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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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Dacic S, Travis WD, Giltnane JM, Kos F, Abel J, Hilz S, Fujimoto J, Sholl L, Ritter J, Khalil F, Liu Y, Taylor-Weiner A, Resnick M, Yu H, Hirsch FR, Bunn PA, Carbone DP, Rusch V, Kwiatkowski DJ, Johnson BE, Lee JM, Hennek SR, Wapinski I, Nicholas A, Johnson A, Schulze K, Kris MG, Wistuba II. Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study. J Thorac Oncol 2024; 19:719-731. [PMID: 38070597 DOI: 10.1016/j.jtho.2023.12.010] [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: 06/22/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 12/31/2023]
Abstract
INTRODUCTION Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate. METHODS We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists. RESULTS For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84-0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09-0.97, p = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02-1.06, p = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements. CONCLUSIONS Artificial intelligence-powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence-powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.
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Affiliation(s)
- Sanja Dacic
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
| | - William D Travis
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Filip Kos
- Department of Machine Learning, PathAI, Inc., Boston, Massachusetts
| | - John Abel
- Department of Machine Learning, PathAI, Inc., Boston, Massachusetts
| | - Stephanie Hilz
- Research Pathology, Genentech, Inc., South San Francisco, California
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lynette Sholl
- Department of Anatomic Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jon Ritter
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Farah Khalil
- Department of Pathology, Moffitt Cancer Center, Tampa, Florida
| | - Yi Liu
- Department of Machine Learning, PathAI, Inc., Boston, Massachusetts
| | | | - Murray Resnick
- Department of Pathology, PathAI, Inc., Boston, Massachusetts
| | - Hui Yu
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Fred R Hirsch
- Department of Hematology and Medical Oncology, University of Colorado/Icahn School of Medicine, Mount Sinai, New York
| | - Paul A Bunn
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - David P Carbone
- Division of Medical Oncology, The Ohio State University Medical Center and Pelotonia Institute for Immuno-Oncology, Columbus, Ohio
| | - Valerie Rusch
- Thoracic Surgery Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David J Kwiatkowski
- Department of Anatomic Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Bruce E Johnson
- Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jay M Lee
- Division of Thoracic Surgery, University of California, Los Angeles, Los Angeles, California
| | - Stephanie R Hennek
- Department of Translational Research, PathAI, Inc., Boston, Massachusetts
| | - Ilan Wapinski
- Department of Translational Research, PathAI, Inc., Boston, Massachusetts
| | - Alan Nicholas
- U.S. Medical Affairs, Genentech, Inc., South San Francisco, California
| | - Ann Johnson
- U.S. Medical Affairs, Genentech, Inc., South San Francisco, California
| | - Katja Schulze
- Research Pathology, Genentech, Inc., South San Francisco, California
| | - Mark G Kris
- Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Zhu M, Kuang Y, Jiang Z, Liu J, Zhang H, Zhao H, Luo H, Chen Y, Peng Y. Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma in situ. Gland Surg 2024; 13:512-527. [PMID: 38720675 PMCID: PMC11074652 DOI: 10.21037/gs-23-417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/10/2024] [Indexed: 05/12/2024]
Abstract
Background Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.
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Affiliation(s)
- Meng Zhu
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yalan Kuang
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zekun Jiang
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, China
| | - Jingyan Liu
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Heqing Zhang
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Haina Zhao
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Honghao Luo
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yujuan Chen
- Department of Breast Surgery, West China Hospital of Sichuan University, Chengdu, China
| | - Yulan Peng
- Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
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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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Ii T, Chambers JK, Nakashima K, Goto-Koshino Y, Uchida K. Application of automated machine learning for histological evaluation of feline endoscopic samples. J Vet Med Sci 2024; 86:160-167. [PMID: 38104975 PMCID: PMC10898981 DOI: 10.1292/jvms.23-0299] [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: 12/19/2023] Open
Abstract
Differentiating intestinal T-cell lymphoma from chronic enteropathy (CE) in endoscopic samples is often challenging. In the present study, automated machine learning systems were developed to distinguish between the two diseases, predict clonality, and detect prognostic factors of intestinal lymphoma in cats. Four models were created for four experimental conditions: experiment 1 to distinguish between intestinal T-cell lymphoma and CE; experiment 2 to distinguish large cell lymphoma, small cell lymphoma, and CE; experiment 3 to distinguish granzyme B+ lymphoma, granzyme B- lymphoma, and CE; and experiment 4 to distinguish between T-cell receptor (TCR) clonal population and TCR polyclonal population. After each experiment, a pathologist reviewed the test images and scored for lymphocytic infiltration, epitheliotropism, and epithelial injury. The models of experiments 1-4 achieved area under the receiver operating characteristic curve scores of 0.943 (precision, 87.59%; recall, 87.59%), 0.962 (precision, 86.30%; recall, 86.30%), 0.904 (precision, 82.86%; recall, 80%), and 0.904 (precision, 81.25%; recall, 81.25%), respectively. The images predicted as intestinal T-cell lymphoma showed significant infiltration of lymphocytes and epitheliotropism than CE. These models can provide evaluation tools to assist pathologists with differentiating between intestinal T-cell lymphoma and CE.
