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Xie W, Lin W, Li P, Lai H, Wang Z, Liu P, Huang Y, Liu Y, Tang L, Lyu G. Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study. J Cancer Res Clin Oncol 2024; 150:346. [PMID: 38981916 PMCID: PMC11233367 DOI: 10.1007/s00432-024-05872-6] [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: 05/21/2024] [Accepted: 06/27/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE To develop a deep learning (DL) model for differentiating between benign and malignant ovarian tumors of Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions, and validate its diagnostic performance. METHODS A retrospective analysis of 1619 US images obtained from three centers from December 2014 to March 2023. DeepLabV3 and YOLOv8 were jointly used to segment, classify, and detect ovarian tumors. Precision and recall and area under the receiver operating characteristic curve (AUC) were employed to assess the model performance. RESULTS A total of 519 patients (including 269 benign and 250 malignant masses) were enrolled in the study. The number of women included in the training, validation, and test cohorts was 426, 46, and 47, respectively. The detection models exhibited an average precision of 98.68% (95% CI: 0.95-0.99) for benign masses and 96.23% (95% CI: 0.92-0.98) for malignant masses. Moreover, in the training set, the AUC was 0.96 (95% CI: 0.94-0.97), whereas in the validation set, the AUC was 0.93(95% CI: 0.89-0.94) and 0.95 (95% CI: 0.91-0.96) in the test set. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive values for the training set were 0.943,0.957,0.951,0.966, and 0.936, respectively, whereas those for the validation set were 0.905,0.935, 0.935,0.919, and 0.931, respectively. In addition, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the test set were 0.925, 0.955, 0.941, 0.956, and 0.927, respectively. CONCLUSION The constructed DL model exhibited high diagnostic performance in distinguishing benign and malignant ovarian tumors in O-RADS US category 4 lesions.
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Affiliation(s)
- Wenting Xie
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Fujian medical University, Quanzhou, Fujian Province, 362000, China
- Department of Ultrasound, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical University, Fuzhou, Fujian Province, 350014, China
| | - Wenjie Lin
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Fujian medical University, Quanzhou, Fujian Province, 362000, China
| | - Ping Li
- Department of Gynecology and Obstetrics, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, 362000, China
| | - Hongwei Lai
- Department of Ultrasound, Fujian Provincial Maternity and Children's Hospital, Fuzhou, Fujian Province, 350014, China
| | - Zhilan Wang
- Department of Ultrasound, Nanping First Hospital Affiliated to Fujian Medical University, Nanping, Fujian Province, 35300, China
| | - Peizhong Liu
- School of Medicine, Huaqiao University, Quanzhou, Fujian Province, 362000, China
| | - Yijun Huang
- Department of Ultrasound, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical University, Fuzhou, Fujian Province, 350014, China
| | - Yao Liu
- Quanzhou Bolang Technology Group Co., Ltd, Quanzhou, Fujian Province, 362000, China.
| | - Lina Tang
- Department of Ultrasound, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical University, Fuzhou, Fujian Province, 350014, China.
| | - Guorong Lyu
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Fujian medical University, Quanzhou, Fujian Province, 362000, China.
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Miao K, Lv Q, Zhang L, Zhao N, Dong X. Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm. Eur J Obstet Gynecol Reprod Biol 2024; 298:135-139. [PMID: 38756053 DOI: 10.1016/j.ejogrb.2024.05.010] [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: 02/19/2024] [Revised: 05/10/2024] [Accepted: 05/11/2024] [Indexed: 05/18/2024]
Abstract
PURPOSE The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated. METHODS A retrospective analysis was conducted on OEC and MC patients who had sought medical attention at the Fourth Affiliated Hospital of Harbin Medical University between August 2018 and May 2023. The diagnosis was established based on postoperative pathology or the characteristics of aspirated fluid guided by ultrasound, serving as the gold standard. Ultrasound images were collected and subjected to screening and preprocessing procedures. The data set was randomly divided into training, validation, and testing sets in a ratio of 5:3:2. Transfer learning was utilized to determine the initial weights of the ConvNeXt deep learning algorithm, which were further adjusted by retraining the algorithm using the training and validation ultrasound images to establish a new deep learning model. The weights that yielded the highest accuracy were selected to evaluate the diagnostic performance of the model using the validation set. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. Additionally, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio were calculated. Decision curve analysis (DCA) curves were plotted. RESULTS The study included 786 ultrasound images from 184 patients diagnosed with either OEC or MC. The deep learning model achieved an AUC of 0.90 (95 % CI: 0.85-0.95) in accurately distinguishing between the two conditions, with a sensitivity of 90 % (95 % CI: 84 %-95 %), specificity of 90 % (95 % CI: 77 %-97 %), a positive predictive value of 96 % (95 % CI: 91 %-99 %), a negative predictive value of 77 % (95 % CI: 63 %-88 %), a positive likelihood ratio of 9.27 (95 % CI: 3.65-23.56), and a negative likelihood ratio of 0.11 (95 % CI: 0.06-0.19). The DCA curve demonstrated the practical clinical utility of the model. CONCLUSIONS The deep learning model developed using the ConvNeXt algorithm exhibits high accuracy (90 %) in distinguishing between OEC and MC. This model demonstrates excellent diagnostic performance and clinical utility, providing a novel approach for the clinical differentiation of these two conditions.
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Affiliation(s)
- Kuo Miao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Qian Lv
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Liwei Zhang
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Ning Zhao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaoqiu Dong
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
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Ding CW, Ren YK, Wang CS, Zhang YC, Zhang Y, Yang M, Mao P, Sheng YJ, Chen XF, Liu CF. Prediction of Parkinson's disease by transcranial sonography-based deep learning. Neurol Sci 2024; 45:2641-2650. [PMID: 37985633 DOI: 10.1007/s10072-023-07154-4] [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: 09/01/2023] [Accepted: 10/21/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVES Transcranial sonography has been used as a valid neuroimaging tool to diagnose Parkinson's disease (PD). This study aimed to develop a modified transcranial sonography (TCS) technique based on a deep convolutional neural network (DCNN) model to predict Parkinson's disease. METHODS This retrospective diagnostic study was conducted using 1529 transcranial sonography images collected from 854 patients with PD and 775 normal controls admitted to the Second Affiliated Hospital of Soochow University (Suzhou, Jiangsu, China) between September 2019 and May 2022. The data set was divided into training cohorts (570 PD patients and 541 normal controls), and the validation set (184 PD patients and 234 normal controls). Using these datasets, we developed four different DCNN models (ResNet18, ResNet50, ResNet152, and DenseNet121). We then assessed their diagnostic performance, including the area under the receiver operating characteristic (AUROC) curve, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F1 score and compared with traditional diagnostic criteria. RESULTS Among the 1529 TCS images, 570 PD patients and 541 normal controls from 4 of 6 sonographers of the TCS team were selected as the training cohort, and 184 PD patients and 234 normal controls from the other 2 sonographers were chosen as the validation cohort. There were no sex and age differences between PD patients and normal control subjects in the training and validation cohorts (P values > 0.05). All DCNN models achieved good performance in distinguishing PD patients from normal control subjects on the validation datasets, with diagnostic AUROCs and accuracy of 0.949 (95% CI 0.925, 0.965) and 86.60 for the RestNet18 model, 0.949 (95% CI 0.929, 0.971) and 87.56 for ResNet50, 0.945 (95% CI 0.931, 0.969) and 88.04 for ResNet152, 0.953 (95% CI 0.935, 0.971) and 87.80 for DenseNet121, respectively. On the other hand, the diagnostic accuracy of the traditional diagnostic method was 82.30. The accuracy of all DCNN models was higher than that of traditional diagnostic method. Moreover, the 5k-fold cross-validation results in train datasets showed that these DCNN models are robust. CONCLUSION The developed transcranial sonography-based DCNN models performed better than traditional diagnostic criteria, thus improving the sonographer's accuracy in diagnosing PD.
