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Meng FX, Zhang JX, Guo YR, Wang LJ, Zhang HZ, Shao WH, Xu J. Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer. Acad Radiol 2024; 31:2356-2366. [PMID: 38061942 DOI: 10.1016/j.acra.2023.11.027] [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: 10/10/2023] [Revised: 11/15/2023] [Accepted: 11/18/2023] [Indexed: 07/01/2024]
Abstract
RATIONALE AND OBJECTIVES An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection. METHODS A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis. RESULTS The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05). CONCLUSION The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.
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Affiliation(s)
- Fan-Xiu Meng
- Faculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China (F.X.M., W.H.S.); Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China (F.X.M.)
| | - Jian-Xin Zhang
- Department of Medical Imaging, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China (J.X.Z.)
| | - Ya-Rong Guo
- Department of Oncology, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (Y.R.G.)
| | - Ling-Jie Wang
- Department of CT Imaging, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (L.J.W.)
| | - He-Zhao Zhang
- Department of Hepatopancreatobiliary Surgery, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (J.X., H.Z.Z.)
| | - Wen-Hao Shao
- Faculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China (F.X.M., W.H.S.)
| | - Jun Xu
- Department of Hepatopancreatobiliary Surgery, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (J.X., H.Z.Z.).
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Gupta P, Basu S, Arora C. Applications of artificial intelligence in biliary tract cancers. Indian J Gastroenterol 2024:10.1007/s12664-024-01518-0. [PMID: 38427281 DOI: 10.1007/s12664-024-01518-0] [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: 09/03/2023] [Accepted: 12/29/2023] [Indexed: 03/02/2024]
Abstract
Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.
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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 - Delhi, New Delhi, 110 016, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology - Delhi, New Delhi, 110 016, India
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Neculoiu D, Neculoiu LC, Popa RM, Manea RM. The Many Hidden Faces of Gallbladder Carcinoma on CT and MRI Imaging-From A to Z. Diagnostics (Basel) 2024; 14:475. [PMID: 38472947 DOI: 10.3390/diagnostics14050475] [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: 12/02/2023] [Revised: 02/11/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Gallbladder carcinoma represents the most aggressive biliary tract cancer and the sixth most common gastrointestinal malignancy. The diagnosis is a challenging clinical task due to its clinical presentation, which is often non-specific, mimicking a heterogeneous group of diseases, as well as benign processes such as complicated cholecystitis, xanthogranulomatous cholecystitis, adenomyomatosis, porcelain gallbladder or metastasis to the gallbladder (most frequently derived from melanoma, renal cell carcinoma). Risk factors include gallstones, carcinogen exposure, porcelain gallbladder, typhoid carrier state, gallbladder polyps and abnormal pancreaticobiliary ductal junction. Typical imaging features on CT or MRI reveal three major patterns: asymmetric focal or diffuse wall-thickening of the gallbladder, a solid mass that replaces the gallbladder and invades the adjacent organs or as an intraluminal enhancement mass arising predominantly from the gallbladder fundus. The tumor can spread to the liver, the adjacent internal organs and lymph nodes. Depending on the disease stage, surgical resection is the curative treatment option in early stages and adjuvant combination chemotherapy at advanced stages. The purpose of this scientific paper is to fully illustrate and evaluate, through multimodality imaging findings (CT and MRI), different presentations and imaging scenarios of gallbladder cancer in six patients and thoroughly analyze the risk factors, patterns of spread and differential diagnosis regarding each particular case.
