Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk

Wetstein, Suzanne C.; Onken, Allison; Luffman, Christina; Baker, Gabrielle M.; Pyle, Michael E.; Kensler, Kevin H.; Liu, Ying; Bakker, Bart; Vlutters, R.; Leeuwen, M.B. van; Collins, Laura C.; Schnitt, Stuart J.; Pluim, Josien P. W.; Tamimi, Rulla M.; Heng, Yujing J.; Veta, Mitko

doi:10.1371/journal.pone.0231653

Cited by 17 publications

(10 citation statements)

References 39 publications

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“…Automated systems have the potential to decrease the workload of pathologists and standardize clinical practice [37,38]. Deep learning based grading and survival prediction have been previously applied to histopathology images [38][39][40][41][42] and deep neural network models have been successfully developed for other tasks specific to breast histopathology [43][44][45][46][47][48][49]. State-of-the-art deep convolutional neural networks (CNN) have been shown to outperform pathologists in detecting metastases in sentinel lymph nodes of breast cancer patients [50].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Wetstein

Stathonikos

Pluim

et al. 2021

Laboratory Investigation

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Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen’s kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Wetstein

Stathonikos

Pluim

et al. 2021

Laboratory Investigation

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“…For example, terminal duct lobular unit (TDLU) involution assessed using qualitative and semi-quantitative methods was suggested to be linked to lower BC risk ( 24‐27 ). We developed and applied an automated deep-learning method to capture quantitative measures of TDLU involution ( 28 , 29 ) in a large, nested case-control study ( 30 ).…”

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confidence: 99%

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattan

Kensler

et al. 2021

JNCI Cancer Spectrum

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Background New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses’ Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, Ptrend = .047). No morphometric signature was associated with BC. Conclusions The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.

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“…Accurate predicting the development of cancers or complications of cancers could indicate earlier diagnosis and therapeutic approaches that would improve outcomes. [26,31,33,35,37,39,41,53,55,[58][59][60]73] The majority of these ML applications use imaging data (most often histologic type) for classification of malignant versus benign tumors. Cardiovascular conditions and DM are among the most common medical conditions used in predictive analysis.…”

Section: Discussionmentioning

confidence: 99%

“…[38,52,67] The most successful and meaningful application of deep learning ML models was achieved in the imaging field. [53,[55][56][57][58][59][60][61][62][63][64][65] Analyses of CT scans, X-rays, Doppler ultrasound, histo-pathological images obtained high accuracy results, which often outperform medical experts. RNN models capture the temporal nature of EHR, imaging and other medical data to predict diseases, complications, and outcomes.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

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Machine learning (ML) models for analyzing medical data are critical for both accelerating development of novel diagnostic and treatment strategies and improving the accuracy of medical care delivery. Our objective was to comprehensively review supervised ML models for diagnosis or treatment prediction. Publications indexed in PubMed were reviewed to identify articles utilizing supervised predictive ML models in medicine. Articles published between 01/01/2020–01/01/2022 were included in this review. Initially, PubMed was searched using MeSH major terms, and if more extensive search results were needed, a broader search was applied (titles/abstracts).PubMed indexed 21,268 published articles (MeSH Major topic) describing ML methods implemented in medicine. Of those, 11,726 articles were published within the last 2 years. Most of the published ML models in medicine in the last two years were different types of deep learning models (about 75%). Fifty articles were included in this review.Almost all categories of disease were subjects of ML predictions. Positive and negative factors in each of the scenarios need to be evaluated before the most optimal ML model is selected. Domain knowledge and collaborations between physicians and ML experts can improve the selection and prediction performance of ML models in medicine and facilitate implementation in clinical practice. Predictive ML models could provide recommendations to recruit suitable patients for clinical trials. Prediction ML models may contribute to development of more effective diagnostic and therapeutic choices, founded on evidence-based medicine. A broad range of methodological approaches have been taken toward this goal, and those approaches are presented here with their various advantages and disadvantages.AUTHOR SUMMARYOver the last decade, there has been rapid development of Machine learning (ML) methods to analyze Big Data in medicine. ML is aimed to make the computer learn from past experiences and make predictions by recognizing patterns in medical data. We performed a comprehensive systematic literature review of recent publications (last two years), indexed in PubMed/MEDLINE that have described either traditional or deep supervised prediction ML models in medicine. We identified 21,268 articles describing ML implementation in medicine. 11,726 articles were published in the last 2 years. We presented the number of publications describing each of the most often ML methods to show current trends in development of these models. Most of the recently published ML models in medicine were deep learning models. We found that the understanding of disease is likely to lead to more accurate prediction. An important dilemma is the selection of optimal ML models for a specific task, considering amount and type of available data. Domain knowledge and collaborations between physicians and ML experts can improve the prediction performance of ML models, which could help clinicians to select the most effective diagnostic and therapeutic choices available and decrease medical errors.

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Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk

Cited by 17 publications

References 39 publications

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

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