽

Kevin Akeret ◽

...

Keyword(s):

Logistic Regression ◽

Deep Learning ◽

Pituitary Adenoma ◽

Pilot Study ◽

Transsphenoidal Surgery ◽

Class Imbalance ◽

Learning Model ◽

Gross Total Resection ◽

Total Resection ◽

Deep Learning Model

OBJECTIVEGross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate cases and by poor interrater agreement. Deep learning, a subset of machine learning, has recently established itself as highly effective in forecasting medical outcomes. In this pilot study, the authors aimed to evaluate the utility of using deep learning to predict GTR after transsphenoidal surgery for pituitary adenoma.METHODSData from a prospective registry were used. The authors trained a deep neural network to predict GTR from 16 preoperatively available radiological and procedural variables. Class imbalance adjustment, cross-validation, and random dropout were applied to prevent overfitting and ensure robustness of the predictive model. The authors subsequently compared the deep learning model to a conventional logistic regression model and to the Knosp classification as a gold standard.RESULTSOverall, 140 patients who underwent endoscopic transsphenoidal surgery were included. GTR was achieved in 95 patients (68%), with a mean extent of resection of 96.8% ± 10.6%. Intraoperative high-field MRI was used in 116 (83%) procedures. The deep learning model achieved excellent area under the curve (AUC; 0.96), accuracy (91%), sensitivity (94%), and specificity (89%). This represents an improvement in comparison with the Knosp classification (AUC: 0.87, accuracy: 81%, sensitivity: 92%, specificity: 70%) and a statistically significant improvement in comparison with logistic regression (AUC: 0.86, accuracy: 82%, sensitivity: 81%, specificity: 83%) (all p < 0.001).CONCLUSIONSIn this pilot study, the authors demonstrated the utility of applying deep learning to preoperatively predict the likelihood of GTR with excellent performance. Further training and validation in a prospective multicentric cohort will enable the development of an easy-to-use interface for use in clinical practice.

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