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Boissin C, Wang Y, Sharma A, Weitz P, Karlsson E, Robertson S, Hartman J, Rantalainen M. Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images. Breast Cancer Res 2024; 26:90. [PMID: 38831336 PMCID: PMC11145850 DOI: 10.1186/s13058-024-01840-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/15/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. METHODS A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. RESULTS Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). CONCLUSIONS DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.
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Affiliation(s)
- Constance Boissin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yinxi Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Abhinav Sharma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Philippe Weitz
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Emelie Karlsson
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | | | - Johan Hartman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
- MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden.
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McCaffrey C, Jahangir C, Murphy C, Burke C, Gallagher WM, Rahman A. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn 2024; 24:363-377. [PMID: 38655907 DOI: 10.1080/14737159.2024.2346545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.
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Affiliation(s)
- Christine McCaffrey
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Chowdhury Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
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Katayama A, Aoki Y, Watanabe Y, Horiguchi J, Rakha EA, Oyama T. Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers. Int J Clin Oncol 2024:10.1007/s10147-024-02513-3. [PMID: 38619651 DOI: 10.1007/s10147-024-02513-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.
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Affiliation(s)
- Ayaka Katayama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan.
| | - Yuki Aoki
- Center for Mathematics and Data Science, Gunma University, Maebashi, Japan
| | - Yukako Watanabe
- Clinical Training Center, Gunma University Hospital, Maebashi, Japan
| | - Jun Horiguchi
- Department of Breast Surgery, International University of Health and Welfare, Narita, Japan
| | - Emad A Rakha
- Department of Histopathology School of Medicine, University of Nottingham, University Park, Nottingham, UK
- Department of Pathology, Hamad Medical Corporation, Doha, Qatar
| | - Tetsunari Oyama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan
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Han J, Hua H, Fei J, Liu J, Guo Y, Ma W, Chen J. Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study. Clin Breast Cancer 2024; 24:215-226. [PMID: 38281863 DOI: 10.1016/j.clbc.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Breast cancer is a leading cause of cancer morbility and mortality in women. The possibility of overtreatment or inappropriate treatment exists, and methods for evaluating prognosis need to be improved. MATERIALS AND METHODS Patients (from January 2013 to December 2018) were recruited and divided into a training group and a testing group. All patients were followed for more than 3 years. Patients were divided into a disease-free group and a recurrence group based on follow up results at 3 years. Ultrasound (US) and mammography (MG) images were collected to establish deep learning models (DLMs) using ResNet50. Clinical data, MG, and US characteristics were collected to select independent prognostic factors using a cox proportional hazards model to establish a clinical model. DLM and independent prognostic factors were combined to establish a combined model. RESULTS In total, 1242 patients were included. Independent prognostic factors included age, neoadjuvant chemotherapy, HER2, orientation, blood flow, dubious calcification, and size. We established 5 models: the US DLM, MG DLM, US + MG DLM, clinical and combined model. The combined model using US images, MG images, and pathological, clinical, and radiographic characteristics had the highest predictive performance (AUC = 0.882 in the training group, AUC = 0.739 in the testing group). CONCLUSION DLMs based on the combination of US, MG, and clinical data have potential as predictive tools for breast cancer prognosis.
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Affiliation(s)
- Junqi Han
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Hui Hua
- Department of Thyroid Surgery, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Jie Fei
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Jingjing Liu
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Yijun Guo
- Department of Breast Imaging Diagnosis, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, People's Republic of China
| | - Wenjuan Ma
- Department of Breast Imaging Diagnosis, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, People's Republic of China
| | - Jingjing Chen
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
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Kang D, Wang C, Han Z, Zheng L, Guo W, Fu F, Qiu L, Han X, He J, Li L, Chen J. Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer. BMC Cancer 2024; 24:318. [PMID: 38454386 PMCID: PMC10921807 DOI: 10.1186/s12885-024-12069-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/28/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients. METHODS In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs. RESULTS Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3. CONCLUSION Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.
