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Luo HJ, Ren JL, Mei Guo L, Liang Niu J, Song XL. MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma. Eur J Radiol Open 2024; 13:100592. [PMID: 39149534 PMCID: PMC11324846 DOI: 10.1016/j.ejro.2024.100592] [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: 04/18/2024] [Revised: 07/08/2024] [Accepted: 07/14/2024] [Indexed: 08/17/2024] Open
Abstract
Background Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC). Objective This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC. Methods A total of 402 female patients with IDC confirmed through surgical pathology were enrolled and subsequently divided into a training group (n = 250, center I) and a validation group (n = 152, center II). Radiomics features were extracted from the preoperative MRI. A simulated annealing algorithm was used for key feature selection. Two classification tasks were performed: task 1, the classification of HER2-positive vs. HER2-negative (HER2-low and HER2-zero), and task 2, the classification of HER2-low vs. HER2-zero. Logistic regression, random forest (RF), and support vector machine were conducted to establish radiomics models. The performance of the models was evaluated using the area under the curve (AUC) of the operating characteristics (ROC). Results In total, 4506 radiomics features were extracted from multisequence MRI. A radiomics model for prediction of expression state of HER2 was successfully developed. Among the three classification algorithms, RF achieved the highest performance in classifying HER2-positive from HER2-negative and HER2-low from HER2-zero, with AUC values of 0.777 and 0.731, respectively. Conclusions Machine learning-based MRI radiomics may aid in the non-invasive prediction of the different expression status of HER2 in IDC.
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Affiliation(s)
- Hong-Jian Luo
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zuiyi, Guizhou province, China
| | | | - Li Mei Guo
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
| | - Jin Liang Niu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
| | - Xiao-Li Song
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi province, China
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Wu J, Ge L, Guo Y, Xu D, Wang Z. Utilizing multiclassifier radiomics analysis of ultrasound to predict high axillary lymph node tumour burden in node-positive breast cancer patients: a multicentre study. Ann Med 2024; 56:2395061. [PMID: 39193658 PMCID: PMC11360645 DOI: 10.1080/07853890.2024.2395061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/19/2024] [Accepted: 04/25/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis. OBJECTIVE This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer. METHODS A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model. RESULTS During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model. CONCLUSIONS The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.
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Affiliation(s)
- Jiangfeng Wu
- Department of Ultrasound, Affiliated Dongyang Hospital of Wenzhou Medical University (Dongyang People’s Hospital), Dongyang, Zhejiang, China
| | - Lifang Ge
- Department of Ultrasound, Affiliated Dongyang Hospital of Wenzhou Medical University (Dongyang People’s Hospital), Dongyang, Zhejiang, China
| | - Yinghong Guo
- Department of Ultrasound, Affiliated Dongyang Hospital of Wenzhou Medical University (Dongyang People’s Hospital), Dongyang, Zhejiang, China
| | - Dong Xu
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Zhengping Wang
- Department of Ultrasound, Affiliated Dongyang Hospital of Wenzhou Medical University (Dongyang People’s Hospital), Dongyang, Zhejiang, China
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Wang Q, Lin Y, Ding C, Guan W, Zhang X, Jia J, Zhou W, Liu Z, Bai G. Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography. Eur Radiol 2024; 34:6121-6131. [PMID: 38337068 DOI: 10.1007/s00330-024-10638-2] [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: 05/22/2023] [Revised: 12/05/2023] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVES We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. METHODS We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. RESULTS The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. CONCLUSION The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. CLINICAL RELEVANCE STATEMENT The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. KEY POINTS • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.
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Affiliation(s)
- Qian Wang
- Department of Radiology, The Affiliated Huaian Clinical College of Xuzhou Medical University, Huaian, Jiangsu, China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China
| | - Cong Ding
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Wenting Guan
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China
| | - Jianye Jia
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Wei Zhou
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Ziyan Liu
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Genji Bai
- Department of Radiology, The Affiliated Huaian Clinical College of Xuzhou Medical University, Huaian, Jiangsu, China.
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
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Wang SR, Cao CL, Du TT, Wang JL, Li J, Li WX, Chen M. Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:1611-1625. [PMID: 38808580 DOI: 10.1002/jum.16483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 04/22/2024] [Accepted: 05/05/2024] [Indexed: 05/30/2024]
Abstract
OBJECTIVE This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. METHODS The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. RESULTS Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. CONCLUSION The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.
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Affiliation(s)
- Si-Rui Wang
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Chun-Li Cao
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Ting-Ting Du
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Jin-Li Wang
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Jun Li
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Wen-Xiao Li
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
| | - Ming Chen
- The Ultrasound Diagnosis Department, The First Affiliated Hospital of Shihezi University, Xinjiang, China
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Zhang J, Sun H, Gao S, Kang Y, Shang C. Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study. BMC Cancer 2024; 24:1057. [PMID: 39192199 DOI: 10.1186/s12885-024-12844-z] [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: 01/25/2024] [Accepted: 08/22/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND Early-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS. METHODS A total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated. RESULTS During the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging-clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786-0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence. CONCLUSION The combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.
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Affiliation(s)
- Jing Zhang
- Department of Ultrasound, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China
| | - Hao Sun
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, China
| | - Song Gao
- Department of Clinical Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China
| | - Ye Kang
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China
| | - Cong Shang
- Department of Ultrasound, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China.
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Zhu M, Xu Z, Hu J, Hua L, Zou Y, Qin F, Chen C. Characteristics of regional lymph node metastasis in breast cancer and construction of a nomogram model based on ultrasonographic analysis: a retrospective study. World J Surg Oncol 2024; 22:221. [PMID: 39183267 PMCID: PMC11345964 DOI: 10.1186/s12957-024-03498-z] [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: 05/20/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVE The ultrasonographic characteristics of lymph node metastasis in breast cancer patients were retrospectively analyzed, and a predictive nomogram model was constructed to provide an imaging basis for better clinical evaluation. METHODS B-mode ultrasound was used to retrospectively analyze the imaging characteristics of regional lymph nodes and tumors. Pathological examination confirmed the presence of lymph node metastasis in breast cancer patients. Univariable and multivariable logistic regression analyses were performed to analyze the risk factors for lymph node metastasis. LASSO regression analysis was performed to screen noninvasive indicators, and a nomogram prediction model was constructed for breast cancer patients with lymph node metastasis. RESULTS A total of 187 breast cancer patients were enrolled, including 74 patients with lymph node metastasis in the positive group and 113 patients without lymph node metastasis in the negative group. Multivariate analysis revealed that pathological type (OR = 4.58, 95% CI: 1.44-14.6, p = 0.01), tumor diameter (OR = 1.37, 95% CI: 1.07-1.74, p = 0.012), spiculated margins (OR = 7.92, 95% CI: 3.03-20.67, p < 0.001), mixed echo of the breast tumor (OR = 37.09, 95% CI: 3.49-394.1, p = 0.003), and unclear lymphatic hilum structure (OR = 16.07, 95% CI: 2.41-107.02, p = 0.004) were independent risk factors for lymph node metastasis. A nomogram model was constructed for predicting breast cancer with lymph node metastasis, incorporating three significantly correlated indicators identified through LASSO regression analysis, namely, tumor spiculated margins, cortical thickness of lymph nodes, and unclear lymphatic hilum structure. The receiver operating characteristic (ROC) curve revealed that the area under the curve (AUC) was 0.717 (95% CI, 0.614-0.820) for the training set and 0.817 (95% CI, 0.738-0.890) for the validation set. The Hosmer-Lemeshow test results for the training set and the validation set were p = 0.9148 and p = 0.1648, respectively. The prediction nomogram has good diagnostic performance. CONCLUSIONS B-mode ultrasound is helpful in the preoperative assessment of breast cancer patients with lymph node metastasis. The predictive nomogram model, which is based on logistic regression and LASSO regression analysis, is clinically safe, reliable, and highly practical.
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Affiliation(s)
- Meidi Zhu
- Department of Ultrasound, Xishan People's Hospital of Wuxi City, Wuxi, 214105, China
| | - Zipeng Xu
- Department of General Surgery, Xishan People's Hospital of Wuxi City, Wuxi, 214105, China
| | - Jing Hu
- Department of Postpartum Rehabilitation Center, Xishan People's Hospital of Wuxi City, Wuxi, Jiangsu, 214105, China
| | - Lingling Hua
- Department of Ultrasound, Xishan People's Hospital of Wuxi City, Wuxi, 214105, China
| | - Yu Zou
- Department of Ultrasound, Xishan People's Hospital of Wuxi City, Wuxi, 214105, China
| | - Fei Qin
- Department of Ultrasound, Affiliated Wuxi Fifth Hospital of Jiangnan University, Wuxi, Jiangsu, 214011, China.