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Affiliation(s)
- Damaris Neculoiu
- Department of Radiology and Medical Imaging, Clinical Emergency County Hospital of Brașov, 500326 Brașov, Romania
| | - Lavinia Claudia Neculoiu
- Department of Radiology and Medical Imaging, Clinical Emergency County Hospital of Brașov, 500326 Brașov, Romania
| | - Ramona Mihaela Popa
- Department of Radiology and Medical Imaging, Clinical Emergency County Hospital of Brașov, 500326 Brașov, Romania
| | - Rosana Mihaela Manea
- Department of Radiology and Medical Imaging, Clinical Emergency County Hospital of Brașov, 500326 Brașov, Romania
- Faculty of Medicine, "Transilvania" University of Brașov, Nicolae Bălcescu 56, 500019 Brașov, Romania
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Zhou S, Han S, Chen W, Bai X, Pan W, Han X, He X. Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer. Abdom Radiol (NY) 2024; 49:3-10. [PMID: 37787963 DOI: 10.1007/s00261-023-04029-2] [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: 06/04/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 10/04/2023]
Abstract
OBJECTIVE Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients. MATERIALS AND METHODS A total of 152 patients diagnosed with GBC were retrospectively enrolled and divided into the serosal involvement group and no serosal involvement group according to paraffin pathology results. The regions of interest (ROIs) in the lesion on all CT images were drawn by two radiologists using ITK-SNAP software (version 3.8.0). A total of 412 features were extracted from the CT images of each patient. The Mann‒Whitney U test was applied to identify features with significant differences between groups. Seven machine learning algorithms and a deep learning model based on fully connected neural networks (f-CNNs) were used for radiomics model construction. The prediction efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS Through the Mann‒Whitney U test, 75 of the 412 features extracted from the CT images of patients were significantly different between groups (P < 0.05). Among all the algorithms, logistic regression achieved the highest performance with an area under the curve (AUC) of 0.944 (sensitivity 0.889, specificity 0.8); the f-CNN deep learning model had an AUC of 0.916, and the model showed high predictive power for serosal involvement, with a sensitivity of 0.733 and a specificity of 0.801. CONCLUSION Radiomics models based on features derived from CECT showed convincing performances in predicting serosal involvement in GBC.
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Affiliation(s)
- Shengnan Zhou
- Department of Gastrointestinal Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Shaoqi Han
- General Surgery Department, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Weijie Chen
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Xuesong Bai
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China
| | - Weidong Pan
- Radiology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, Beijing, China
| | - Xianlin Han
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
| | - Xiaodong He
- General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
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Xu C, Liu W, Zhao Q, Zhang L, Yin M, Zhou J, Zhu J, Qin S. CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy. Front Oncol 2023; 13:1287121. [PMID: 38162501 PMCID: PMC10755472 DOI: 10.3389/fonc.2023.1287121] [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: 09/01/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024] Open
Abstract
Background and purpose To establish and validate a hybrid radiomics model to predict overall survival in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). Methods We retrospectively collected 367 cervical cancer patients receiving chemoradiotherapy from the First Affiliated Hospital of Soochow University in China and divided them into a training set and a test set in a ratio of 7:3. Handcrafted and deep learning (DL)-based radiomics features were extracted from the contrast-enhanced computed tomography (CT), and the two types of radiomics signatures were calculated based on the features selected using the least absolute shrinkage and selection operator (LASSO) Cox regression. A hybrid radiomics nomogram was constructed by integrating independent clinical risk factors, handcrafted radiomics signature, and DL-based radiomics signature in the training set and was validated in the test set. Results The hybrid radiomics nomogram exhibited favorable performance in predicting overall survival, with areas under the receiver operating characteristic curve (AUCs) for 1, 3, and 5 years in the training set of 0.833, 0.777, and 0.871, respectively, and in the test set of 0.811, 0.713, and 0.730, respectively. Furthermore, the hybrid radiomics nomogram outperformed the single clinical model, handcrafted radiomics signature, and DL-based radiomics signature in both the training (C-index: 0.793) and test sets (C-index: 0.721). The calibration curves and decision curve analysis (DCA) indicated that our hybrid nomogram had good calibration and clinical benefits. Finally, our hybrid nomogram demonstrated value in stratifying patients into high- and low-risk groups (cutoff value: 5.6). Conclusion A high-performance hybrid radiomics model based on pre-radiotherapy CT was established, presenting strengths in risk stratification.