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Affiliation(s)
- Tatsuhito Ii
- Laboratory of Veterinary Pathology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - James K Chambers
- Laboratory of Veterinary Pathology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Ko Nakashima
- Japan Small Animal Medical Center (JSAMC), Saitama, Japan
| | - Yuko Goto-Koshino
- Veterinary Medical Center, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Kazuyuki Uchida
- Laboratory of Veterinary Pathology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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Mudeng V, Farid MN, Ayana G, Choe SW. Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:2080-2098. [PMID: 37673327 DOI: 10.1016/j.ajpath.2023.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 06/30/2023] [Accepted: 07/19/2023] [Indexed: 09/08/2023]
Abstract
Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual evaluation may lead to high costs and be ineffective because diagnosis depends on the facility and the pathologists' insights and experiences. Convolutional neural network acts as a computer-based observer to improve clinicians' capacity in grading breast cancer. Therefore, this study proposes a novel scheme for automatic breast cancer malignancy grading from invasive ductal carcinoma. The proposed classifiers implement multistage transfer learning incorporating domain and histopathologic transformations. Domain adaptation using pretrained models, such as InceptionResNetV2, InceptionV3, NASNet-Large, ResNet50, ResNet101, VGG19, and Xception, was applied to classify the ×40 magnification BreaKHis data set into eight classes. Subsequently, InceptionV3 and Xception, which contain the domain and histopathology pretrained weights, were determined to be the best for this study and used to categorize the Databiox database into grades 1, 2, or 3. To provide a comprehensive report, this study offered a patchless automated grading system for magnification-dependent and magnification-independent classifications. With an overall accuracy (means ± SD) of 90.17% ± 3.08% to 97.67% ± 1.09% and an F1 score of 0.9013 to 0.9760 for magnification-dependent classification, the classifiers in this work achieved outstanding performance. The proposed approach could be used for breast cancer grading systems in clinical settings.
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Affiliation(s)
- Vicky Mudeng
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea; Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
| | - Mifta Nur Farid
- Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
| | - Gelan Ayana
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
| | - Se-Woon Choe
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea; Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea.
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Su F, Zhang W, Liu Y, Chen S, Lin M, Feng M, Yin J, Tan L, Shen Y. The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm. J Gastrointest Oncol 2023; 14:1982-1992. [PMID: 37969831 PMCID: PMC10643591 DOI: 10.21037/jgo-23-587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 09/08/2023] [Indexed: 11/17/2023] Open
Abstract
Background Deep learning methods have demonstrated great potential for processing high-resolution images. The U-Net model, in particular, has shown proficiency in the segmentation of biomedical images. However, limited research has examined the application of deep learning to esophageal squamous cell carcinoma (ESCC) segmentation. Therefore, this study aimed to develop deep learning segmentation systems specifically for ESCC. Methods A Visual Geometry Group (VGG)-based U-Net neural network architecture was utilized to develop the segmentation models. A pathological image cohort of surgical specimens was used for model training and internal validation, with two additional endoscopic biopsy section cohort for external validation. Model efficacy was evaluated across several metrics including Intersection over Union (IOU), accuracy, positive predict value (PPV), true positive rate (TPR), specificity, dice similarity coefficient (DSC), area under the receiver operating characteristic curve (AUC), and F1-Score. Results Surgical samples from ten patients were analyzed retrospectively, with each biopsy section cohort encompassing five patients. Transfer learning models based on U-Net weights yielded optimal results. For mucosa segmentation, the in internal validation achieved 93.81% IOU, with other parameters exceeding 96% (96.96% accuracy, 96.45% PPV, 96.65% TPR, 98.41% specificity, 96.81% DSC, 96.11% AUC, and 96.55% F1-Score). The tumor segmentation model attained an IOU of 91.95%, along with other parameters surpassing 95% (95.90% accuracy, 95.62% PPV, 95.71% TPR, 97.88% specificity, 95.81% DSC, 94.92% AUC, and 95.67% F1-Score). In the external validation for tumor segmentation model, IOU was 59.86% for validation database 1 (72.74% for accuracy, 76.03% for PPV, 77.17% for TPR, 83.80% for specificity, 74.89% for DSC, 71.83% for AUC, and 76.60% for F1-Score), and 50.88% for validation cohort 2 (68.03% for accuracy, 59.02% for PPV, 66.87% for TPR, 78.48% for specificity, 67.44% for DSC, 64.68% for AUC, and 62.70% for F1-Score). Conclusions The models exhibited satisfactory results, paving the way for their potential deployment on standard computers and integration with other artificial intelligence models in clinical practice in the future. However, limited to the size of study, the generalizability of models is impaired in the external validation, larger pathological section cohort would be needed in future development to ensure robustness and generalization.