- Department of Ultrasound, the Fifth People's Hospital of Wuxi, Wuxi, Jiangsu, 214011, China.
| | - Chaobo Chen
- Department of General Surgery, Xishan People's Hospital of Wuxi City, Wuxi, 214105, China.
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Luo H, Chen Z, Xu H, Ren J, Zhou P. Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer. Sci Rep 2024; 14:18900. [PMID: 39143315 PMCID: PMC11324898 DOI: 10.1038/s41598-024-69725-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 08/08/2024] [Indexed: 08/16/2024] Open
Abstract
To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.
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Affiliation(s)
- Hongbing Luo
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China.
| | - Zhe Chen
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, 4th Section of South Ren-Min Road, Chengdu, 610041, China.
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Wei C, Ai H, Mo D, Wang P, Wei L, Liu Z, Li P, Huang T, Liu M. A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients. Front Endocrinol (Lausanne) 2024; 15:1388861. [PMID: 39170737 PMCID: PMC11335604 DOI: 10.3389/fendo.2024.1388861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 07/24/2024] [Indexed: 08/23/2024] Open
Abstract
Background We aim to develop a new prognostic model that incorporates inflammation, nutritional parameters and clinical-pathological features to predict overall survival (OS) and disease free survival (DFS) of breast cancer (BC) patients. Methods The study included clinicopathological and follow-up data from a total of 2857 BC patients between 2013 and 2021. Data were randomly divided into two cohorts: training (n=2001) and validation (n=856) cohorts. A nomogram was established based on the results of a multivariate Cox regression analysis from the training cohorts. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. Furthermore, decision curve analysis (DCA) was performed to assess the clinical value of the nomogram. Results A nomogram was developed for BC, incorporating lymphocyte, platelet count, hemoglobin levels, albumin-to-globulin ratio, prealbumin level and other key variables: subtype and TNM staging. In the prediction of OS and DFS, the concordance index (C-index) of the nomogram is statistically greater than the C-index values obtained using TNM staging alone. Moreover, the time-dependent AUC, exceeding the threshold of 0.7, demonstrated the nomogram's satisfactory discriminative performance over different periods. DCA revealed that the nomogram offered a greater overall net benefit than the TNM staging system. Conclusion The nomogram incorporating inflammation, nutritional and clinicopathological variables exhibited excellent discrimination. This nomogram is a promising instrument for predicting outcomes and defining personalized treatment strategies for patients with BC.
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Affiliation(s)
- Caibiao Wei
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Huaying Ai
- Department of Injection Room, The People’s Hospital of Yingtan, Yingtan, Jiangxi, China
| | - Dan Mo
- Department of Breast, Guangxi Zhuang Autonomous Region Maternal and Child Health Care Hospital, Nanning, China
| | - Peidong Wang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Liling Wei
- Department of Anesthesiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhimin Liu
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Peizhang Li
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Taijun Huang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Miaofeng Liu
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
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Tang X, Zhang H, Mao R, Zhang Y, Jiang X, Lin M, Xiong L, Chen H, Li L, Wang K, Zhou J. Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer Through Multimodal Deep Learning Based on Ultrasound and Magnetic Resonance Imaging Images. Acad Radiol 2024:S1076-6332(24)00465-3. [PMID: 39107188 DOI: 10.1016/j.acra.2024.07.029] [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: 04/15/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 08/09/2024]
Abstract
RATIONALE AND OBJECTIVES Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and complementary efficacy, in predicting axillary lymph node (ALN) metastasis. Therefore, we established a multimodal deep learning model incorporating ultrasound (US) and magnetic resonance imaging (MRI) images to predict ALN metastasis in patients with breast cancer. MATERIALS AND METHODS A retrospective cohort of patients with histologically confirmed breast cancer from two hospitals composed of the primary cohort (n = 465) and the external validation cohort (n = 123). All patients had undergone both preoperative US and MRI scans. After data preprocessing, three convolutional neural network models were used to analyze the US and MRI images, respectively. After integrating the US and MRI deep learning prediction results (DLUS and DLMRI, respectively), a multimodal deep learning (DLMRI+US+Clinical parameter) model was constructed. The predictive ability of the proposed model was compared to that of the DLUS, DLMRI, combined bimodal (DLMRI+US), and clinical parameter models. Evaluation was performed using the area under the receiver operating characteristic curves (AUCs) and decision curves. RESULTS A total of 588 patients with breast cancer participated in this study. The DLMRI+US+Clinical parameter model outperformed the alternative models, achieving the highest AUCs of 0.819 (95% confidence interval [CI] 0.734-0.903) and 0.809 (95% CI 0.723-0.895) on the internal and external validation sets, respectively. The decision curve analysis confirmed its clinical usefulness. CONCLUSION The DLMRI+US+Clinical parameter model demonstrates the feasibility and reliability of its performance for ALN metastasis prediction in patients with breast cancer.
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Affiliation(s)
- Xiaofeng Tang
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Haoyan Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Rushuang Mao
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yafang Zhang
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xinhua Jiang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Min Lin
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Lang Xiong
- Department of Medical Imaging, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
| | - Haolin Chen
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Li Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
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Jiang C, Zhang X, Qu T, Yang X, Xiu Y, Yu X, Zhang S, Qiao K, Meng H, Li X, Huang Y. The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning. Transl Oncol 2024; 46:101985. [PMID: 38805774 PMCID: PMC11154003 DOI: 10.1016/j.tranon.2024.101985] [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: 03/14/2024] [Revised: 04/19/2024] [Accepted: 04/30/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Limited studies have investigated the predictive value of multiomics signatures (radiomics, deep learning features, pathological features and DLG3) in breast cancer patients who underwent neoadjuvant chemotherapy (NAC). However, no study has explored the relationships among radiomic, pathomic signatures and chemosensitivity. This study aimed to predict pathological complete response (pCR) using multiomics signatures, and to evaluate the predictive utility of radiomic and pathomic signatures for guiding chemotherapy selection. METHODS The oncogenic function of DLG3 was explored in breast cancer cells via DLG3 knockdown. Immunohistochemistry (IHC) was used to evaluate the relationship between DLG3 expression and docetaxel/epirubin sensitivity. Machine learning (ML) and deep learning (DL) algorithms were used to develop multiomics signatures. Survival analysis was conducted by K-M curves and log-rank. Multivariate logistic regression analysis was used to develop nomograms. RESULTS A total of 311 patients with malignant breast tumours who underwent NAC were retrospectively included in this multicentre study. Multiomics (DLG3, RADL and PATHO) signatures could accurately predict pCR (AUC: training: 0.900; testing: 0.814; external validation: 0.792). Its performance is also superior to that of clinical TNM staging and the single RADL signature in different cohorts. Patients in the low DLG3 group more easily achieved pCR, and those in the high RADL Signature_pCR and PATHO_Signature_pCR (OR = 7.93, 95 % CI: 3.49-18, P < 0.001) groups more easily achieved pCR. In the TEC regimen NAC group, patients who achieved pCR had a lower DLG3 score (4.00 ± 2.33 vs. 6.43 ± 3.01, P < 0.05). Patients in the low RADL_Signature_DLG3 and PATHO_Signature_DLG3 groups had lower DLG3 IHC scores (P < 0.05). Patients in the high RADL signature, PATHO signature and DLG3 signature groups had worse DFS and OS. CONCLUSIONS Multiomics signatures (RADL, PATHO and DLG3) demonstrated great potential in predicting the pCR of breast cancer patients who underwent NAC. The RADL and PATHO signatures are associated with DLG3 status and could help doctors or patients choose proper neoadjuvant chemotherapy regimens (TEC regimens). This simple, structured, convenient and inexpensive multiomics model could help clinicians and patients make treatment decisions.
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Affiliation(s)
- Cong Jiang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150086, China
| | - XueFang Zhang
- Department of Pathology, The first people's hospital of Xiangtan City, Xiangtan 411100, China
| | - Tong Qu
- Department of Oncology, The second cancer hospital of Heilongjiang province, Harbin 150086, China
| | - Xinxin Yang
- Department of Pathology, Harbin Medical University Cancer Hospital, Harbin 150086, China
| | - Yuting Xiu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150086, China
| | - Xiao Yu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150086, China
| | - Shiyuan Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150086, China
| | - Kun Qiao
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150086, China
| | - Hongxue Meng
- Department of Pathology, Harbin Medical University Cancer Hospital, Harbin 150086, China
| | - Xuelian Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150086, China; State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Department of Pharmacology (State Key Labratoray -Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, China
| | - Yuanxi Huang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150086, China.