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Affiliation(s)
- Chao Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wen Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qi Zhao
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lu Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Juying Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Songbing Qin
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Rohilla KK, Kalyani CV, Gupta A, Gupta M, Matella N. Effectivity of Palliative Care Bundle on Advanced Gallbladder Cancer: A Randomised Controlled Trial. Indian J Palliat Care 2023; 29:447-455. [PMID: 38058481 PMCID: PMC10696337 DOI: 10.25259/ijpc_33_2023] [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: 02/06/2023] [Accepted: 10/17/2023] [Indexed: 12/08/2023] Open
Abstract
Objectives The main aim of this study was to develop, test, and compare palliative care bundles to improve functional recovery, resilience, and quality of life among advanced gallbladder cancer patient with their routine palliative care. Material and Methods This study was to test a palliative care bundle, a single-center, and two-arm randomised controlled trial done on a total of 116 participants (58 in each arm) from July 2019 to December 2021 at All India Institute of Medical Sciences, Rishikesh. Results By the end of 4th month, the recruitment rate was 96.7%, retention rate acceptance rate was 95%, and adherence rate was 85%. The palliative care bundle showed that a significant difference in trial outcome index score (P = 0.014*) indicates the effectiveness of the palliative care bundle related to improvement in physical mobility, resilience, and quality of life of patients and reduced caregiver burden. Reported barriers faced by participants were physical exhaustion (65%), psychological factors (25%), social factors (15%) and unfamiliar surroundings (5%). Caregivers reported barriers that their job (40%), physical fatigue related to the care of their patient (40%), their education (10%), and lack of support for their other family members (10%) were some reasons forcing them not to practice palliative care bundle. Conclusion The palliative care bundle did not interfere with the palliative treatment plan of any patients and significantly improved physical mobility, resilience, quality of life of patients, and reduced caregiver burden. Hence, a palliative care bundle can be considered in the palliative care of advanced cancer patients during their palliative treatment to provide holistic care.
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Affiliation(s)
- Kusum K. Rohilla
- College of Nursing, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
| | - C. Vasantha Kalyani
- College of Nursing, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
| | - Amit Gupta
- Department of Surgery, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
| | - Manoj Gupta
- Department of Radiation Oncology, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
| | - Nirmal Matella
- Department ofArt of Living, Rishikesh, Uttarakhand, India
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Yin Y, Yakar D, Slangen JJG, Hoogwater FJH, Kwee TC, de Haas RJ. Optimal radiological gallbladder lesion characterization by combining visual assessment with CT-based radiomics. Eur Radiol 2023; 33:2725-2734. [PMID: 36434398 DOI: 10.1007/s00330-022-09281-6] [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: 05/28/2022] [Revised: 10/30/2022] [Accepted: 11/04/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Differentiating benign gallbladder diseases from gallbladder cancer (GBC) remains a radiological challenge because they can appear very similar on imaging. This study aimed at investigating whether CT-based radiomic features of suspicious gallbladder lesions analyzed by machine learning algorithms could adequately discriminate benign gallbladder disease from GBC. In addition, the added value of machine learning models to radiological visual CT-scan interpretation was assessed. METHODS Patients were retrospectively selected based on confirmed histopathological diagnosis and available contrast-enhanced portal venous phase CT-scan. The radiomic features were extracted from the entire gallbladder, then further analyzed by machine learning classifiers based on Lasso regression, Ridge regression, and XG Boosting. The results of the best-performing classifier were combined with radiological visual CT diagnosis and then compared with radiological visual CT assessment alone. RESULTS In total, 127 patients were included: 83 patients with benign gallbladder lesions and 44 patients with GBC. Among all machine learning classifiers, XG boosting achieved the best AUC of 0.81 (95% CI 0.72-0.91) and the highest accuracy rate of 73% (95% CI 65-80%). When combining radiological visual interpretation and predictions of the XG boosting classifier, the highest diagnostic performance was achieved with an AUC of 0.98 (95% CI 0.96-1.00), a sensitivity of 91% (95% CI 86-100%), a specificity of 93% (95% CI 90-100%), and an accuracy of 92% (95% CI 90-100%). CONCLUSIONS Machine learning analysis of CT-based radiomic features shows promising results in discriminating benign from malignant gallbladder disease. Combining CT-based radiomic analysis and radiological visual interpretation provided the most optimal strategy for GBC and benign gallbladder disease differentiation. KEY POINTS Radiomic-based machine learning algorithms are able to differentiate benign gallbladder disease from gallbladder cancer. Combining machine learning algorithms with a radiological visual interpretation of gallbladder lesions at CT increases the specificity, compared to visual interpretation alone, from 73 to 93% and the accuracy from 85 to 92%. Combined use of machine learning algorithms and radiological visual assessment seems the most optimal strategy for GBC and benign gallbladder disease differentiation.
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Affiliation(s)
- Yunchao Yin
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700, RB, Groningen, The Netherlands
| | - Derya Yakar
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700, RB, Groningen, The Netherlands
| | - Jules J G Slangen
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700, RB, Groningen, The Netherlands
| | - Frederik J H Hoogwater
- Department of Surgery, Section Hepato-Pancreato-Biliary Surgery and Liver Transplantation, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700, RB, Groningen, The Netherlands
| | - Thomas C Kwee
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700, RB, Groningen, The Netherlands
| | - Robbert J de Haas
- Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700, RB, Groningen, The Netherlands.