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Affiliation(s)
- Chang Wei Ding
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Ya Kun Ren
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Cai Shan Wang
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Ying Chun Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China.
| | - Ying Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Min Yang
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Pan Mao
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Yu Jing Sheng
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Xiao Fang Chen
- Department of Ultrasound, The Second Affiliated Hospital of Soochow University, 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Chun Feng Liu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
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Yao L, Li S, Tao Q, Mao Y, Dong J, Lu C, Han C, Qiu B, Huang Y, Huang X, Liang Y, Lin H, Guo Y, Liang Y, Chen Y, Lin J, Chen E, Jia Y, Chen Z, Zheng B, Ling T, Liu S, Tong T, Cao W, Zhang R, Chen X, Liu Z. Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study. EBioMedicine 2024; 104:105183. [PMID: 38848616 PMCID: PMC11192791 DOI: 10.1016/j.ebiom.2024.105183] [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: 10/27/2023] [Revised: 04/30/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).
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Affiliation(s)
- Lisha Yao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Suyun Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, South Medical University, Guangzhou, China
| | - Quan Tao
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yun Mao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Dong
- Department of Radiology, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), The Third Affiliated Hospital of Shanxi Medical University, Taiyuan, China
| | - Cheng Lu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Bingjiang Qiu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Sciences), Guangzhou, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Xin Huang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, Shantou University Medical College, Shantou, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, South Medical University, Guangzhou, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Yongmei Guo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Yingying Liang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
| | - Yizhou Chen
- Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
| | - Jie Lin
- Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
| | - Enyan Chen
- Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
| | - Yanlian Jia
- Department of Radiology, Liaobu Hospital of Guangdong, Dongguan, China
| | - Zhihong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Bochi Zheng
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Tong Ling
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ruiping Zhang
- Department of Radiology, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), The Third Affiliated Hospital of Shanxi Medical University, Taiyuan, China.
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
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Fanizzi A, Arezzo F, Cormio G, Comes MC, Cazzato G, Boldrini L, Bove S, Bollino M, Kardhashi A, Silvestris E, Quarto P, Mongelli M, Naglieri E, Signorile R, Loizzi V, Massafra R. An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators. Cancer Med 2024; 13:e7425. [PMID: 38923847 PMCID: PMC11196372 DOI: 10.1002/cam4.7425] [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: 10/09/2023] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black-boxes due to the difficulty of understanding the decision-making process used by the algorithm to obtain a specific result. AIMS For this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis. MATERIALS & METHODS Since the diagnostic task was a three-class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme. RESULTS The accuracy of the three-class model reaches an overall accuracy of 86.36%, and the precision per-class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. DISCUSSION SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system. CONCLUSIONS This is the first work that attempts to design an explainable machine-learning tool for the histological diagnosis of solid masses of the ovary.
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Affiliation(s)
- Annarita Fanizzi
- Laboratorio Biostatistica e BioinformaticaI.R.C.C.S. Istituto Tumori ‘Giovanni Paolo II’BariItaly
| | - Francesca Arezzo
- Gynecologic Oncology UnitIRCCS Istituto Tumori “Giovanni Paolo II”BariItaly
- Department of Precision and Regenerative Medicine – Ionian AreaUniversity of Bari “Aldo Moro”BariItaly
| | - Gennaro Cormio
- Gynecologic Oncology UnitIRCCS Istituto Tumori “Giovanni Paolo II”BariItaly
- Interdisciplinar Department of MedicineUniversity of Bari “Aldo Moro”BariItaly
| | - Maria Colomba Comes
- Laboratorio Biostatistica e BioinformaticaI.R.C.C.S. Istituto Tumori ‘Giovanni Paolo II’BariItaly
| | - Gerardo Cazzato
- Section of Molecular Pathology, Department of Emergency and Organ TransplantationUniversity of Bari “Aldo Moro”BariItaly
| | - Luca Boldrini
- Fondazione Policlinico Universitario “A. Gemelli” IRCCSItaly
| | - Samantha Bove
- Laboratorio Biostatistica e BioinformaticaI.R.C.C.S. Istituto Tumori ‘Giovanni Paolo II’BariItaly
| | - Michele Bollino
- Department of Obstetrics and Gynecology, Division of Gynecologic oncology, Skåne University Hospital and Lund UniversityFaculty of Medicine, Clinical SciencesLundSweden
| | - Anila Kardhashi
- Gynecologic Oncology UnitIRCCS Istituto Tumori “Giovanni Paolo II”BariItaly
| | - Erica Silvestris
- Gynecologic Oncology UnitIRCCS Istituto Tumori “Giovanni Paolo II”BariItaly
| | - Pietro Quarto
- Gynecologic Oncology UnitIRCCS Istituto Tumori “Giovanni Paolo II”BariItaly
- Interdisciplinar Department of MedicineUniversity of Bari “Aldo Moro”BariItaly
| | - Michele Mongelli
- Department of Precision and Regenerative Medicine – Ionian AreaUniversity of Bari “Aldo Moro”BariItaly
| | - Emanuele Naglieri
- Medical Oncology Unit, IRCCSIstituto Tumori Giovanni Paolo IIBariItaly
| | - Rahel Signorile
- Laboratorio Biostatistica e BioinformaticaI.R.C.C.S. Istituto Tumori ‘Giovanni Paolo II’BariItaly
| | - Vera Loizzi
- Gynecologic Oncology UnitIRCCS Istituto Tumori “Giovanni Paolo II”BariItaly
- Interdisciplinar Department of MedicineUniversity of Bari “Aldo Moro”BariItaly
| | - Raffaella Massafra
- Laboratorio Biostatistica e BioinformaticaI.R.C.C.S. Istituto Tumori ‘Giovanni Paolo II’BariItaly
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Wang Z, Luo S, Chen J, Jiao Y, Cui C, Shi S, Yang Y, Zhao J, Jiang Y, Zhang Y, Xu F, Xu J, Lin Q, Dong F. Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer. iScience 2024; 27:109403. [PMID: 38523785 PMCID: PMC10959660 DOI: 10.1016/j.isci.2024.109403] [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: 10/20/2023] [Revised: 12/29/2023] [Accepted: 02/28/2024] [Indexed: 03/26/2024] Open
Abstract
We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for ovarian mass differential diagnosis. This single-center retrospective study included 1,054 ultrasound (US)-detected ovarian tumors (699 benign and 355 malignant). Patients were randomly divided into training (n = 675), validation (n = 169), and testing (n = 210) sets. The model was developed using ResNet-50. Three DL-based models were proposed for benign-malignant classification of these lesions: single-modality model that only utilized US images; dual-modality model that used US images and menopausal status as inputs; and multi-modality model that integrated US images, menopausal status, and serum indicators. After 5-fold cross-validation, 210 lesions were tested. We evaluated the three models using the area under the curve (AUC), accuracy, sensitivity, and specificity. The multimodal model outperformed the single- and dual-modality models with 93.80% accuracy and 0.983 AUC. The Multimodal ResNet-50 DL model outperformed the single- and dual-modality models in identifying benign and malignant ovarian tumors.