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Hu W, Wen F, Zhao M, Li X, Luo P, Jiang G, Yang H, Herth FJF, Zhang X, Zhang Q. Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiating between Benign and Malignant Mediastinal and Hilar Lymph Nodes. Respiration 2024:1-11. [PMID: 39038439 DOI: 10.1159/000540467] [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: 03/14/2024] [Accepted: 07/10/2024] [Indexed: 07/24/2024] Open
Abstract
INTRODUCTION The aim of the study was to establish an ultrasonographic radiomics machine learning model based on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malignant mediastinal and hilar lymph nodes (LNs). METHODS The clinical and ultrasonographic image data of 197 patients were retrospectively analyzed. The radiomics features extracted by EBUS-based radiomics were analyzed by the least absolute shrinkage and selection operator. Then, we used a support vector machine (SVM) algorithm to establish an EBUS-based radiomics model. A total of 205 lesions were randomly divided into training (n = 143) and validation (n = 62) groups. The diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS A total of 13 stable radiomics features with non-zero coefficients were selected. The SVM model exhibited promising performance in both groups. In the training group, the SVM model achieved an ROC area under the curve (AUC) of 0.892 (95% CI: 0.885-0.899), with an accuracy of 85.3%, sensitivity of 93.2%, and specificity of 79.8%. In the validation group, the SVM model had an ROC AUC of 0.906 (95% CI: 0.890-0.923), an accuracy of 74.2%, a sensitivity of 70.3%, and a specificity of 74.1%. CONCLUSION The EBUS-based radiomics model can be used to differentiate mediastinal and hilar benign and malignant LNs. The SVM model demonstrated excellent potential as a diagnostic tool in clinical practice.
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Affiliation(s)
- Wenjia Hu
- Department of Ultrasound, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Feifei Wen
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China,
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China,
| | - Mengyu Zhao
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xiangnan Li
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
| | - Peiyuan Luo
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
| | - Guancheng Jiang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
| | - Huizhen Yang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
| | - Felix J F Herth
- Department of Pneumology and Respiratory Care Medicine, Thoraxklinik and Translational Lung Research Center, University of Heidelberg, Heidelberg, Germany
| | - Xiaoju Zhang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
- Henan International Joint Laboratory of Diagnosis and Treatment for Pulmonary Nodules, Zhengzhou, China
| | - Quncheng Zhang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China
- Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China
- Henan International Joint Laboratory of Diagnosis and Treatment for Pulmonary Nodules, Zhengzhou, China
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Bai C, Sun Y, Zhang X, Zuo Z. Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature. Heliyon 2024; 10:e33107. [PMID: 39022022 PMCID: PMC11253280 DOI: 10.1016/j.heliyon.2024.e33107] [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: 01/21/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 07/20/2024] Open
Abstract
Objective This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD). Methods A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival. Results High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022). Conclusion Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.
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Affiliation(s)
- Cuiqing Bai
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yan Sun
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiuqin Zhang
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhitong Zuo
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
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Qian F, Shen H, Deng C, Liu C, Su T, Chen A, Hu D, Zhu J. Establishment of a logistic regression model nomogram for clinicopathological characteristics and risk factors with axillary lymph node metastasis in T1 locally advanced breast cancer: a retrospective study. Gland Surg 2024; 13:871-884. [PMID: 39015720 PMCID: PMC11247567 DOI: 10.21037/gs-24-34] [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: 01/25/2024] [Accepted: 05/29/2024] [Indexed: 07/18/2024]
Abstract
Background Although the research reports on locally advanced breast cancer (LABC) are increasing year by year, there are few reports on T1 LABC axillary lymph node metastasis (ALNM). By establishing a prediction model for T1 LABC ALNM, this study provides a reference value for the probability of ALNM of related patients, which helps clinicians to develop a more effective and individualized treatment plan for LABC. Methods Cases with pathologically confirmed T1 breast cancer (BC) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were identified. Logistic regression was used to analyze the correlation between LABC lymph node metastasis and every factor, and the odds ratio (OR) and 95% confidence interval (CI) were used to identify any influencing factors. A nomogram was drawn after incorporating meaningful factors identified in multivariate logistic regression into the model. The receiver operating characteristic (ROC) curve of the model was drawn, and the area under the curve (AUC) and its 95% CI were calculated. Hosmer-Lemeshow goodness-of-fit test and clinical decision curve analysis (DCA) were performed. The results were validated in the validation group. Results A total of 200,933 female T1 BC patients were included in this study. Univariate and multivariate logistic regression analysis of T1 BC showed that progesterone receptor (PR)-negative, race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and larger tumor diameter increased the probability of ALNM in T1 LABC. A predictive nomogram was established using the above predictors, the AUC of the modeling group was 0.739 (95% CI: 0.732-0.747), and when the AUC cut-off value was 0.026, the specificity and sensitivity of the model were 65.78% and 69.99%, respectively. Validation of the model showed that the AUC of the validation group (n=60,280) was 0.741. When all the risk factors were met, the predicted probability of N2-N3 was 50.40%. Conclusions In this study, it was found that PR-negative, Black race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and tumor diameter increased the probability of large lymph node metastasis in T1 LABC small tumors.
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Affiliation(s)
- Fang Qian
- Postgraduate Training Base of the Xiaogan Central Hospital of Jinzhou Medical University, Xiaogan, China
| | - Haoyuan Shen
- Department of Thyroid Gland Breast Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology (Xiaogan Central Hospital), Xiaogan, China
| | - Chunyan Deng
- Department of Pediatrics, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology (Xiaogan Central Hospital), Xiaogan, China
| | - Chenghao Liu
- Department of Thyroid Gland Breast Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology (Xiaogan Central Hospital), Xiaogan, China
| | - Tingting Su
- Department of Thyroid Gland Breast Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology (Xiaogan Central Hospital), Xiaogan, China
| | - Anli Chen
- Postgraduate Training Base of the Xiaogan Central Hospital of Jinzhou Medical University, Xiaogan, China
| | - Di Hu
- Department of Thyroid Gland Breast Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology (Xiaogan Central Hospital), Xiaogan, China
| | - Jiacheng Zhu
- Department of Thyroid Gland Breast Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology (Xiaogan Central Hospital), Xiaogan, China
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Azeroual S, Ben-Bouazza FE, Naqi A, Sebihi R. Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study. J Egypt Natl Canc Inst 2024; 36:20. [PMID: 38853190 DOI: 10.1186/s43046-024-00222-6] [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: 01/26/2024] [Accepted: 04/06/2024] [Indexed: 06/11/2024] Open
Abstract
BACKGROUND The goal is to use three different machine learning models to predict the recurrence of breast cancer across a very heterogeneous sample of patients with varying disease kinds and stages. METHODS A heterogeneous group of patients with varying cancer kinds and stages, including both triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC), was examined. Three distinct models were created using the following five machine learning techniques: Adaptive Boosting (AdaBoost), Random Under-sampling Boosting (RUSBoost), Extreme Gradient Boosting (XGBoost), support vector machines (SVM), and Logistic Regression. The clinical model used both clinical and pathology data in conjunction with the machine learning algorithms. The machine learning algorithms were combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) imaging characteristics in the radiomic model, and the merged model combined the two types of data. Each technique was evaluated using several criteria, including the receiver operating characteristic (ROC) curve, precision, recall, and F1 score. RESULTS The results suggest that the integration of clinical and radiomic data improves the predictive accuracy in identifying instances of breast cancer recurrence. The XGBoost algorithm is widely recognized as the most effective algorithm in terms of performance. CONCLUSION The findings presented in this study offer significant contributions to the field of breast cancer research, particularly in relation to the prediction of cancer recurrence. These insights hold great potential for informing future investigations and clinical interventions that seek to enhance the accuracy and effectiveness of recurrence prediction in breast cancer patients.