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Yuan HX, Wang C, Tang CY, You QQ, Zhang Q, Wang WP. Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study. BMC Med Imaging 2023; 23:26. [PMID: 36747143 PMCID: PMC9901123 DOI: 10.1186/s12880-023-00982-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/01/2023] [Indexed: 02/08/2023] Open
Abstract
PURPOSE To verify whether radiomics techniques based on dual-modality ultrasound consisting of B-mode and superb microvascular imaging (SMI) can improve the accuracy of the differentiation between gallbladder neoplastic polyps and cholesterol polyps. METHODS A total of 100 patients with 100 pathologically proven gallbladder polypoid lesions were enrolled in this retrospective study. Radiomics features on B-mode ultrasound and SMI of each lesion were extracted. Support vector machine was used to classify adenomas and cholesterol polyps of gallbladder for B-mode, SMI and dual-modality ultrasound, respectively, and the classification results were compared among the three groups. RESULTS Six, eight and nine features were extracted for each lesion at B-mode ultrasound, SMI and dual-modality ultrasound, respectively. In dual-modality ultrasound model, the area under the receiver operating characteristic curve (AUC), classification accuracy, sensitivity, specificity, and Youden's index were 0.850 ± 0.090, 0.828 ± 0.097, 0.892 ± 0.144, 0.803 ± 0.149 and 0.695 ± 0.157, respectively. The AUC and Youden's index of the dual-modality model were higher than those of the B-mode model (p < 0.05). The AUC, accuracy, specificity and Youden's index of the dual-modality model were higher than those of the SMI model (p < 0.05). CONCLUSIONS Radiomics analysis of the dual-modality ultrasound composed of B-mode and SMI can improve the accuracy of classification between gallbladder neoplastic polyps and cholesterol polyps.
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Affiliation(s)
- Hai-xia Yuan
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University (Qingpu Branch), Shanghai, China ,grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Department of Ultrasound, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian Province China
| | - Changyan Wang
- grid.39436.3b0000 0001 2323 5732School of Communication and Information Engineering, Shanghai University, Shanghai, 200444 China ,grid.39436.3b0000 0001 2323 5732The SMART (Smart Medicine and AI-Based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Cong-yu Tang
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Qi-qin You
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University (Qingpu Branch), Shanghai, China
| | - Qi Zhang
- School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China. .,The SMART (Smart Medicine and AI-Based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
| | - Wen-ping Wang
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
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Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12123130. [PMID: 36553137 PMCID: PMC9776855 DOI: 10.3390/diagnostics12123130] [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: 09/27/2022] [Revised: 12/02/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2- breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2- cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann-Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2- groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.
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Huang YM, Wang TE, Chen MJ, Lin CC, Chang CW, Tai HC, Hsu SM, Chen YJ. Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy. Front Oncol 2022; 12:906498. [PMID: 36203419 PMCID: PMC9530279 DOI: 10.3389/fonc.2022.906498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/26/2022] [Indexed: 12/04/2022] Open
Abstract
Background This study aims to establish and validate a predictive model based on radiomics features, clinical features, and radiation therapy (RT) dosimetric parameters for overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with RT for portal vein tumor thrombosis (PVTT). Methods We retrospectively reviewed 131 patients. Patients were randomly divided into the training (n = 105) and validation (n = 26) cohorts. The clinical target volume was contoured on pre-RT computed tomography images and 48 textural features were extracted. The least absolute shrinkage and selection operator regression was used to determine the radiomics score (rad-score). A nomogram based on rad-score, clinical features, and dosimetric parameters was developed using the results of multivariate regression analysis. The predictive nomogram was evaluated using Harrell’s concordance index (C-index), area under the curve (AUC), and calibration curve. Results Two radiomics features were extracted to calculate the rad-score for the prediction of OS. The radiomics-based nomogram had better performance than the clinical nomogram for the prediction of OS, with a C-index of 0.73 (95% CI, 0.67–0.79) and an AUC of 0.71 (95% CI, 0.62–0.79). The predictive accuracy was assessed by a calibration curve. Conclusion The radiomics-based predictive model significantly improved OS prediction in HCC patients treated with RT for PVTT.