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Affiliation(s)
- Zimo Wang
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Shuyu Luo
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Jing Chen
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Yang Jiao
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Chen Cui
- Illuminate, LLC, 6B, Building 5, Tianyu Xiangshan Garden, No. 33, Nongxuan Road, Futian District, Donghai Community, Xiangmihu Street, Futian District, Shenzhen 518000, China
- Microport Prophecy, 1601 ZhangDong Road, ZJHi-Tech Park, Shanghai 201203, China
| | - Siyuan Shi
- Illuminate, LLC, 6B, Building 5, Tianyu Xiangshan Garden, No. 33, Nongxuan Road, Futian District, Donghai Community, Xiangmihu Street, Futian District, Shenzhen 518000, China
- Microport Prophecy, 1601 ZhangDong Road, ZJHi-Tech Park, Shanghai 201203, China
| | - Yang Yang
- Illuminate, LLC, 6B, Building 5, Tianyu Xiangshan Garden, No. 33, Nongxuan Road, Futian District, Donghai Community, Xiangmihu Street, Futian District, Shenzhen 518000, China
- Microport Prophecy, 1601 ZhangDong Road, ZJHi-Tech Park, Shanghai 201203, China
| | - Junyi Zhao
- University of Shanghai for Science and Technology, Shanghai 201203, China
| | - Yitao Jiang
- Illuminate, LLC, 6B, Building 5, Tianyu Xiangshan Garden, No. 33, Nongxuan Road, Futian District, Donghai Community, Xiangmihu Street, Futian District, Shenzhen 518000, China
- Microport Prophecy, 1601 ZhangDong Road, ZJHi-Tech Park, Shanghai 201203, China
| | - Yujuan Zhang
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Fanhua Xu
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Jinfeng Xu
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Qi Lin
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
| | - Fajin Dong
- Second Clinical College of Jinan University, Department of Ultrasound, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Medical Ultrasound Engineering Center. Shenzhen, Guangdong 518020, China
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Du Y, Guo W, Xiao Y, Chen H, Yao J, Wu J. Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study. BMC Med Imaging 2024; 24:89. [PMID: 38622546 PMCID: PMC11020982 DOI: 10.1186/s12880-024-01251-2] [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: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. METHODS We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. RESULTS The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. CONCLUSIONS The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.
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Affiliation(s)
- Yangchun Du
- Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, 530021, Nanning, China
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Wenwen Guo
- Department of Pathology, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Yanju Xiao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Haining Chen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Jinxiu Yao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Ji Wu
- Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, 530021, Nanning, China.
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Du Y, Xiao Y, Guo W, Yao J, Lan T, Li S, Wen H, Zhu W, He G, Zheng H, Chen H. Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours. Biomed Eng Online 2024; 23:41. [PMID: 38594729 PMCID: PMC11003110 DOI: 10.1186/s12938-024-01234-y] [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: 02/05/2024] [Accepted: 04/02/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). METHODS This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). RESULTS The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. CONCLUSIONS The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.
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Affiliation(s)
- Yangchun Du
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Yanju Xiao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Wenwen Guo
- Department of Pathology, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Jinxiu Yao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Tongliu Lan
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Sijin Li
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Huoyue Wen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Wenying Zhu
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Guangling He
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Hongyu Zheng
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
| | - Haining Chen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
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9
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Liu L, Cai W, Zhou C, Tian H, Wu B, Zhang J, Yue G, Hao Y. Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst. Front Med (Lausanne) 2024; 11:1362588. [PMID: 38523908 PMCID: PMC10957533 DOI: 10.3389/fmed.2024.1362588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
Background Accurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases. Methods We collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature. Results A total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963-1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967-1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model. Conclusion This ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.
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Affiliation(s)
- Lu Liu
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Wenjun Cai
- Department of Ultrasound, Shenzhen University General Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Chenyang Zhou
- Department of Information, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Hongyan Tian
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Beibei Wu
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Jing Zhang
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Guanghui Yue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Yi Hao
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
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Cai G, Huang F, Gao Y, Li X, Chi J, Xie J, Zhou L, Feng Y, Huang H, Deng T, Zhou Y, Zhang C, Luo X, Xie X, Gao Q, Zhen X, Liu J. Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study. Lancet Digit Health 2024; 6:e176-e186. [PMID: 38212232 DOI: 10.1016/s2589-7500(23)00245-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 10/26/2023] [Accepted: 11/22/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer. METHODS In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer. FINDINGS Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values. INTERPRETATION The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer. FUNDING Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Guangyao Cai
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Fangjun Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yue Gao
- Cancer Biology Research Centre (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Li
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jianhua Chi
- Cancer Biology Research Centre (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jincheng Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yanling Feng
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - He Huang
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ting Deng
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yun Zhou
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuyao Zhang
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaolin Luo
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xing Xie
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qinglei Gao
- Cancer Biology Research Centre (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
| | - Jihong Liu
- Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
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Barcroft JF, Linton-Reid K, Landolfo C, Al-Memar M, Parker N, Kyriacou C, Munaretto M, Fantauzzi M, Cooper N, Yazbek J, Bharwani N, Lee SR, Kim JH, Timmerman D, Posma J, Savelli L, Saso S, Aboagye EO, Bourne T. Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound. NPJ Precis Oncol 2024; 8:41. [PMID: 38378773 PMCID: PMC10879532 DOI: 10.1038/s41698-024-00527-8] [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: 05/23/2023] [Accepted: 01/30/2024] [Indexed: 02/22/2024] Open
Abstract
Ultrasound-based models exist to support the classification of adnexal masses but are subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses. In this retrospective study, transvaginal ultrasound scan images with linked diagnoses (ultrasound subjective assessment or histology) were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n = 577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n = 184 masses; 476 images). A segmentation and classification model was developed using convolutional neural networks and traditional radiomics features. Dice surface coefficient (DICE) was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance. The ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) years old and consisted of 23.1% and 31.5% malignant cases, respectively. The best segmentation model achieved a DICE score of 0.85 ± 0.01, 0.88 ± 0.01 and 0.85 ± 0.01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1.00 and F1-score of 0.88 (AUC:0.93), 0.94 (AUC:0.89) and 0.83 (AUC:0.90) in the ICH training, ICH validation and MPH test sets, respectively. We have developed an end-to-end radiomics-based model capable of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0.90) to the published performance of expert subjective assessment (gold standard), and current risk models. Further prospective evaluation of the classification performance of this ML model against existing methods is required.