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Affiliation(s)
- Saadia Azeroual
- LPHE-Modeling and Simulations, Faculty of Science, Mohammed V University in Rabat, Rabat, Morocco.
| | - Fatima-Ezzahraa Ben-Bouazza
- Faculty of Sciences and Technology, Hassan First University, Settat, Morocco
- LaMSN (La Maison Des Sciences Num´Eriques), Saint-Denis, France
| | - Amine Naqi
- Mohammed VI University of Sciences and Health, Casablanca, Morocco
| | - Rajaa Sebihi
- LPHE-Modeling and Simulations, Faculty of Science, Mohammed V University in Rabat, Rabat, Morocco
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Ma X, Zhang L, Xiao Q, Huang Y, Lin L, Peng W, Gong J, Gu Y. Predicting Prognosis of Phyllodes Tumors Using a Mammography- and Magnetic Resonance Imaging-Based Radiomics Model: A Preliminary Study. Clin Breast Cancer 2024:S1526-8209(24)00119-8. [PMID: 38839461 DOI: 10.1016/j.clbc.2024.05.006] [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: 11/30/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE To investigate whether a radiomics model based on mammography (MG) and magnetic resonance imaging (MRI) can be used to predict disease-free survival (DFS) after phyllodes tumor (PT) surgery. METHOD About 131 PT patients who underwent MG and MRI before surgery between January 2010 and December 2020 were retrospectively enrolled, including 15 patients with recurrence and metastasis and 116 without recurrence. 884 and 3138 radiomic features were extracted from MG and MR images, respectively. Then, multiple radiomics models were established to predict the recurrence risk of the patients by applying a support vector machine classifier. The area under the ROC curve (AUC) was calculated to evaluate model performance. After dividing the patients into high- and low-risk groups based on the predicted radiomics scores, survival analysis was conducted to compare differences between the groups. RESULTS In total, 3 MG-related and 5 MRI-related radiomic models were established; the prediction performance of the T1WI feature fusion model was the best, with an AUC value of 0.93. After combining the features of MG and MRI, the AUC increased to 0.95. Furthermore, the MG, MRI and all-image radiomic models had statistically significant differences in survival between the high- and low-risk groups (P < .001). All-image radiomics model showed higher survival performance than the MG and MRI radiomics models alone. CONCLUSIONS Radiomics features based on preoperative MG and MR images can predict DFS after PT surgery, and the prediction score of the image radiomics model can be used as a potential indicator of recurrence risk.
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Affiliation(s)
- Xiaowen Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Li Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qin Xiao
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Huang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Luyi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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James J, Law M, Sengupta S, Saunders C. Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review. World J Surg Oncol 2024; 22:127. [PMID: 38725006 PMCID: PMC11084006 DOI: 10.1186/s12957-024-03394-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/28/2024] [Indexed: 05/12/2024] Open
Abstract
Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.
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Affiliation(s)
- Justin James
- Eastern Health, Melbourne, Australia.
- Monash University, Melbourne, Australia.
- Department of Breast and Endocrine Surgery, Maroondah Hospital, Davey Drive, Ringwood East, Melbourne, VIC, 3135, Australia.
| | - Michael Law
- Eastern Health, Melbourne, Australia
- Monash University, Melbourne, Australia
| | - Shomik Sengupta
- Eastern Health, Melbourne, Australia
- Monash University, Melbourne, Australia
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Yu Y, Jia X, Chen S, Lai Z, Deng H, Mo Y, Xie X, Wang Z, Lin R, Ouyang W, Yao H, Wu J. Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy. Front Genet 2024; 15:1332935. [PMID: 38756447 PMCID: PMC11097162 DOI: 10.3389/fgene.2024.1332935] [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: 11/03/2023] [Accepted: 04/08/2024] [Indexed: 05/18/2024] Open
Abstract
Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.
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Affiliation(s)
- Yunfang Yu
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Phase I Clinical Trial Cent, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xueyuan Jia
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao, China
| | - Sunyu Chen
- School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Zijia Lai
- School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Heran Deng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Phase I Clinical Trial Cent, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuqian Mo
- School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xinxin Xie
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Phase I Clinical Trial Cent, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zehua Wang
- Division of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Hong Kong Baptist University, Zhuhai, China
| | - Ruichong Lin
- School of Computer Engineering, Guangzhou Huali College, Guangzhou, China
- Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao, China
| | - Wenhao Ouyang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Phase I Clinical Trial Cent, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Herui Yao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Phase I Clinical Trial Cent, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiannan Wu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Phase I Clinical Trial Cent, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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Polat DS, Nguyen S, Karbasi P, Hulsey K, Cobanoglu MC, Wang L, Montillo A, Dogan BE. Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network. Radiol Imaging Cancer 2024; 6:e230107. [PMID: 38607282 PMCID: PMC11148663 DOI: 10.1148/rycan.230107] [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: 07/18/2023] [Revised: 12/31/2023] [Accepted: 02/29/2024] [Indexed: 04/13/2024]
Abstract
Purpose To develop a custom deep convolutional neural network (CNN) for noninvasive prediction of breast cancer nodal metastasis. Materials and Methods This retrospective study included patients with newly diagnosed primary invasive breast cancer with known pathologic (pN) and clinical nodal (cN) status who underwent dynamic contrast-enhanced (DCE) breast MRI at the authors' institution between July 2013 and July 2016. Clinicopathologic data (age, estrogen receptor and human epidermal growth factor 2 status, Ki-67 index, and tumor grade) and cN and pN status were collected. A four-dimensional (4D) CNN model integrating temporal information from dynamic image sets was developed. The convolutional layers learned prognostic image features, which were combined with clinicopathologic measures to predict cN0 versus cN+ and pN0 versus pN+ disease. Performance was assessed with the area under the receiver operating characteristic curve (AUC), with fivefold nested cross-validation. Results Data from 350 female patients (mean age, 51.7 years ± 11.9 [SD]) were analyzed. AUC, sensitivity, and specificity values of the 4D hybrid model were 0.87 (95% CI: 0.83, 0.91), 89% (95% CI: 79%, 93%), and 76% (95% CI: 68%, 88%) for differentiating pN0 versus pN+ and 0.79 (95% CI: 0.76, 0.82), 80% (95% CI: 77%, 84%), and 62% (95% CI: 58%, 67%), respectively, for differentiating cN0 versus cN+. Conclusion The proposed deep learning model using tumor DCE MR images demonstrated high sensitivity in identifying breast cancer lymph node metastasis and shows promise for potential use as a clinical decision support tool. Keywords: MR Imaging, Breast, Breast Cancer, Breast MRI, Machine Learning, Metastasis, Prognostic Prediction Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Dogan S. Polat
- From the Department of Diagnostic Radiology (D.S.P., K.H., A.M.,
B.E.D.), Lyda Hill Department of Bioinformatics (S.N., P.K., M.C.C., L.W.,
A.M.), and Biomedical Engineering Department (A.M.), University of Texas
Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
| | - Son Nguyen
- From the Department of Diagnostic Radiology (D.S.P., K.H., A.M.,
B.E.D.), Lyda Hill Department of Bioinformatics (S.N., P.K., M.C.C., L.W.,
A.M.), and Biomedical Engineering Department (A.M.), University of Texas
Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
| | | | - Keith Hulsey
- From the Department of Diagnostic Radiology (D.S.P., K.H., A.M.,
B.E.D.), Lyda Hill Department of Bioinformatics (S.N., P.K., M.C.C., L.W.,
A.M.), and Biomedical Engineering Department (A.M.), University of Texas
Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
| | | | - Liqiang Wang
- From the Department of Diagnostic Radiology (D.S.P., K.H., A.M.,
B.E.D.), Lyda Hill Department of Bioinformatics (S.N., P.K., M.C.C., L.W.,
A.M.), and Biomedical Engineering Department (A.M.), University of Texas
Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
| | - Albert Montillo
- From the Department of Diagnostic Radiology (D.S.P., K.H., A.M.,
B.E.D.), Lyda Hill Department of Bioinformatics (S.N., P.K., M.C.C., L.W.,
A.M.), and Biomedical Engineering Department (A.M.), University of Texas
Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
| | - Basak E. Dogan
- From the Department of Diagnostic Radiology (D.S.P., K.H., A.M.,
B.E.D.), Lyda Hill Department of Bioinformatics (S.N., P.K., M.C.C., L.W.,
A.M.), and Biomedical Engineering Department (A.M.), University of Texas
Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-8585
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Wei Z, Xv Y, Liu H, Li Y, Yin S, Xie Y, Chen Y, Lv F, Jiang Q, Li F, Xiao M. A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study. Int J Surg 2024; 110:2922-2932. [PMID: 38349205 PMCID: PMC11093481 DOI: 10.1097/js9.0000000000001194] [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: 11/28/2023] [Accepted: 01/31/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. METHODS MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. RESULTS A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. CONCLUSIONS A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.
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Affiliation(s)
| | | | | | | | - Siwen Yin
- Department of Urology, Chongqing University Fuling Hospital
| | | | - Yong Chen
- Department of Urology, Chongqing University Fuling Hospital
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University
| | - Feng Li
- Department of Urology, Chongqing University Three Gorges Hospital, Chongqing, People’s Republic of China
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Xue T, Wan X, Zhou T, Zou Q, Ma C, Chen J. Potential value of CT-based comprehensive nomogram in predicting occult lymph node metastasis of esophageal squamous cell paralaryngeal nerves: a two-center study. J Transl Med 2024; 22:399. [PMID: 38689366 PMCID: PMC11059581 DOI: 10.1186/s12967-024-05217-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
PURPOSE The aim of this study is to construct a combined model that integrates radiomics, clinical risk factors and machine learning algorithms to predict para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma. METHODS A retrospective study included 361 patients with esophageal squamous cell carcinoma from 2 centers. Radiomics features were extracted from the computed tomography scans. Logistic regression, k nearest neighbor, multilayer perceptron, light Gradient Boosting Machine, support vector machine, random forest algorithms were used to construct radiomics models. The receiver operating characteristic curve and The Hosmer-Lemeshow test were employed to select the better-performing model. Clinical risk factors were identified through univariate logistic regression analysis and multivariate logistic regression analysis and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis. RESULTS A total of 1024 radiomics features were extracted. Among the radiomics models, the KNN model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.84 in the training cohort and 0.62 in the internal test cohort. Furthermore, the combined model exhibited an AUC of 0.97 in the training cohort and 0.86 in the internal test cohort. CONCLUSION A clinical-radiomics integrated nomogram can predict occult para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma and provide guidance for personalized treatment.