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Affiliation(s)
- Yu-Ming Huang
- Department of Radiation Oncology, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsang-En Wang
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Ming-Jen Chen
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Ching-Chung Lin
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Ching-Wei Chang
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
| | - Hung-Chi Tai
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan
| | - Shih-Ming Hsu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- *Correspondence: Yu-Jen Chen, ; Shih-Ming Hsu,
| | - Yu-Jen Chen
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Department of Artificial Intelligence and Medical Application, MacKay Junior College of Medicine, Nursing, and Management, New Taipei City, Taiwan
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- *Correspondence: Yu-Jen Chen, ; Shih-Ming Hsu,
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11
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Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection. Eur Radiol 2022; 32:7087-7097. [PMID: 35612664 DOI: 10.1007/s00330-022-08858-5] [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: 02/17/2022] [Revised: 04/11/2022] [Accepted: 04/30/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Gallbladder carcinoma (GBC) is the most common and aggressive biliary tract malignancy with high postoperative recurrence rates. This single-center study aimed to develop and validate a radiomics signature to estimate GBC recurrence-free survival (RFS). METHODS This study retrospectively included 204 consecutive patients with pathologically diagnosed GBC and were randomly divided into development (n = 142) and validation (n = 62) cohorts (7:3). The radiomics features of tumor were extracted from preoperative contrast-enhanced CT imaging for each patient. In the development cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression was employed to develop a radiomics signature for RFS prediction. The patients were stratified into high-score or low-score groups according to their median value of radiomics score. A nomogram was established using multivariable Cox regression by incorporating significant pathological predictors and radiomics signatures. RESULTS The radiomics signature based on 12 features could discriminate high-risk patients with poor RFS. Multivariate Cox analysis revealed that pT3/4 stage (hazard ratio, [HR] = 2.691), pN2 stage (HR = 3.60), poor differentiation grade (HR = 2.651), and high radiomics score (HR = 1.482) were independent risk variables associated with worse RFS and were incorporated to construct a nomogram. The nomogram displayed good prediction performance in estimating RFS with AUC values of 0.895, 0.935, and 0.907 at 1, 3, and 5 years, respectively. CONCLUSIONS The radiomics signature and combined nomogram may assist in predicting RFS in GBC patients. KEY POINTS • A radiomics signature extracted from preoperative contrast-enhanced CT can be a useful tool to preoperatively predict RFS of GBC. • T3/T4 stage, N2, poor tumor differentiation, and high radiomics score were positively associated with postoperative recurrence.
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12
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FDG PET/CT to Predict Recurrence of Early Breast Invasive Ductal Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12030694. [PMID: 35328247 PMCID: PMC8947709 DOI: 10.3390/diagnostics12030694] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/03/2022] [Accepted: 03/09/2022] [Indexed: 02/01/2023] Open
Abstract
This study investigated the prognostic value of FDG PET/CT radiomic features for predicting recurrence in patients with early breast invasive ductal carcinoma (IDC). The medical records of consecutive patients who were newly diagnosed with primary breast IDC after curative surgery were reviewed. Patients who received any neoadjuvant treatment before surgery were not included. FDG PET/CT radiomic features, such as a maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), skewness, kurtosis, entropy, and uniformity, were measured for the primary breast tumor using LIFEx software to evaluate recurrence-free survival (RFS). A total of 124 patients with early breast IDC were evaluated. Eleven patients had a recurrence (8.9%). Univariate survival analysis identified large tumor size (>2 cm, p = 0.045), high Ki-67 expression (≥30%, p = 0.017), high AJCC prognostic stage (≥II, p = 0.044), high SUVmax (≥5.0, p = 0.002), high MTV (≥3.25 mL, p = 0.044), high TLG (≥10.5, p = 0.004), and high entropy (≥3.15, p = 0.003) as significant predictors of poor RFS. After multivariate survival analysis, only high MTV (p = 0.045) was an independent prognostic predictor. Evaluation of the MTV of the primary tumor by FDG PET/CT in patients with early breast IDC provides useful prognostic information regarding recurrence.