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Affiliation(s)
- Jennifer F Barcroft
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | | | - Chiara Landolfo
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Maya Al-Memar
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Nina Parker
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Chris Kyriacou
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Maria Munaretto
- Department of Obstetrics and Gynaecology, Ospedale Morgagni-Pierantoni, Forli, Italy
| | - Martina Fantauzzi
- Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy
| | - Nina Cooper
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Joseph Yazbek
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
| | - Nishat Bharwani
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
| | - Sa Ra Lee
- Department of Obstetrics and Gynaecology, Asan Medical Center, Seoul, South Korea
| | - Ju Hee Kim
- Department of Obstetrics and Gynaecology, Asan Medical Center, Seoul, South Korea
| | - Dirk Timmerman
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Joram Posma
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Luca Savelli
- Department of Obstetrics and Gynaecology, Ospedale Morgagni-Pierantoni, Forli, Italy
| | - Srdjan Saso
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College London, London, UK.
| | - Tom Bourne
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, Imperial College Healthcare NHS Trust, London, UK
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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Zhao HN, Yin H, Liu JY, Song LL, Peng YL, Ma BY. Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients. Front Oncol 2024; 14:1204987. [PMID: 38390270 PMCID: PMC10881794 DOI: 10.3389/fonc.2024.1204987] [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: 04/13/2023] [Accepted: 01/09/2024] [Indexed: 02/24/2024] Open
Abstract
Objective This study aimed to develop a deep learning system to identify and differentiate the metastatic cervical lymph nodes (CLNs) of thyroid cancer. Methods From January 2014 to December 2020, 3059 consecutive patients with suspected with metastatic CLNs of thyroid cancer were retrospectively enrolled in this study. All CLNs were confirmed by fine needle aspiration. The patients were randomly divided into the training (1228 benign and 1284 metastatic CLNs) and test (307 benign and 240 metastatic CLNs) groups. Grayscale ultrasonic images were used to develop and test the performance of the Y-Net deep learning model. We used the Y-Net network model to segment and differentiate the lymph nodes. The Dice coefficient was used to evaluate the segmentation efficiency. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the classification efficiency. Results In the test set, the median Dice coefficient was 0.832. The sensitivity, specificity, accuracy, PPV, and NPV were 57.25%, 87.08%, 72.03%, 81.87%, and 66.67%, respectively. We also used the Y-Net classified branch to evaluate the classification efficiency of the LNs ultrasonic images. The classification branch model had sensitivity, specificity, accuracy, PPV, and NPV of 84.78%, 80.23%, 82.45%, 79.35%, and 85.61%, respectively. For the original ultrasonic reports, the sensitivity, specificity, accuracy, PPV, and NPV were 95.14%, 34.3%, 64.66%, 59.02%, 87.71%, respectively. The Y-Net model yielded better accuracy than the original ultrasonic reports. Conclusion The Y-Net model can be useful in assisting sonographers to improve the accuracy of the classification of ultrasound images of metastatic CLNs.
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Affiliation(s)
- Hai Na Zhao
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hao Yin
- Computer science of Sichuan University, Chengdu, Sichuan, China
| | - Jing Yan Liu
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Lin Lin Song
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yu Lan Peng
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
| | - Bu Yun Ma
- Department of Ultrasonography, West China hospital of Sichuan University, Chengdu, Sichuan, China
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Mitchell S, Nikolopoulos M, El-Zarka A, Al-Karawi D, Al-Zaidi S, Ghai A, Gaughran JE, Sayasneh A. Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis. Cancers (Basel) 2024; 16:422. [PMID: 38275863 PMCID: PMC10813993 DOI: 10.3390/cancers16020422] [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: 12/21/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80-0.82), and specificity was 92% (95% CI, 0.92-0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice.
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Affiliation(s)
- Sian Mitchell
- Department of Women’s Health, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London SE1 7EH, UK
| | - Manolis Nikolopoulos
- Department of Women’s Health, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London SE1 7EH, UK
| | - Alaa El-Zarka
- Department of Gynaecology, Alexandria Faculty of Medicine, Alexandria 21433, Egypt
| | | | | | - Avi Ghai
- School of Life Course Sciences, Faculty of Life Sciences and Medicine, King’s College London, Strand, London WC2R 2LS, UK
| | - Jonathan E. Gaughran
- Department of Women’s Health, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London SE1 7EH, UK
| | - Ahmad Sayasneh
- Department of Gynaecological Oncology, Surgical Oncology Directorate, Cancer Centre, Guy’s Hospital, Great Maze Pond, London SE1 9RT, UK
- School of Life Course Sciences, Faculty of Life Sciences and Medicine, St Thomas Hospital, Westminster Bridge Road, London SE1 7EH, UK
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Taddese AA, Tilahun BC, Awoke T, Atnafu A, Mamuye A, Mengiste SA. Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis. Front Oncol 2024; 13:1216326. [PMID: 38273847 PMCID: PMC10809847 DOI: 10.3389/fonc.2023.1216326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/13/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications. Methods The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model. Results The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias. Conclusion This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.
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Affiliation(s)
- Asefa Adimasu Taddese
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Binyam Chakilu Tilahun
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Tadesse Awoke
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Asmamaw Atnafu
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Adane Mamuye
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- School of Information Technology and Engineering, Addis Ababa University, Addis Ababa, Ethiopia
| | - Shegaw Anagaw Mengiste
- Department of Business, History and Social Sciences, University of Southeastern Norway, Vestfold, Vestfold, Norway
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Gupta P, Basu S, Yadav TD, Kaman L, Irrinki S, Singh H, Prakash G, Gupta P, Nada R, Dutta U, Sandhu MS, Arora C. Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound. Indian J Gastroenterol 2023:10.1007/s12664-023-01483-0. [PMID: 38110782 DOI: 10.1007/s12664-023-01483-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/05/2023] [Indexed: 12/20/2023]
Abstract
BACKGROUND The radiological differentiation of xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) is challenging yet critical. We aimed at utilizing the deep learning (DL)-based approach for differentiating XGC and GBC on ultrasound (US). METHODS This single-center study comprised consecutive patients with XGC and GBC from a prospectively acquired database who underwent pre-operative US evaluation of the gallbladder lesions. The performance of state-of-the-art (SOTA) DL models (GBCNet-convolutional neural network [CNN] and RadFormer, transformer) for XGC vs. GBC classification in US images was tested and compared with popular DL models and a radiologist. RESULTS Twenty-five patients with XGC (mean age, 57 ± 12.3, 17 females) and 55 patients with GBC (mean age, 54.6 ± 11.9, 38 females) were included. The performance of GBCNet and RadFormer was comparable (sensitivity 89.1% vs. 87.3%, p = 0.738; specificity 72% vs. 84%, p = 0.563; and AUC 0.744 vs. 0.751, p = 0.514). The AUCs of DenseNet-121, vision transformer (ViT) and data-efficient image transformer (DeiT) were significantly smaller than of GBCNet (p = 0.015, 0.046, 0.013, respectively) and RadFormer (p = 0.012, 0.027, 0.007, respectively). The radiologist labeled US images of 24 (30%) patients non-diagnostic. In the remaining patients, the sensitivity, specificity and AUC for GBC detection were 92.7%, 35.7% and 0.642, respectively. The specificity of the radiologist was significantly lower than of GBCNet and RadFormer (p = 0.001). CONCLUSION SOTA DL models have a better performance than radiologists in differentiating XGC and GBC on the US.