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Affiliation(s)
- Ting Xue
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, China.
| | - Xinyi Wan
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Taohu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Qin Zou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Chao Ma
- Department of Radiology, Frist Affiliated Hospital of Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, China
| | - Jieqiong Chen
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, China
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Ling X, Alexander GS, Molitoris J, Choi J, Schumaker L, Tran P, Mehra R, Gaykalova D, Ren L. Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans. Front Oncol 2024; 14:1380599. [PMID: 38715772 PMCID: PMC11074368 DOI: 10.3389/fonc.2024.1380599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/04/2024] [Indexed: 05/15/2024] Open
Abstract
Introduction This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Methods Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers. Results and discussion Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.
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Affiliation(s)
- Xiao Ling
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Gregory S. Alexander
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jason Molitoris
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Jinhyuk Choi
- Department of Breast Surgery, Kosin University Gospel Hospital, Busan, Republic of Korea
| | - Lisa Schumaker
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Phuoc Tran
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Ranee Mehra
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Daria Gaykalova
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Otorhinolaryngology-Head and Neck Surgery, Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland Medical Center, Baltimore, MD, United States
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
| | - Lei Ren
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
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Yuan S, Liu Y, Wei R, Zhu J, Men K, Dai J. A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis. Med Phys 2024; 51:2695-2706. [PMID: 38043105 DOI: 10.1002/mp.16850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/03/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention. PURPOSE This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel-wise consistency for deep learning-based MRI-to-CT synthesis. The method was expected to assist the multi-modality studies for radiomics. METHODS The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre-procession. We proposed a gray-level co-occurrence matrix (GLCM)-based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning-based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function. RESULTS Compared with the baseline, the proposed method improved the pixel-wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t-test). Generally, > 90% (22/24) of the GLCM-based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs. CONCLUSIONS The proposed method reproduced texture features for MRI-to-CT synthesis, which would benefit radiomics studies based on image multi-modality synthesis.
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Affiliation(s)
- Siqi Yuan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ran Wei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li Z, Wang F, Zhang H, Xie S, Peng L, Xu H, Wang Y. A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108052. [PMID: 38447320 DOI: 10.1016/j.ejso.2024.108052] [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: 10/28/2023] [Revised: 01/24/2024] [Accepted: 02/21/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans. METHODS Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set. RESULTS Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749. CONCLUSION Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.
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Affiliation(s)
- Zhiyang Li
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Fuqiang Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China; West China School of Medicine, West China Hospital, Sichuan University, China
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Shenglong Xie
- Department of Thoracic Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Peng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, China.
| | - Yun Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, China.
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Xu YH, Lu P, Gao MC, Wang R, Li YY, Guo RQ, Zhang WS, Song JX. Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer. World J Clin Oncol 2024; 15:419-433. [PMID: 38576593 PMCID: PMC10989267 DOI: 10.5306/wjco.v15.i3.419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/15/2024] [Accepted: 02/06/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis. AIM To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients. METHODS A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model. RESULTS The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice. CONCLUSION This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.
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Affiliation(s)
- Yan-Han Xu
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Peng Lu
- Department of Imaging, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Ming-Cheng Gao
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rui Wang
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Yang-Yang Li
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rong-Qi Guo
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Wei-Song Zhang
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Jian-Xiang Song
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
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Wang Y, Shang Y, Guo Y, Hai M, Gao Y, Wu Q, Li S, Liao J, Sun X, Wu Y, Wang M, Tan H. Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer. Front Oncol 2024; 14:1357145. [PMID: 38567148 PMCID: PMC10985134 DOI: 10.3389/fonc.2024.1357145] [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: 12/17/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. Methods A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). Results A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. Conclusion The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.
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Affiliation(s)
- Yunxia Wang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yiyan Shang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yaxin Guo
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Menglu Hai
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University &Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Yang Gao
- Heart Center, People’s Hospital of Zhengzhou University & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging & United Imaging Intelligence Co., Ltd., Beijing, China
| | - Shunian Li
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jun Liao
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaojuan Sun
- School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yaping Wu
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hongna Tan
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Liu J, Leng X, Liu W, Ma Y, Qiu L, Zumureti T, Zhang H, Mila Y. An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer. Front Oncol 2024; 14:1285511. [PMID: 38500656 PMCID: PMC10946249 DOI: 10.3389/fonc.2024.1285511] [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: 09/19/2023] [Accepted: 02/20/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features. Methods Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve. Results We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value. Discussion The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.
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Affiliation(s)
- Jinhui Liu
- Department of Ultrasound, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, China
| | - Xiaoling Leng
- Department of Ultrasound, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, China
| | - Wen Liu
- Artificial Intelligence and Smart Mine Engineering Technology Center, Xinjiang Institute of Engineering, Urumqi, China
| | - Yuexin Ma
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Lin Qiu
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Tuerhong Zumureti
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Haijian Zhang
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yeerlan Mila
- Department of Ultrasound, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
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Yao J, Zhou W, Zhu Y, Zhou J, Chen X, Zhan W. Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer. Oncol Lett 2024; 27:95. [PMID: 38288042 PMCID: PMC10823315 DOI: 10.3892/ol.2024.14228] [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: 08/10/2023] [Accepted: 12/19/2023] [Indexed: 01/31/2024] Open
Abstract
Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.
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Affiliation(s)
- Jiejie Yao
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Wei Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Ying Zhu
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Xiaosong Chen
- Comprehensive Breast Health Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
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Liu J, Lin W, Yan L, Xie J, Dai J, Xu D, Zhao J. Contrast CT radiomic features add value to prediction of prognosis in adrenal cortical carcinoma. Endocrine 2024; 83:763-774. [PMID: 37968537 DOI: 10.1007/s12020-023-03568-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 10/09/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy with poor prognosis due to high postoperative recurrence rates. The aim of this study is to develop a contrast CT radiomic feature-based prognosis prediction model for ACC and evaluate its performance by comparison with ENSAT staging system and S-GRAS score. METHODS Included in this study were 39 ACC patients, from which we extracted 1411 radiomic features. Using cross-validated least absolute shrinkage and selection operator regression (cv-LASSO regression), we generated a radiomic index. Additionally, we further validated the radiomic index using both univariate and multivariate Cox regression analyses. We constructed a radiomic nomogram that incorporated the radiomic signature and compared it with ENSAT stage and S-GRAS score in terms of calibration, discrimination and clinical usefulnes. RESULTS In this study, the average progression free survival (PFS) of 39 patients was 20.4 (IQR 9.1-60.1) months and the average overall survival (OS) was 57.8 (IQR 32.4-NA). The generated radiomic features were significantly associated with PFS, OS, independent of clinical-pathologic risk factors (HR 0.16, 95%CI 0.02-0.99, p = 0.05; HR 0.20, 95%CI 0.04-1.07, p = 0.06, respectively). The radiomic index, ENSAT stage, resection status, and Ki67% index incorporated nomogram exhibited better performance for both PFS and OS prediction as compared with the S-GRAS and ENSAT nomogram (C-index: 0.75 vs. C-index: 0.68, p = 0.030 and 0.67, p = 0.025; C-index: 0.78 vs. C-index: 0.72, p = 0.003 and 0.73, p = 0.006). Calibration curve analysis showed that the radiomics-based model performs best in predicting the two-year PFS and the three-year OS. Decision curve analysis demonstrated that the radiomic index nomogram outperformed the S-GRAS and ENSAT nomogram in predicting the two-year PFS and the three-year OS. CONCLUSION The contrast CT radiomic-based nomogram performed better than S-GRAS or ENSAT in predicting PFS and OS in ACC patients.