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Affiliation(s)
- Deyong Kang
- Department of Pathology, Fujian Medical University Union Hospital, 350001, Fuzhou, P. R. China
| | - Chuan Wang
- Breast Surgery Ward, Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, P. R. China
| | - Zhonghua Han
- Breast Surgery Ward, Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, P. R. China
| | - Liqin Zheng
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, 350007, Fuzhou, P. R. China
| | - Wenhui Guo
- Breast Surgery Ward, Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, P. R. China
| | - Fangmeng Fu
- Breast Surgery Ward, Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, P. R. China
| | - Lida Qiu
- College of Physics and Electronic Information Engineering, Minjiang University, 350108, Fuzhou, P. R. China
| | - Xiahui Han
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, 350007, Fuzhou, P. R. China
| | - Jiajia He
- School of Science, Jimei University, 361021, Xiamen, P. R. China.
| | - Lianhuang Li
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, 350007, Fuzhou, P. R. China.
| | - Jianxin Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, 350007, Fuzhou, P. R. China.
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Sharma A, Weitz P, Wang Y, Liu B, Vallon-Christersson J, Hartman J, Rantalainen M. Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer. Breast Cancer Res 2024; 26:17. [PMID: 38287342 PMCID: PMC10823657 DOI: 10.1186/s13058-024-01770-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/15/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. METHODS This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. RESULTS We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. CONCLUSION Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.
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Affiliation(s)
- Abhinav Sharma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Philippe Weitz
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yinxi Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Bojing Liu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Johan Hartman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
- MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden.
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Marmé F, Krieghoff-Henning E, Gerber B, Schmitt M, Zahm DM, Bauerschlag D, Forstbauer H, Hildebrandt G, Ataseven B, Brodkorb T, Denkert C, Stachs A, Krug D, Heil J, Golatta M, Kühn T, Nekljudova V, Gaiser T, Schönmehl R, Brochhausen C, Loibl S, Reimer T, Brinker TJ. Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images. Eur J Cancer 2023; 195:113390. [PMID: 37890350 DOI: 10.1016/j.ejca.2023.113390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/07/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images. METHODS Using a ResNet 50 architecture pretrained on ImageNet and a previously successful strategy, we trained deep learning (DL)-based image analysis algorithms to predict sentinel status on hematoxylin/eosin-stained images of predominantly luminal, primary breast tumours from the INSEMA trial and three additional, independent cohorts (The Cancer Genome Atlas (TCGA) and cohorts from the University hospitals of Mannheim and Regensburg), and compared their performance with that of a logistic regression using clinical data only. Performance on an INSEMA hold-out set was investigated in a blinded manner. RESULTS None of the generated image analysis algorithms yielded significantly better than random areas under the receiver operating characteristic curves on the test sets, including the hold-out test set from INSEMA. In contrast, the logistic regression fitted on the Mannheim cohort retained a better than random performance on INSEMA and Regensburg. Including the image analysis model output in the logistic regression did not improve performance further on INSEMA. CONCLUSIONS Employing DL-based image analysis on histological slides, we could not predict SLN status for unseen cases in the INSEMA trial and other predominantly luminal cohorts.