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Affiliation(s)
- Pankaj Gupta
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
| | - Soumen Basu
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, 110 016, India
| | - Thakur Deen Yadav
- Department of Surgical Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Lileswar Kaman
- Department of General Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Santosh Irrinki
- Department of General Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Harjeet Singh
- Department of Surgical Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Gaurav Prakash
- Department of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Parikshaa Gupta
- Department of Cytology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Ritambhra Nada
- Department of Histopathology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Usha Dutta
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Manavjit Singh Sandhu
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, 110 016, India
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Miao K, Zhao N, Lv Q, He X, Xu M, Dong X, Li D, Shao X. Prediction of benign and malignant ovarian tumors using Resnet34 on ultrasound images. J Obstet Gynaecol Res 2023; 49:2910-2917. [PMID: 37696522 DOI: 10.1111/jog.15788] [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: 05/30/2023] [Accepted: 08/24/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE To develop deep learning (DL) prediction models using transvaginal ultrasound (TVS), transabdominal ultrasound (TAS), and color Doppler flow imaging (CDFI) of TVS (CDFI_TVS) to automatically predict benign or malignant ovarian tumors. METHODS This retrospective study included women with ovarian tumors who underwent ultrasound between August 2018 and October 2022. Histopathological analysis was used as a reference standard. The dataset was preprocessed by clipping, flipping, and rotating images to generate a larger, more complicated, and diverse dataset to improve accuracy and generalizability. The dataset was then divided into training (80%) and test (20%) sets. The weights of the models, modified from the residual network (ResNet) with the TVS, TAS, and CDFI_TVS images (hereafter, referred to as DLTVS , DLTAS , and DLCDFI_TVS , respectively) were developed. The area under the receiver operating characteristic curve (AUC) analysis in the test set was used to compare the predictive value of DL for malignancy. RESULTS A total of 2340 images from 1350 women with adnexal masses were included. DLTVS had an AUC of 0.95 (95% CI: 0.93-0.97) for classifying malignant and benign ovarian tumors, comparable with that of DLTAS (AUC, 0.95; 95% CI: 0.91-0.98; p = 0.96) and DLCDFI_TVS (AUC, 0.88; 95% CI: 0.84-0.93; p = 0.02). Decision curve analysis indicated that DLTVS performed better than DLTAS and DLCDFI_TVS . CONCLUSION We developed DL models based on TVS, TAS, and CDFI_TVS on ultrasound images to predict benign and malignant ovarian tumors with high diagnostic performance. The DLTVS model had the best prediction compared with the DLTAS and DLCDFI_TVS models.
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Affiliation(s)
- Kuo Miao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ning Zhao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qian Lv
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xin He
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Mingda Xu
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoqiu Dong
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dandan Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Xiaohui Shao
- Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
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Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J Clin Med 2023; 12:6833. [PMID: 37959298 PMCID: PMC10649694 DOI: 10.3390/jcm12216833] [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: 09/21/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.
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Affiliation(s)
- Elena Jost
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Philipp Kosian
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Jorge Jimenez Cruz
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Shadi Albarqouni
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz AI, Helmholtz Munich, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Ulrich Gembruch
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Florian Recker
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
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19
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Li Y, Dong B, Yuan P. The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis. Front Oncol 2023; 13:1207175. [PMID: 37746301 PMCID: PMC10513372 DOI: 10.3389/fonc.2023.1207175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Background Malignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches. Results The inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set. Conclusion Machine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.
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Affiliation(s)
| | - Bo Dong
- Department of Orthopedics, Xi’an Honghui Hospital, Xi’an Jiaotong University, Xi’an Shaanxi, China
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Yin R, Guo Y, Wang Y, Zhang Q, Dou Z, Wang Y, Qi L, Chen Y, Zhang C, Li H, Jian X, Ma W. Predicting Neoadjuvant Chemotherapy Response and High-Grade Serous Ovarian Cancer From CT Images in Ovarian Cancer with Multitask Deep Learning: A Multicenter Study. Acad Radiol 2023; 30 Suppl 2:S192-S201. [PMID: 37336707 DOI: 10.1016/j.acra.2023.04.036] [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: 04/10/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 06/21/2023]
Abstract
RATIONALE AND OBJECTIVES Accurate prediction neoadjuvant chemotherapy (NACT) response in ovarian cancer (OC) is essential for personalized medicine. We aimed to develop and validate a deep learning (DL) model based on pretreatment contrast-enhanced CT (CECT) images for predicting NACT responses and classifying high-grade serous ovarian cancer (HGSOC) to identify patients who may benefit from NACT. MATERIALS AND METHODS This multicenter study, which contained both retrospective and prospective studies, included consecutive OC patients (n = 757) from three hospitals. Using WHO RECIST 1.1 for the reference standard, a total of 587 women with 1761 images were included in the training and validation sets, 67 women with 201 images were included in the prospective sets, and 103 women with 309 images were included in the external sets. A multitask DL model based on the multiperiod CT image was developed to predict NACT response and HGSOC. RESULTS Logistic regression analysis showed that peritoneal invasion, retinal invasion, and inguinal lymph node metastasis were independent predictors. The DL achieved promising segmentation performances with DICEmean= 0.83 (range: 0.78-0.87). For predicting NACT response, the DL model combined with clinical risk factors obtained area under the receiver operating characteristic curve (AUCs) of 0.87 (0.83-0.89), 0.88 (0.86-0.91), 0.86 (0.82-0.89), and 0.79 (0.75-0.82) in the training, validation, prospective, and external sets, respectively. The AUCs were 0.91 (0.87-0.94), 0.89 (0.86-0.91), 0.80 (0.76-0.84), and 0.80 (0.75-0.85) in four sets in HGSOC classification. CONCLUSION The multitask DL model developed using multiperiod CT images exhibited a promising performance for predicting NACT response and HGSOC with OC, which could provide valuable information for individualized treatment.