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Affiliation(s)
- Jiacheng Liu
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenhao Lin
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ling Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jialing Xie
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Dai
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Danfeng Xu
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Juping Zhao
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Li H, Liu R, Long C, Teng Y, Liu Y. A novel machine learning prediction model for metastasis in breast cancer. Cancer Rep (Hoboken) 2024; 7:e2006. [PMID: 38425238 PMCID: PMC10905156 DOI: 10.1002/cnr2.2006] [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: 10/13/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Breast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models. AIM We developed a model to predict BC metastasis using the random survival forest (RSF) method. METHODS Based on demographic data and routine clinical data, we used RSF-recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan-Meier survival (KM) analyses were plotted to validate the predictive effect when C-index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model. RESULTS We developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF-recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C-indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model. CONCLUSIONS This model relies on routine data and examination indicators in real-time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.
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Affiliation(s)
- Huan Li
- Department of Thyroid and Breast SurgeryThird Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Ren‐Bin Liu
- Department of Thyroid and Breast SurgeryThird Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Chen‐meng Long
- Department of Breast SurgeryLiuzhou Women and Children's Medical CenterLiuzhouChina
| | - Yuan Teng
- Department of Breast SurgeryGuangzhou Women and Children's Medical CenterGuangzhouChina
| | - Yu Liu
- Department of Thyroid and Breast SurgeryThird Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
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Geng X, Zhang Y, Li Y, Cai Y, Liu J, Geng T, Meng X, Hao F. Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma. Br J Radiol 2024; 97:652-659. [PMID: 38268475 PMCID: PMC11027331 DOI: 10.1093/bjr/tqae009] [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: 07/14/2023] [Revised: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVES This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted. RESULTS The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization. CONCLUSION A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage. ADVANCES IN KNOWLEDGE This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.
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Affiliation(s)
- Xiaotao Geng
- Shandong University Cancer Center, Shandong University, 440 Jiyan Road, Jinan, 250117, China
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yaping Zhang
- Department of Radiology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yang Li
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yuanyuan Cai
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Jie Liu
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Tianxiang Geng
- Department of Biomaterials, Faculty of Dentistry, University of Oslo, Oslo, 0455, Norway
| | - Xiangdi Meng
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Furong Hao
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
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Li H, Chai L, Pu H, Yin LL, Li M, Zhang X, Liu YS, Pang MH, Lu T. T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer. Insights Imaging 2024; 15:57. [PMID: 38411722 PMCID: PMC10899552 DOI: 10.1186/s13244-024-01625-8] [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: 06/25/2023] [Accepted: 01/18/2024] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVE To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer. METHODS One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS). RESULTS Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233-5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247-5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710). CONCLUSION The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS. CRITICAL RELEVANCE STATEMENT A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer. KEY POINTS • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Li Chai
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Pu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Long-Lin Yin
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
- Institute of Radiation Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Mou Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Xin Zhang
- Pharmaceutical Diagnostic Team, GE Healthcare, Beijing, 100176, China
| | - Yi-Sha Liu
- Department of Pathology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Ming-Hui Pang
- Department of Geriatric Surgery, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China
| | - Tao Lu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# Second Section of First Ring Road, Qingyang District, Chengdu, Sichuan, 610070, China.
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Zhang H, Yin J, Zhou C, Qiu J, Wang J, Lv Q, Luo T. Identification of ipsilateral supraclavicular lymph node metastasis in breast cancer based on LASSO regression with a high penalty factor. Front Oncol 2024; 14:1349315. [PMID: 38371618 PMCID: PMC10869533 DOI: 10.3389/fonc.2024.1349315] [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: 12/04/2023] [Accepted: 01/19/2024] [Indexed: 02/20/2024] Open
Abstract
Aiming at the problems of small sample size and large feature dimension in the identification of ipsilateral supraclavicular lymph node metastasis status in breast cancer using ultrasound radiomics, an optimized feature combination search algorithm is proposed to construct linear classification models with high interpretability. The genetic algorithm (GA) is used to search for feature combinations within the feature subspace using least absolute shrinkage and selection operator (LASSO) regression. The search is optimized by applying a high penalty to the L1 norm of LASSO to retain excellent features in the crossover operation of the GA. The experimental results show that the linear model constructed using this method outperforms those using the conventional LASSO regression and standard GA. Therefore, this method can be used to build linear models with higher classification performance and more robustness.
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Affiliation(s)
- Haohan Zhang
- West China Hospital, Sichuan University, Chengdu, China
| | - Jin Yin
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Division of Breast Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Chen Zhou
- Division of Breast Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Breast Center, West China Hospital, Sichuan University, Chengdu, China
- Clinical Research Center for Breast Diseases, West China Hospital, Sichuan University, Chengdu, China
| | - Jiajun Qiu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Division of Breast Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Junren Wang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Qing Lv
- Division of Breast Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Breast Center, West China Hospital, Sichuan University, Chengdu, China
- Clinical Research Center for Breast Diseases, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Luo
- Breast Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Lai Y, Wu Y, Chen X, Gu W, Zhou G, Weng M. MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:209-229. [PMID: 38343263 DOI: 10.1007/s10278-023-00905-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 03/02/2024]
Abstract
The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.
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Affiliation(s)
- Yuling Lai
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yiyang Wu
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xiangyuan Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan.
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan.
| | - Guoxia Zhou
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China.
| | - Meilin Weng
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Cai Q, Huang Y, Ling J, Kong L, Lin Y, Chen Y, Cao W, Liao Y, Guo Y, Guan J, Wang H. Radiomics nomogram for predicting disease-free survival after partial resection or radical cystectomy in patients with bladder cancer. Br J Radiol 2024; 97:201-209. [PMID: 38263836 PMCID: PMC11027261 DOI: 10.1093/bjr/tqad010] [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: 06/27/2023] [Revised: 09/13/2023] [Accepted: 10/25/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVES To create a MRI-derived radiomics nomogram that combined clinicopathological factors and radiomics signature (Rad-score) for predicting disease-free survival (DFS) in patients with bladder cancer (BCa) following partial resection (PR) or radical cystectomy (RC), including lymphadenectomy (LAE). METHODS Finally, 80 patients with BCa after PR or RC with LAE were enrolled. Patients were randomly split into training (n = 56) and internal validation (n = 24) cohorts. Radiomic features were extracted from T2-weighted, dynamic contrast-enhanced, diffusion-weighted imaging, and apparent diffusion coefficient sequence. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to choose the valuable features and construct the Rad-score. The DFS prediction model was built using the Cox proportional hazards model. The relationship between the Rad-score and DFS was assessed using Kaplan-Meier analysis. A radiomics nomogram that combined the Rad-score and clinicopathological factors was created for individualized DFS estimation. RESULTS In both the training and validation cohorts, the Rad-score was positively correlated with DFS (P < .001). In the validation cohort, the radiomics nomogram combining the Rad-score, tumour pathologic stage (pT stage), and lymphovascular invasion (LVI) achieved better performance in DFS prediction (C-index, 0.807; 95% CI, 0.713-0.901) than either the clinicopathological (C-index, 0.654; 95% CI, 0.467-0.841) or Rad-score-only model (C-index, 0.770; 95% CI, 0.702-0.837). CONCLUSION The Rad-score was an independent predictor of DFS for patients with BCa after PR or RC with LAE, and the radiomics nomogram that combined the Rad-score, pT stage, and LVI achieved better performance in individual DFS prediction. ADVANCES IN KNOWLEDGE This study provided a non-invasive and simple method for personalized and accurate prediction of DFS in BCa patients after PR or RC.
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Affiliation(s)
- Qian Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yiping Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian Ling
- Department of Radiology, The Eastern Hospital of the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510700, China
| | - Lingmin Kong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yanling Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenxin Cao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuting Liao
- Philips Healthcare, Guangzhou, Guangdong 510220, China
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian Guan
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Huanjun Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
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Ren L, Ling X, Alexander G, Molitoris J, Choi J, Schumaker L, Mehra R, Gaykalova D. Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights from Oral Cavity Squamous Cell Carcinoma preoperative CT scans. RESEARCH SQUARE 2024:rs.3.rs-3857391. [PMID: 38343846 PMCID: PMC10854303 DOI: 10.21203/rs.3.rs-3857391/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Our study involved a retrospective review of 78 patients with OSCC who underwent surgical treatment at a single medical center. An approach involving feature selection and statistical model diagnostics was utilized to identify biomarkers. Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ = 3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.