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Ye L, Chen Y, Xu H, Wang Z, Li H, Qi J, Wang J, Yao J, Liu J, Song B. Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response to Bacillus Calmette-Guerin Immunotherapy in Non-Muscle-Invasive Bladder Cancer. Front Cell Dev Biol 2022; 10:814388. [PMID: 35281100 PMCID: PMC8914064 DOI: 10.3389/fcell.2022.814388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background:Bacillus Calmette-Guerin (BCG) instillation is recommended postoperatively after transurethral resection of bladder cancer (TURBT) in patients with high-risk non-muscle-invasive bladder cancer (NMIBC). An accurate prediction model for the BCG response can help identify patients with NMIBC who may benefit from alternative therapy.Objective: To investigate the value of computed tomography (CT) radiomics features in predicting the response to BCG instillation among patients with primary high-risk NMIBC.Methods: Patients with pathologically confirmed high-risk NMIBC were retrospectively reviewed. Patients who underwent contrast-enhanced CT examination within one to 2 weeks before TURBT and received ≥5 BCG instillation treatments in two independent hospitals were enrolled. Patients with a routine follow-up of at least 1 year at the outpatient department were included in the final cohort. Radiomics features based on CT images were extracted from the tumor and its periphery in the training cohort, and a radiomics signature was built with recursive feature elimination. Selected features further underwent an unsupervised radiomics analysis using the newly introduced method, non-negative matrix factorization (NMF), to compute factor factorization decompositions of the radiomics matrix. Finally, a robust component, which was most associated with BCG failure in 1 year, was selected. The performance of the selected component was assessed and tested in an external validation cohort.Results: Overall, 128 patients (training cohort, n = 104; external validation cohort, n = 24) were included, including 12 BCG failures in the training cohort and 11 failures in the validation cohort each. NMF revealed five components, of which component 3 was selected for the best discrimination of BCG failure; it had an area under the curve (AUC) of .79, sensitivity of .79, and specificity of .65 in the training set. In the external validation cohort, it achieved an AUC of .68, sensitivity of .73, and specificity of .69. Survival analysis showed that patients with higher component scores had poor recurrence-free survival (RFS) in both cohorts (C-index: training cohort, .69; validation cohort, .68).Conclusion: The study suggested that radiomics components based on NMF might be a potential biomarker to predict BCG response and RFS after BCG treatment in patients with high-risk NMIBC.
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Affiliation(s)
- Lei Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaoxiang Wang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | | | - Jin Qi
- University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Wang
- University of Electronic Science and Technology of China, Chengdu, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Jin Yao, ; Jiaming Liu,
| | - Jiaming Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Jin Yao, ; Jiaming Liu,
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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14
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Takamatsu M, Yamamoto N, Kawachi H, Nakano K, Saito S, Fukunaga Y, Takeuchi K. Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence. Sci Rep 2022; 12:2963. [PMID: 35194184 PMCID: PMC8863850 DOI: 10.1038/s41598-022-07038-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/08/2022] [Indexed: 12/13/2022] Open
Abstract
Risk evaluation of lymph node metastasis (LNM) for endoscopically resected submucosal invasive (T1) colorectal cancers (CRC) is critical for determining therapeutic strategies, but interobserver variability for histologic evaluation remains a major problem. To address this issue, we developed a machine-learning model for predicting LNM of T1 CRC without histologic assessment. A total of 783 consecutive T1 CRC cases were randomly split into 548 training and 235 validation cases. First, we trained convolutional neural networks (CNN) to extract cancer tile images from whole-slide images, then re-labeled these cancer tiles with LNM status for re-training. Statistical parameters of the tile images based on the probability of primary endpoints were assembled to predict LNM in cases with a random forest algorithm, and defined its predictive value as random forest score. We evaluated the performance of case-based prediction models for both training and validation datasets with area under the receiver operating characteristic curves (AUC). The accuracy for classifying cancer tiles was 0.980. Among cancer tiles, the accuracy for classifying tiles that were LNM-positive or LNM-negative was 0.740. The AUCs of the prediction models in the training and validation sets were 0.971 and 0.760, respectively. CNN judged the LNM probability by considering histologic tumor grade.
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Affiliation(s)
- Manabu Takamatsu
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan. .,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
| | - Noriko Yamamoto
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hiroshi Kawachi
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kaoru Nakano
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Shoichi Saito
- Department of Endoscopy, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yosuke Fukunaga
- Department of Colorectal Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kengo Takeuchi
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Ko-to-ku, Tokyo, 135-8550, Japan.,Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.,Pathology Project for Molecular Targets, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
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15
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Chato L, Latifi S. Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients. J Pers Med 2021; 11:jpm11121336. [PMID: 34945808 PMCID: PMC8705288 DOI: 10.3390/jpm11121336] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/24/2021] [Accepted: 12/07/2021] [Indexed: 01/11/2023] Open
Abstract
Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions.
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