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Affiliation(s)
- Rui Yin
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China (R.Y., X.J.)
| | - Yijun Guo
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China (Y.G., Z.D., W.M.)
| | - Yanyan Wang
- Department of CT and MRI, Shanxi Tumor Hospital, Taiyuan, China (Y.W.)
| | - Qian Zhang
- Department of Radiology, Baoding No. 1 Central Hospital, Baoding, China (Q.Z.)
| | - Zhaoxiang Dou
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China (Y.G., Z.D., W.M.)
| | - Yigeng Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China (Y.W.)
| | - Lisha Qi
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China (L.Q.)
| | - Ying Chen
- Department of Gynecologic Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China (Y.C.)
| | - Chao Zhang
- Department of Bone Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China (C.Z.)
| | - Huiyang Li
- Department of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin, China (H.L.)
| | - Xiqi Jian
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China (R.Y., X.J.)
| | - Wenjuan Ma
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China (Y.G., Z.D., W.M.).
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Sukegawa S, Ono S, Tanaka F, Inoue Y, Hara T, Yoshii K, Nakano K, Takabatake K, Kawai H, Katsumitsu S, Nakai F, Nakai Y, Miyazaki R, Murakami S, Nagatsuka H, Miyake M. Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists. Sci Rep 2023; 13:11676. [PMID: 37468501 DOI: 10.1038/s41598-023-38343-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 07/06/2023] [Indexed: 07/21/2023] Open
Abstract
The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples of oral squamous cell carcinoma were prepared by oral pathologists. Images were divided into tiles on a virtual slide, and labels (squamous cell carcinoma, normal, and others) were applied. VGG16 and ResNet50 with the optimizers stochastic gradient descent with momentum and spectral angle mapper (SAM) were used, with and without a learning rate scheduler. The conditions for achieving good CNN performances were identified by examining performance metrics. We used ROCAUC to statistically evaluate diagnostic performance improvement of six oral pathologists using the results from the selected CNN model for assisted diagnosis. VGG16 with SAM showed the best performance, with accuracy = 0.8622 and AUC = 0.9602. The diagnostic performances of the oral pathologists statistically significantly improved when the diagnostic results of the deep learning model were used as supplementary diagnoses (p-value = 0.031). By considering the learning results of deep learning model classifiers, the diagnostic accuracy of pathologists can be improved. This study contributes to the application of highly reliable deep learning models for oral pathological diagnosis.
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Affiliation(s)
- Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan.
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-Machi, Takamatsu, Kagawa, 760-8557, Japan.
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan.
| | - Sawako Ono
- Department of Pathology, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-Machi, Takamatsu, Kagawa, 760-8557, Japan
| | - Futa Tanaka
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Yuta Inoue
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Takeshi Hara
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
- Center for Healthcare Information Technology, Tokai National Higher Education and Research System, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Kazumasa Yoshii
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Shimada Katsumitsu
- Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University, 1780 Hirooka-Gobara, Shiojiri, Nagano, 399-0781, Japan
| | - Fumi Nakai
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Yasuhiro Nakai
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Ryo Miyazaki
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Satoshi Murakami
- Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University, 1780 Hirooka-Gobara, Shiojiri, Nagano, 399-0781, Japan
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Minoru Miyake
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
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Zhang J, Lin H, Wang H, Xue M, Fang Y, Liu S, Huo T, Zhou H, Yang J, Xie Y, Xie M, Cheng L, Lu L, Liu P, Ye Z. Deep learning system assisted detection and localization of lumbar spondylolisthesis. Front Bioeng Biotechnol 2023; 11:1194009. [PMID: 37539438 PMCID: PMC10394621 DOI: 10.3389/fbioe.2023.1194009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 07/10/2023] [Indexed: 08/05/2023] Open
Abstract
Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors' evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis. Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals' evaluation. Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s. Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.
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Affiliation(s)
- Jiayao Zhang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Heng Lin
- Department of Orthopedics, Nanzhang People’s Hospital, Nanzhang, China
| | - Honglin Wang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mingdi Xue
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Fang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Songxiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongtong Huo
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Zhou
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaming Yang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mao Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liangli Cheng
- Department of Orthopedics, Daye People’s Hospital, Daye, China
| | - Lin Lu
- Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pengran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhewei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Pawłowska A, Rekowska A, Kuryło W, Pańczyszyn A, Kotarski J, Wertel I. Current Understanding on Why Ovarian Cancer Is Resistant to Immune Checkpoint Inhibitors. Int J Mol Sci 2023; 24:10859. [PMID: 37446039 DOI: 10.3390/ijms241310859] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/21/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
The standard treatment of ovarian cancer (OC) patients, including debulking surgery and first-line chemotherapy, is unsatisfactory because of recurrent episodes in the majority (~70%) of patients with advanced OC. Clinical trials have shown only a modest (10-15%) response of OC individuals to treatment based on immune checkpoint inhibitors (ICIs). The resistance of OC to therapy is caused by various factors, including OC heterogeneity, low density of tumor-infiltrating lymphocytes (TILs), non-cellular and cellular interactions in the tumor microenvironment (TME), as well as a network of microRNA regulating immune checkpoint pathways. Moreover, ICIs are the most efficient in tumors that are marked by high microsatellite instability and high tumor mutation burden, which is rare among OC patients. The great challenge in ICI implementation is connected with distinguishing hyper-, pseudo-, and real progression of the disease. The understanding of the immunological, molecular, and genetic mechanisms of OC resistance is crucial to selecting the group of OC individuals in whom personalized treatment would be beneficial. In this review, we summarize current knowledge about the selected factors inducing OC resistance and discuss the future directions of ICI-based immunotherapy development for OC patients.