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Affiliation(s)
- Lei Ren
- University of Maryland School of Medicine
| | - Xiao Ling
- University of Maryland School of Medicine
| | | | | | | | | | | | - Daria Gaykalova
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University; Marlene & Stewart Greenebaum Comprehensive Cancer Center, University of Maryland Medical Center; Institute for Genome Sciences, U
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Zhang C, Shi J, Li B, Yu X, Feng X, Yang H. Magnetic resonance imaging-guided radiofrequency ablation of breast cancer: a current state of the art review. Diagn Interv Radiol 2024; 30:48-54. [PMID: 36971252 PMCID: PMC10773175 DOI: 10.4274/dir.2022.221429] [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: 03/09/2022] [Accepted: 11/22/2022] [Indexed: 03/31/2023]
Abstract
With a gradual increase in breast cancer incidence and mortality rates and an urgent need to improve patient prognosis and cosmetology, magnetic resonance imaging (MRI)-guided radiofrequency ablation (RFA) therapy has attracted wide attention as a new treatment method for breast cancer. MRI-RFA results in a higher complete ablation rate and extremely low recurrence and complication rates. Thus, it may be used as an independent treatment for breast cancer or adjuvant to breast-conserving surgery to reduce the extent of breast resection. Furthermore, with MRI guidance, accurate control of RFA can be achieved, and breast cancer treatment can enter a new stage of minimally invasive, safe, and comprehensive therapy. With progress in MR thermometry technology, the applications of MRI are expected to broaden.
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Affiliation(s)
- Chuan Zhang
- Affiliated Hospital of North Sichuan Medical College, Department of Radiology, Nanchong, China
| | - Jing Shi
- North Sichuan Medical College, School of Medical Imaging, Sichuan Province Nanchong, China
| | - Bing Li
- Affiliated Hospital of North Sichuan Medical College, Department of Radiology, Nanchong, China
| | - Xiaoxuan Yu
- Affiliated Hospital of North Sichuan Medical College, Department of Radiology, Nanchong, China
| | - Xu Feng
- Affiliated Hospital of North Sichuan Medical College, Department of Radiology, Nanchong, China
| | - Hanfeng Yang
- Affiliated Hospital of North Sichuan Medical College, Department of Radiology, Nanchong, China
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Li J, Wang D, Zhang C. Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer. Front Oncol 2024; 13:1232192. [PMID: 38260829 PMCID: PMC10802857 DOI: 10.3389/fonc.2023.1232192] [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: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 01/24/2024] Open
Abstract
CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.
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Affiliation(s)
- Jia Li
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Dongxu Wang
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Chenxin Zhang
- Department of General Surgery, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
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Huang S, Lai H, Pan X, Lin Q, Qin Y, Liu F, Fang M, Huang W, Wei C. Development and Validation of a Nomogram for Predicting Survival Based on Ferritin and Transferrin Ratio in Breast Cancer Patients. Cancer Control 2024; 31:10732748241261553. [PMID: 38850515 PMCID: PMC11162600 DOI: 10.1177/10732748241261553] [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: 11/24/2023] [Revised: 05/08/2024] [Accepted: 05/28/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND Our objective is to develop a predictive model utilizing the ferritin and transferrin ratio (FTR) and clinical factors to forecast overall survival (OS) in breast cancer (BC) patients. METHODS We conducted a retrospective analysis of clinical data from 2858 BC patients diagnosed between 2013 and 2021. Subsequently, the cohort of 2858 BC patients underwent random assignment into distinct subsets: a training cohort comprising 2002 patients and a validation cohort comprising 856 patients, maintaining a proportional ratio of 7:3. Employing multivariable Cox regression analysis within the training cohort, we derived a prognostic nomogram. The predictive performance was assessed using calibration curves, C-index, and decision curve analysis. RESULTS The final prognostic model included the TNM stage, subtype, hemoglobin levels, and the ferritin-transferrin ratio. The nomogram achieved a C-index of .794 (95% CI: .777-.810). The nomogram demonstrated superior predictive accuracy for OS at 3, 5, and 7 years for BC, with area under the time-dependent curves of .812, .782, and .773, respectively. These values notably outperformed those of the conventional TNM stage. Decision curve analysis reaffirmed the greater net benefit of our nomogram compared to the TNM stage. These findings were subsequently validated in the independent validation cohort. CONCLUSION The FTR-based prognostic model may predict a patient's OS better than the TNM stage in a clinical setting. The nomogram can provide an early, affordable, and reliable tool for survival prediction, as well as aid clinicians in treatment option-making and prognosis evaluation. However, further multi-center prospective trials are required to confirm the reliability of the existing nomogram.
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Affiliation(s)
- Shuqing Huang
- Department of State-owned Asset Supervision and Administration Office, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Hao Lai
- Department of Guangxi Clinical Research Center for Colorectal Cancer, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Xiaolan Pan
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Qiumei Lin
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Yuling Qin
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Fengfei Liu
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Min Fang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Wencheng Huang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Caibiao Wei
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
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Zhou L, Li Y, Ni Y, Liu C. Analysis of postoperative pulmonary complications after gastrectomy for gastric cancer: development and validation of a nomogram. Front Surg 2023; 10:1308591. [PMID: 38186389 PMCID: PMC10768169 DOI: 10.3389/fsurg.2023.1308591] [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: 10/17/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
Background Postoperative pulmonary complications (PPCs) are common in gastric cancer patients after gastrectomy. The aim of our study was to investigate the perioperative risk factors and to develop a nomogram to identify patients who are at significant risk of PPCs. Methods The clinical data of gastric cancer patients who underwent elective gastrectomy in the First Affiliated Hospital of Nanjing Medical University from 2017 to 2021 were retrospectively collected. All patients were randomly divided into a training and a validation cohort at a ratio of 7:3. Univariate and multivariate analysis were applied to identify the independent risk factors that might predict PPCs, and a nomogram was constructed. Both discrimination and calibration abilities were estimated by the area under a receiver operating characteristic curve (AUC) and calibration curves. The clinical effectiveness of the nomogram was further quantified with the decision curve analysis (DCA). Results Of 2,124 included patients, one hundred and fifty patients (7.1%) developed PPCs. Binary logistic analysis showed that age > 65 years, higher total cholesterol level, longer duration of surgery, total gastrectomy, and the dose of oxycodone > 5.5 mg were independent risk factors for the occurrence of PPCs, which were contained in the nomogram. The predictive nomogram showed good discrimination and calibration [an AUC of 0.735 (95% CI: 0.687-0.783) in a training cohort and 0.781 (95% CI: 0.715-0.847) in a validation cohort]. The calibration curve and decision curve analysis showed a good agreement between nomogram predictions and actual observations. Conclusion We developed a nomogram model based on age, total cholesterol, extent of resection, duration of surgery, and the dose of oxycodone to predict the risk of PPCs in gastric cancer patients after elective gastrectomy.
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Affiliation(s)
| | | | | | - Cunming Liu
- Department of Anesthesiology and Perioperative Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Zhu Y, Ma Y, Zhang Y, Liu A, Wang Y, Zhao M, Li H, He N, Wu Y, Ye Z. Radiomics nomogram for predicting axillary lymph node metastasis-a potential method to address the limitation of axilla coverage in cone-beam breast CT: a bi-center retrospective study. LA RADIOLOGIA MEDICA 2023; 128:1472-1482. [PMID: 37857980 DOI: 10.1007/s11547-023-01731-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/21/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE Cone-beam breast CT (CBBCT) has an inherent limitation that the axilla cannot be imaged in its entirety. We aimed to develop and validate a nomogram based on clinical factors and contrast-enhanced (CE) CBBCT radiomics features to predict axillary lymph node (ALN) metastasis and complement limited axilla coverage. MATERIAL AND METHODS This retrospective study included 312 patients with breast cancer from two hospitals who underwent CE-CBBCT examination in a clinical trial (NCT01792999) during 2012-2020. Patients from TCIH comprised training set (n = 176) and validation set (n = 43), and patients from SYSUCC comprised external test set (n = 93). 3D ROIs were delineated manually and radiomics features were extracted by 3D Slicer software. RadScore was calculated and radiomics model was constructed after feature selection. Clinical model was built on independent predictors. Nomogram was developed with independent clinical predictors and RadScore. Diagnostic performance was compared among three models by ROC curve, and decision curve analysis (DCA) was used to evaluate the clinical utility of nomogram. RESULTS A total of 139 patients were ALN positive and 173 patients were negative. Twelve radiomics features remained after feature selection. Location and focality were selected as independent predictors for ALN status. The AUC of nomogram in external test set was higher than that of clinical model (0.80 vs. 0.66, p = 0.012). DCA demonstrated that the nomogram had higher overall net benefit than that of clinical model. CONCLUSION The nomogram combined CE-CBBCT-based radiomics features and clinical factors could have potential in distinguishing ALN positive from negative and addressing the limitation of axilla coverage in CBBCT.
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Affiliation(s)
- Yueqiang Zhu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Yue Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Aidi Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Yafei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Mengran Zhao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Haijie Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China
| | - Ni He
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Yaopan Wu
- Department of Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dong-Feng-Dong Road, Yuexiu District, Guangzhou, 510060, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, Tianjin, 300060, China.