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Affiliation(s)
- Feng Su
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Zhang
- Department of Cardio and Thoracic Surgery, Hanzhong Central Hospital, Hanzhong, China
| | - Yunzhong Liu
- Department of Cardio and Thoracic Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Shanglin Chen
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Miao Lin
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mingxiang Feng
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jun Yin
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lijie Tan
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yaxing Shen
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Tian C, Zhu H, Meng X, Ma Z, Yuan S, Li W. Research for accurate auxiliary diagnosis of lung cancer based on intracellular fluorescent fingerprint information. JOURNAL OF BIOPHOTONICS 2023; 16:e202300174. [PMID: 37350031 DOI: 10.1002/jbio.202300174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/09/2023] [Accepted: 06/20/2023] [Indexed: 06/24/2023]
Abstract
The distinctions in pathological types and genetic subtypes of lung cancer have a direct impact on the choice of treatment choices and clinical prognosis in clinical practice. This study used pathological histological sections of surgically removed or biopsied tumor tissue from 36 patients. Based on a small sample size, millions of spectral data points were extracted to investigate the feasibility of employing intracellular fluorescent fingerprint information to diagnose the pathological types and mutational status of lung cancer. The intracellular fluorescent fingerprint information revealed the EGFR gene mutation characteristics in lung cancer, and the area under the curve (AUC) value for the optimal model was 0.98. For the classification of lung cancer pathological types, the macro average AUC value for the ensemble-learning model was 0.97. Our research contributes new idea for pathological diagnosis of lung cancer and offers a quick, easy, and accurate auxiliary diagnostic approach.
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Affiliation(s)
- Chongxuan Tian
- Department of Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - He Zhu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xiangwei Meng
- Department of Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhixiang Ma
- Department of Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Shuanghu Yuan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, Shandong, China
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wei Li
- Department of Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
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Luo J, Li X, Wei KL, Chen G, Xiong DD. Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review. J Cancer Res Clin Oncol 2023; 149:12535-12542. [PMID: 37389595 DOI: 10.1007/s00432-023-05002-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/15/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND Breast cancer (BC) is a prevalent and highly lethal malignancy affecting women worldwide. Immunotherapy has emerged as a promising therapeutic strategy for BC, offering potential improvements in patient survival. Neoadjuvant therapy (NAT) has also gained significant clinical traction. With the advancement of computer technology, Artificial Intelligence (AI) has been increasingly applied in pathology research, expanding and redefining the scope of the field. This narrative review aims to provide a comprehensive overview of the current literature on the application of computational pathology in BC, specifically focusing on diagnosis, immune microenvironment recognition, and the evaluation of immunotherapy and NAT response. METHODS A thorough examination of relevant literature was conducted, focusing on studies investigating the role of computational pathology in BC diagnosis, immune microenvironment recognition, and immunotherapy and NAT assessment. RESULTS The application of computational pathology has shown significant potential in BC management. AI-based techniques enable improved diagnosis and classification of BC subtypes, enhance the identification and characterization of the immune microenvironment, and facilitate the evaluation of immunotherapy and NAT response. However, challenges related to data quality, standardization, and algorithm development still need to be addressed. CONCLUSION The integration of computational pathology and AI has transformative implications for BC patient care. By leveraging AI-based technologies, clinicians can make more informed decisions in diagnosis, treatment planning, and therapeutic response assessment. Future research should focus on refining AI algorithms, addressing technical challenges, and conducting large-scale clinical validation studies to facilitate the translation of computational pathology into routine clinical practice for BC patients.
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Affiliation(s)
- Jie Luo
- Department of Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, Guangxi, People's Republic of China
| | - Xia Li
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Kang-Lai Wei
- Department of Pathology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, Guangxi, People's Republic of China
| | - Gang Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Dan-Dan Xiong
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.
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