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Affiliation(s)
- Frederik Marmé
- Department of Obstetrics and Gynaecology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bernd Gerber
- Department of Obstetrics and Gynecology, University Hospital of Rostock, Rostock, Germany
| | - Max Schmitt
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Dirk Bauerschlag
- Department of Gynecology and Obstetrics, University Medical Center Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany
| | | | - Guido Hildebrandt
- Department of Radiotherapy, University Medicine Rostock, Rostock, Germany
| | - Beyhan Ataseven
- Department of Gynecology, Gynecologic Oncology and Obstetrics, Klinikum Lippe, Bielefeld University, Medical School and University Medical Center East Westphalia-Lippe, Bielefeld, Germany
| | - Tobias Brodkorb
- Department of Obstetrics and Gynaecology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany
| | - Carsten Denkert
- Institute of Pathology, University Clinic Marburg, Marburg, Germany
| | - Angrit Stachs
- Department of Obstetrics and Gynecology, University Hospital of Rostock, Rostock, Germany
| | - David Krug
- Klinik für Strahlentherapie, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Jörg Heil
- Brustzentrum Heidelberg - Klinik St. Elisabeth, Heidelberg, Germany; Department of Obstetrics and Gynecology, Uniklinikum Heidelberg, Heidelberg, Germany
| | - Michael Golatta
- Brustzentrum Heidelberg - Klinik St. Elisabeth, Heidelberg, Germany; Department of Obstetrics and Gynecology, Uniklinikum Heidelberg, Heidelberg, Germany
| | - Thorsten Kühn
- Department of Gynaecology and Obstetrics, Klinikum Esslingen, Neckar, Germany
| | | | - Timo Gaiser
- Institute of Pathology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany
| | - Rebecca Schönmehl
- Institute of Pathology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany
| | - Christoph Brochhausen
- Institute of Pathology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany; Institute of Pathology, University Regensburg, Regensburg, Germany
| | - Sibylle Loibl
- German Breast Group, GBG Forschungs GmbH, Neu-Isenburg, Germany
| | - Toralf Reimer
- Department of Obstetrics and Gynecology, University Hospital of Rostock, Rostock, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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8
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Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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9
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Khoraminia F, Fuster S, Kanwal N, Olislagers M, Engan K, van Leenders GJLH, Stubbs AP, Akram F, Zuiverloon TCM. Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review. Cancers (Basel) 2023; 15:4518. [PMID: 37760487 PMCID: PMC10526515 DOI: 10.3390/cancers15184518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/30/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell's nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets-representative of clinical scenarios-are needed to address artificial intelligence's reliability, robustness, and black box challenge.
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Affiliation(s)
- Farbod Khoraminia
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands;
| | - Saul Fuster
- Department of Electrical Engineering and Computer Science, University of Stavanger, 4021 Stavanger, Norway; (S.F.); (N.K.); (K.E.)
| | - Neel Kanwal
- Department of Electrical Engineering and Computer Science, University of Stavanger, 4021 Stavanger, Norway; (S.F.); (N.K.); (K.E.)
| | - Mitchell Olislagers
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands;
| | - Kjersti Engan
- Department of Electrical Engineering and Computer Science, University of Stavanger, 4021 Stavanger, Norway; (S.F.); (N.K.); (K.E.)
| | - Geert J. L. H. van Leenders
- Department of Pathology and Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (G.J.L.H.v.L.); (A.P.S.); (F.A.)
| | - Andrew P. Stubbs
- Department of Pathology and Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (G.J.L.H.v.L.); (A.P.S.); (F.A.)
| | - Farhan Akram
- Department of Pathology and Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands; (G.J.L.H.v.L.); (A.P.S.); (F.A.)
| | - Tahlita C. M. Zuiverloon
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands;
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10
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Köteles MM, Vigdorovits A, Kumar D, Mihai IM, Jurescu A, Gheju A, Bucur A, Harich OO, Olteanu GE. Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists' Assessment Approach. Diagnostics (Basel) 2023; 13:2326. [PMID: 37510069 PMCID: PMC10377791 DOI: 10.3390/diagnostics13142326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model's grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.
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Affiliation(s)
| | - Alon Vigdorovits
- Bihor County Clinical Emergency Hospital, Gh. Doja Street No. 65, 410169 Oradea, Romania
- Center for Research and Innovation in Personalized Medicine of Respiratory Diseases, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
- Victor Babes Institute of Pathology-Next Generation Pathology Research Group, Splaiul Independenţei 99-101, 050096 Bucharest, Romania
| | | | - Ioana-Maria Mihai
- Department of Microscopic Morphology-Morphopatology, ANAPATMOL Research Center, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Aura Jurescu
- Department of Microscopic Morphology-Morphopatology, ANAPATMOL Research Center, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Adelina Gheju
- Emergency County Hospital Deva, Bulevardul 22 Decembrie 58, 330032 Deva, Romania
| | - Adeline Bucur
- Department of Microscopic Morphology, Discipline of Histology, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
| | - Octavia Oana Harich
- Department of Functional Sciences, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
| | - Gheorghe-Emilian Olteanu
- Center for Research and Innovation in Personalized Medicine of Respiratory Diseases, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
- Faculty of Pharmacy, "Victor Babes" University of Medicine and Pharmacy, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, "Victor Babes" University of Medicine and Pharmacy, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
- Center of Expertise for Rare Lung Diseases, Clinical Hospital of Infectious Diseases and Pneumophthisiology "Dr. Victor Babes" Timisoara, Gh. Adam Street No. 13, 300310 Timisoara, Romania
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11
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Chen Y, Li H, Janowczyk A, Toro P, Corredor G, Whitney J, Lu C, Koyuncu CF, Mokhtari M, Buzzy C, Ganesan S, Feldman MD, Fu P, Corbin H, Harbhajanka A, Gilmore H, Goldstein LJ, Davidson NE, Desai S, Parmar V, Madabhushi A. Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer. NPJ Breast Cancer 2023; 9:40. [PMID: 37198173 PMCID: PMC10192429 DOI: 10.1038/s41523-023-00545-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN-) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN- IBC. H&E images from a total of n = 321 patients with ER+ and LN- IBC from three cohorts were employed for this study (Training set: D1 (n = 116), Validation sets: D2 (n = 121) and D3 (n = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02-5.32, p = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18-7.35, p = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20-89.18, p = 0.0106; D1: p = 0.0238; D2: p = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.