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Affiliation(s)
- Anna Pawłowska
- Independent Laboratory of Cancer Diagnostics and Immunology, Department of Oncological Gynaecology and Gynaecology, Faculty of Medicine, Medical University of Lublin, Chodźki 1, 20-093 Lublin, Poland
| | - Anna Rekowska
- Students' Scientific Association, Independent Laboratory of Cancer Diagnostics and Immunology, Medical University of Lublin, Chodźki 1, 20-093 Lublin, Poland
| | - Weronika Kuryło
- Students' Scientific Association, Independent Laboratory of Cancer Diagnostics and Immunology, Medical University of Lublin, Chodźki 1, 20-093 Lublin, Poland
| | - Anna Pańczyszyn
- Institute of Medical Sciences, Department of Biology and Genetics, Faculty of Medicine, University of Opole, Oleska 48, 45-052 Opole, Poland
| | - Jan Kotarski
- Independent Laboratory of Cancer Diagnostics and Immunology, Department of Oncological Gynaecology and Gynaecology, Faculty of Medicine, Medical University of Lublin, Chodźki 1, 20-093 Lublin, Poland
| | - Iwona Wertel
- Independent Laboratory of Cancer Diagnostics and Immunology, Department of Oncological Gynaecology and Gynaecology, Faculty of Medicine, Medical University of Lublin, Chodźki 1, 20-093 Lublin, Poland
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Wu S, Hong G, Xu A, Zeng H, Chen X, Wang Y, Luo Y, Wu P, Liu C, Jiang N, Dang Q, Yang C, Liu B, Shen R, Chen Z, Liao C, Lin Z, Wang J, Lin T. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study. Lancet Oncol 2023; 24:360-370. [PMID: 36893772 DOI: 10.1016/s1470-2045(23)00061-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Accurate lymph node staging is important for the diagnosis and treatment of patients with bladder cancer. We aimed to develop a lymph node metastases diagnostic model (LNMDM) on whole slide images and to assess the clinical effect of an artificial intelligence-assisted (AI) workflow. METHODS In this retrospective, multicentre, diagnostic study in China, we included consecutive patients with bladder cancer who had radical cystectomy and pelvic lymph node dissection, and from whom whole slide images of lymph node sections were available, for model development. We excluded patients with non-bladder cancer and concurrent surgery, or low-quality images. Patients from two hospitals (Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China) were assigned before a cutoff date to a training set and after the date to internal validation sets for each hospital. Patients from three other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, Nanfang Hospital of Southern Medical University, and the Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong, China) were included as external validation sets. A validation subset of challenging cases from the five validation sets was used to compare performance between the LNMDM and pathologists, and two other datasets (breast cancer from the CAMELYON16 dataset and prostate cancer from the Sun Yat-sen Memorial Hospital of Sun Yat-sen University) were collected for a multi-cancer test. The primary endpoint was diagnostic sensitivity in the four prespecified groups (ie, the five validation sets, a single-lymph-node test set, the multi-cancer test set, and the subset for a performance comparison between the LNMDM and pathologists). FINDINGS Between Jan 1, 2013 and Dec 31, 2021, 1012 patients with bladder cancer had radical cystectomy and pelvic lymph node dissection and were included (8177 images and 20 954 lymph nodes). We excluded 14 patients (165 images) with concurrent non-bladder cancer and also excluded 21 low-quality images. We included 998 patients and 7991 images (881 [88%] men; 117 [12%] women; median age 64 years [IQR 56-72]; ethnicity data not available; 268 [27%] with lymph node metastases) to develop the LNMDM. The area under the curve (AUC) for accurate diagnosis of the LNMDM ranged from 0·978 (95% CI 0·960-0·996) to 0·998 (0·996-1·000) in the five validation sets. Performance comparisons between the LNMDM and pathologists showed that the diagnostic sensitivity of the model (0·983 [95% CI 0·941-0·998]) substantially exceeded that of both junior pathologists (0·906 [0·871-0·934]) and senior pathologists (0·947 [0·919-0·968]), and that AI assistance improved sensitivity for both junior (from 0·906 without AI to 0·953 with AI) and senior (from 0·947 to 0·986) pathologists. In the multi-cancer test, the LNMDM maintained an AUC of 0·943 (95% CI 0·918-0·969) in breast cancer images and 0·922 (0·884-0·960) in prostate cancer images. In 13 patients, the LNMDM detected tumour micrometastases that had been missed by pathologists who had previously classified these patients' results as negative. Receiver operating characteristic curves showed that the LNMDM would enable pathologists to exclude 80-92% of negative slides while maintaining 100% sensitivity in clinical application. INTERPRETATION We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work. FUNDING National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, and the Guangdong Provincial Clinical Research Centre for Urological Diseases.
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Affiliation(s)
- Shaoxu Wu
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, Guangdong, China
| | - Guibin Hong
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Abai Xu
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Hong Zeng
- Department of Pathology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xulin Chen
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Yun Wang
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yun Luo
- Sun Yat-sen Memorial Hospital and Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Peng Wu
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Cundong Liu
- Department of Urology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Ning Jiang
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiang Dang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Cheng Yang
- Department of Urology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Bohao Liu
- Sun Yat-sen Memorial Hospital and Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Runnan Shen
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zeshi Chen
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Chengxiao Liao
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhen Lin
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Jin Wang
- Cells Vision Medical Technology, Guangzhou, Guangdong, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Clinical Research Centre for Urological Diseases, Guangzhou, Guangdong, China.
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Koch AH, Jeelof LS, Muntinga CLP, Gootzen TA, van de Kruis NMA, Nederend J, Boers T, van der Sommen F, Piek JMJ. Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review. Insights Imaging 2023; 14:34. [PMID: 36790570 PMCID: PMC9931983 DOI: 10.1186/s13244-022-01345-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 12/05/2022] [Indexed: 02/16/2023] Open
Abstract
OBJECTIVES Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors. METHODS We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards. RESULTS In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set. CONCLUSION Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.
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Affiliation(s)
- Anna H. Koch
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Lara S. Jeelof
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Caroline L. P. Muntinga
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - T. A. Gootzen
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Nienke M. A. van de Kruis
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Joost Nederend
- grid.413532.20000 0004 0398 8384Department of Radiology, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Tim Boers
- grid.6852.90000 0004 0398 8763Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, 5600 MB Eindhoven, Noord-Brabant The Netherlands
| | - Fons van der Sommen
- grid.6852.90000 0004 0398 8763Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, 5600 MB Eindhoven, Noord-Brabant The Netherlands
| | - Jurgen M. J. Piek
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
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Tamash Y, Hammer N, Varga I, Supilnikov A, Iukhimetc S. Arterial Blood Supply of the Mesosalpinx Appears Segmentally Organized in Absence of Uterine Tubes Arteries. Physiol Res 2022. [DOI: 10.33549/physiolres.935015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Arterial branches to the uterus and ovaries that pass through the mesosalpinx contribute significantly to the maintenance of the ovarian reserve. Especially arterial supply of the uterine tube is provided by a number of anastomoses between both the uterine and ovarian vessels. Knowledge on the morphologic peculiarities will allow to identify main contributors especially blood flow ultrasound examination for the purpose of ovary preserving surgery. This study aimed at identifying landmarks especially for so-called low-flow tubal vessels. Arteries of 17 female Thiel-embalmed bodies were studied along three preselected paramedian segments and measurements taken. A section was made through the center of the ovary perpendicular to uterine tube, then the mesosalpinx tissue distance was divided into 3 equivalent zones: upper, middle and lower thirds. The surface area of the mesosalpinx averaged 1088 ± 62 mm2. 47.7 ± 7.1 % of the mesosalpinx zones included macroscopically visible vessels. The lower third segment of mesosalpinx was the thickest averaging 2.4 ± 1.5 mm. One to three tubal branches were identified in the middle third of the mesosalpinx. Arterial anastomoses were found in the upper segment of the mesosalpinx, but no presence of a marginal vessel supplying the fallopian tube could be found. Statistically significant moderate positive correlations were established between the diameters of the mesosalpingeal arteries between the three zones. The mesosalpinx, uterine tube and the ovary form areas of segmental blood supply. Variants of tubal vessels appear to be a sparse source of blood supply.