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Grants
- R01 CA216579 NCI NIH HHS
- C06 RR012463 NCRR NIH HHS
- R43 EB028736 NIBIB NIH HHS
- U01 CA239055 NCI NIH HHS
- R01 CA249992 NCI NIH HHS
- UG1 CA233328 NCI NIH HHS
- R01 CA220581 NCI NIH HHS
- R01 CA202752 NCI NIH HHS
- R01 CA208236 NCI NIH HHS
- U01 CA248226 NCI NIH HHS
- I01 BX004121 BLRD VA
- R01 CA257612 NCI NIH HHS
- U54 CA254566 NCI NIH HHS
- Research reported in this study was supported by the National Cancer Institute under award numbers R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01, National Heart, Lung and Blood Institute 1R01HL15127701A1, R01HL15807101A1, National Institute of Biomedical Imaging and Bioengineering 1R43EB028736-01, National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345, W81XWH-21-1-0160), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly and Astrazeneca.
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Affiliation(s)
- Yuli Chen
- Shaanxi Normal University, School of Computer Science, Xi'an, China
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Haojia Li
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Precision Oncology Center, University of Lausanne, Lausanne, Switzerland
| | - Paula Toro
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Germán Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Cheng Lu
- Shaanxi Normal University, School of Computer Science, Xi'an, China
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Can F Koyuncu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mojgan Mokhtari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Christina Buzzy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Michael D Feldman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, USA
| | - Haley Corbin
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Hannah Gilmore
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Nancy E Davidson
- Fred Hutchinson Cancer Research Center, University of Washington, and Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Sangeeta Desai
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Vani Parmar
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Anant Madabhushi
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Louis Stokes VA Medical Center, Cleveland, OH, USA.
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12
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Zhang L. The Role of Mesenchymal Stem Cells in Modulating the Breast Cancer Microenvironment. Cell Transplant 2023; 32:9636897231220073. [PMID: 38135917 DOI: 10.1177/09636897231220073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2023] Open
Abstract
The role of mesenchymal stem cells (MSCs) in the breast tumor microenvironment (TME) is significant and multifaceted. MSCs are recruited to breast tumor sites through molecular signals released by tumor sites. Once in the TME, MSCs undergo polarization and interact with various cell populations, including immune cells, cancer-associated fibroblasts (CAFs), cancer stem cells (CSCs), and breast cancer cells. In most cases, MSCs play roles in breast cancer therapeutic resistance, but there is also evidence that indicates their abilities to sensitize cancer cells to chemotherapy and radiotherapy. MSCs possess inherent regenerative and homing properties, making them attractive candidates for cell-based therapies. Therefore, MSCs can be engineered to express therapeutic molecules or deliver anti-cancer agents directly to tumor sites. Unraveling the intricate relationship between MSCs and the breast TME has the potential to uncover novel therapeutic targets and advance our understanding of breast cancer biology.
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Affiliation(s)
- Luxiao Zhang
- Department of Surgical Oncology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
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