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Hsu ST, Su YJ, Hung CH, Chen MJ, Lu CH, Kuo CE. Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging. BMC Med Inform Decis Mak 2022; 22:298. [PMID: 36397100 PMCID: PMC9673368 DOI: 10.1186/s12911-022-02047-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
Background Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of distinguishing between benign and malignant ovarian tumors. Numerous types of ovarian tumors exist, many of which exhibit similar characteristics that increase the ambiguity in clinical diagnosis. Using deep learning technology, we aimed to develop a method that rapidly and accurately assists the different diagnosis of ovarian tumors in ultrasound images. Methods Based on deep learning method, we used ten well-known convolutional neural network models (e.g., Alexnet, GoogleNet, and ResNet) for training of transfer learning. To ensure method stability and robustness, we repeated the random sampling of the training and validation data ten times. The mean of the ten test results was set as the final assessment data. After the training process was completed, the three models with the highest ratio of calculation accuracy to time required for classification were used for ensemble learning pertaining. Finally, the interpretation results of the ensemble classifier were used as the final results. We also applied ensemble gradient-weighted class activation mapping (Grad-CAM) technology to visualize the decision-making results of the models. Results The highest mean accuracy, mean sensitivity, and mean specificity of ten single CNN models were 90.51 ± 4.36%, 89.77 ± 4.16%, and 92.00 ± 5.95%, respectively. The mean accuracy, mean sensitivity, and mean specificity of the ensemble classifier method were 92.15 ± 2.84%, 91.37 ± 3.60%, and 92.92 ± 4.00%, respectively. The performance of the ensemble classifier is better than that of a single classifier in three evaluation metrics. Moreover, the standard deviation is also better which means the ensemble classifier is more stable and robust. Conclusion From the comprehensive perspective of data quantity, data diversity, robustness of validation strategy, and overall accuracy, the proposed method outperformed the methods used in previous studies. In future studies, we will continue to increase the number of authenticated images and apply our proposed method in clinical settings to increase its robustness and reliability.
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Hashemi M, Hajimazdarany S, Mohan CD, Mohammadi M, Rezaei S, Olyaee Y, Goldoost Y, Ghorbani A, Mirmazloomi SR, Gholinia N, Kakavand A, Salimimoghadam S, Ertas YN, Rangappa KS, Taheriazam A, Entezari M. Long non-coding RNA/epithelial-mesenchymal transition axis in human cancers: Tumorigenesis, chemoresistance, and radioresistance. Pharmacol Res 2022; 186:106535. [DOI: 10.1016/j.phrs.2022.106535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/22/2022] [Accepted: 10/30/2022] [Indexed: 11/07/2022]
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Xu HL, Gong TT, Liu FH, Chen HY, Xiao Q, Hou Y, Huang Y, Sun HZ, Shi Y, Gao S, Lou Y, Chang Q, Zhao YH, Gao QL, Wu QJ. Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. EClinicalMedicine 2022; 53:101662. [PMID: 36147628 PMCID: PMC9486055 DOI: 10.1016/j.eclinm.2022.101662] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/25/2022] [Accepted: 08/30/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time. METHODS The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611. FINDINGS Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk). INTERPRETATION AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies. FUNDING This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).
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Key Words
- AI, Artificial intelligence
- AUC, Area Under the Curve
- Artificial intelligence
- CT, Computed Tomography
- DL, Deep learning
- ML, Machine learning
- MRI, Magnetic Resonance Imaging
- Medical imaging
- Meta-analysis
- OC, Ovarian cancer
- Ovarian cancer
- SE, Sensitivity
- SP, Specificity
- US, Ultrasound
- XAI, Explainable artificial intelligence
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Affiliation(s)
- He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Yu Chen
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ying Huang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Zan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Lou
- Department of Intelligent Medicine, China Medical University, China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qing-Lei Gao
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynecology and Obstetrics, Tongji Hospital, Wuhan, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Corresponding author at: Department of Clinical Epidemiology, Department of Obstetrics and Gynecology, Clinical Research Center, Shengjing Hospital of China Medical University, Address: No. 36, San Hao Street, Shenyang, Liaoning 110004, PR China.
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Liu P, Lu L, Chen Y, Huo T, Xue M, Wang H, Fang Y, Xie Y, Xie M, Ye Z. Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era. Front Bioeng Biotechnol 2022; 10:927926. [PMID: 36147533 PMCID: PMC9486191 DOI: 10.3389/fbioe.2022.927926] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/04/2022] [Indexed: 12/09/2022] Open
Abstract
Objective: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of femoral intertrochanteric fracture (FIF), and further compare the performance with human level to confirm the effect and feasibility of the AI algorithm.Methods: 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for the training database and 57 for the test database. A Faster-RCNN algorithm was applied to be trained and detect the FIF on X-rays. The performance of the AI algorithm such as accuracy, sensitivity, miss diagnosis rate, specificity, misdiagnosis rate, and time consumption was calculated and compared with that of orthopedic attending physicians.Results: Compared with orthopedic attending physicians, the Faster-RCNN algorithm performed better in accuracy (0.88 vs. 0.84 ± 0.04), specificity (0.87 vs. 0.71 ± 0.08), misdiagnosis rate (0.13 vs. 0.29 ± 0.08), and time consumption (5 min vs. 18.20 ± 1.92 min). As for the sensitivity and missed diagnosis rate, there was no statistical difference between the AI and orthopedic attending physicians (0.89 vs. 0.87 ± 0.03 and 0.11 vs. 0.13 ± 0.03).Conclusion: The AI diagnostic algorithm is an available and effective method for the clinical diagnosis of FIF. It could serve as a satisfying clinical assistant for orthopedic physicians.
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Affiliation(s)
- Pengran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lin Lu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yufei Chen
- Department of Orthopedics, The Second Affiliated Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Tongtong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mingdi Xue
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Honglin Wang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Fang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mao Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhewei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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31
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Gao Y, Li H, Chen L, Wu Y, Ma D, Gao Q. A deep-learning-enabled diagnosis of ovarian cancer - Authors' reply. Lancet Digit Health 2022; 4:e631. [PMID: 36028288 DOI: 10.1016/s2589-7500(22)00145-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Yue Gao
- National Clinical Research Centre for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan 430030, China; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huayi Li
- National Clinical Research Centre for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan 430030, China; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lingxi Chen
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, China
| | - Yuan Wu
- National Clinical Research Centre for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan 430030, China; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ding Ma
- National Clinical Research Centre for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan 430030, China; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qinglei Gao
- National Clinical Research Centre for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan 430030, China; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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32
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Van Calster B, Timmerman S, Geysels A, Verbakel JY, Froyman W. A deep-learning-enabled diagnosis of ovarian cancer. Lancet Digit Health 2022; 4:e630. [PMID: 36028287 DOI: 10.1016/s2589-7500(22)00130-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium; EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Stefan Timmerman
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynaecology, University Hospitals Leuven, Leuven 3000, Belgium
| | - Axel Geysels
- Department of Electrical Engineering, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium; Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Wouter Froyman
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynaecology, University Hospitals Leuven, Leuven 3000, Belgium.
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