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Fang J, Deng Q, Zhang J, Ma Y, Jin C, Lu J, Hao Y, Ma Y, Liu W, Li Z, Liu G, Mo Y, Xiao Y, Zheng C, Yang Y, Wu T, Zhao C, Zhou X, Zhou P. A Nomogram Based on Conventional and Contrast-Enhanced Ultrasound for Pre-operative Prediction of Nipple-Areola Complex Involvement in Breast Cancer: A Prospective Study. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2565-2572. [PMID: 37752018 DOI: 10.1016/j.ultrasmedbio.2023.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 08/16/2023] [Accepted: 08/28/2023] [Indexed: 09/28/2023]
Abstract
OBJECTIVE Accurately predicting nipple-areola complex (NAC) involvement in breast cancer is essential for identifying eligible patients for a nipple-sparing mastectomy. This study was aimed at developing a pre-operative nomogram for NAC involvement in breast cancer using conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS). METHODS All patients with primary breast cancer confirmed by pre-operative biopsy underwent US and CEUS examinations. Post-operative pathology was used as the gold standard in assessing NAC involvement. Lasso regression was used to select the predictors most associated with NAC involvement. A nomogram was constructed to calculate the diagnostic efficacy. The data were internally verified with 500 bootstrapped replications, and a calibration curve was generated to validate the predictive capability. RESULTS Seventy-six patients with primary breast cancer were included in this study, which included 16 patients (21.1%) with NAC involvement and 60 patients (78.9%) without NAC involvement. Among the 23 features of US and CEUS, Lasso regression selected one US feature and two CEUS features, namely, ductal echo extending from the lesion, ductal enhancement extending to the nipple and focal nipple enhancement. A nomogram was constructed, and the results revealed that the area under the curve, sensitivity, specificity and accuracy were 0.891, 81.3%, 86.7% and 85.5%, respectively. The calibration curve exhibited good consistency between the predicted probability and the actual probability. CONCLUSION The nomogram developed based on US and CEUS had good performance in predicting NAC involvement in breast cancer before surgery, which may facilitate the selection of suitable patients for NAC preservation with greater oncological safety.
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Affiliation(s)
- Jinghui Fang
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Qiongxia Deng
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Jingwen Zhang
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yuqin Ma
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Chunchun Jin
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Jianghao Lu
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yanli Hao
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yuanyuan Ma
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Weizong Liu
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Zhengyi Li
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Guowen Liu
- Department of Thyroid and Breast Surgery, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yongpan Mo
- Department of Thyroid and Breast Surgery, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yu Xiao
- Department of Thyroid and Breast Surgery, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Chang Zheng
- Department of Thyroid and Breast Surgery, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yajie Yang
- Department of Pathology, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Tingting Wu
- Department of Pathology, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China
| | - Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xin Zhou
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Peng Zhou
- Department of Ultrasound, First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.
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Zuo D, Yang L, Jin Y, Qi H, Liu Y, Ren L. Machine learning-based models for the prediction of breast cancer recurrence risk. BMC Med Inform Decis Mak 2023; 23:276. [PMID: 38031071 PMCID: PMC10688055 DOI: 10.1186/s12911-023-02377-z] [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: 07/21/2023] [Accepted: 11/17/2023] [Indexed: 12/01/2023] Open
Abstract
Breast cancer is the most common malignancy diagnosed in women worldwide. The prevalence and incidence of breast cancer is increasing every year; therefore, early diagnosis along with suitable relapse detection is an important strategy for prognosis improvement. This study aimed to compare different machine algorithms to select the best model for predicting breast cancer recurrence. The prediction model was developed by using eleven different machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), support vector classification (SVC), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), decision tree, multilayer perceptron (MLP), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), Gaussian naive Bayes (GaussianNB), and light gradient boosting machine (LightGBM), to predict breast cancer recurrence. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score were used to evaluate the performance of the prognostic model. Based on performance, the optimal ML was selected, and feature importance was ranked by Shapley Additive Explanation (SHAP) values. Compared to the other 10 algorithms, the results showed that the AdaBoost algorithm had the best prediction performance for successfully predicting breast cancer recurrence and was adopted in the establishment of the prediction model. Moreover, CA125, CEA, Fbg, and tumor diameter were found to be the most important features in our dataset to predict breast cancer recurrence. More importantly, our study is the first to use the SHAP method to improve the interpretability of clinicians to predict the recurrence model of breast cancer based on the AdaBoost algorithm. The AdaBoost algorithm offers a clinical decision support model and successfully identifies the recurrence of breast cancer.
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Affiliation(s)
- Duo Zuo
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China
- National Clinical Research Center for Cancer, Tianjin, 300060, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Lexin Yang
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China
- National Clinical Research Center for Cancer, Tianjin, 300060, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Yu Jin
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China
- Tongji University Cancer Center, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China
| | - Huan Qi
- China Mobile Group Tianjin Company Limited, Tianjin, 300308, China
| | - Yahui Liu
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China
- National Clinical Research Center for Cancer, Tianjin, 300060, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Li Ren
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China.
- National Clinical Research Center for Cancer, Tianjin, 300060, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China.
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [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: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Liu CJ, Zhang L, Sun Y, Geng L, Wang R, Shi KM, Wan JX. Application of CT and MRI images based on an artificial intelligence algorithm for predicting lymph node metastasis in breast cancer patients: a meta-analysis. BMC Cancer 2023; 23:1134. [PMID: 37993845 PMCID: PMC10666295 DOI: 10.1186/s12885-023-11638-z] [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: 07/20/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND This study aimed to comprehensively evaluate the accuracy and effect of computed tomography (CT) and magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithms for predicting lymph node metastasis in breast cancer patients. METHODS We systematically searched the PubMed, Embase and Cochrane Library databases for literature from inception to June 2023 using keywords that included 'artificial intelligence', 'CT,' 'MRI', 'breast cancer' and 'lymph nodes'. Studies that met the inclusion criteria were screened and their data were extracted for analysis. The main outcome measures included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC). RESULTS A total of 16 studies were included in the final meta-analysis, covering 4,764 breast cancer patients. Among them, 11 studies used the manual algorithm MRI to calculate breast cancer risk, which had a sensitivity of 0.85 (95% confidence interval [CI] 0.79-0.90; p < 0.001; I2 = 75.3%), specificity of 0.81 (95% CI 0.66-0.83; p < 0.001; I2 = 0%), a positive likelihood ratio of 4.6 (95% CI 4.0-4.8), a negative likelihood ratio of 0.18 (95% CI 0.13-0.26) and a diagnostic odds ratio of 25 (95% CI 17-38). Five studies used manual algorithm CT to calculate breast cancer risk, which had a sensitivity of 0.88 (95% CI 0.79-0.94; p < 0.001; I2 = 87.0%), specificity of 0.80 (95% CI 0.69-0.88; p < 0.001; I2 = 91.8%), a positive likelihood ratio of 4.4 (95% CI 2.7-7.0), a negative likelihood ratio of 0.15 (95% CI 0.08-0.27) and a diagnostic odds ratio of 30 (95% CI 12-72). For MRI and CT, the AUC after study pooling was 0.85 (95% CI 0.82-0.88) and 0.91 (95% CI 0.88-0.93), respectively. CONCLUSION Computed tomography and MRI images based on an AI algorithm have good diagnostic accuracy in predicting lymph node metastasis in breast cancer patients and have the potential for clinical application.
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Affiliation(s)
- Cheng-Jie Liu
- Department of Information Center, Lianyungang Human Resources and Social Security Bureau, Lianyungang, 222000, JiangSu, China
| | - Lei Zhang
- Department of Information System, Lianyungang 149 Hospital, Lianyungang, 222000, Jiangsu, China
| | - Yi Sun
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Lei Geng
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Rui Wang
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Kai-Min Shi
- Department of Information Center, Lianyungang Shuangcheng Information Technology Co., Ltd, Lianyungang, 222000, China
| | - Jin-Xin Wan
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China.
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Li Q, Zheng JX, Jia TW, Feng XY, Lv C, Zhang LJ, Yang GJ, Xu J, Zhou XN. Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling. Parasit Vectors 2023; 16:419. [PMID: 37968661 PMCID: PMC10652544 DOI: 10.1186/s13071-023-06001-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/06/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying levels of poverty. METHODS We collected and analyzed the prevalence patterns along with the cost of control measures in 11 counties over the last 20 years in China. Seven machine learning models, including XGBoost, support vector machine, generalized linear model, regression tree, random forest, gradient boosting machine and neural network, were used for developing model and calculate marginal benefits. RESULTS The XGBoost model had the highest prediction accuracy with an R2 of 0.7308. Results showed that risk surveillance, snail control with molluscicides and treatment were the most effective interventions in controlling schistosomiasis prevalence. The best combination of interventions was interlacing seven interventions, including risk surveillance, treatment, toilet construction, health education, snail control with molluscicides, cattle slaughter and animal chemotherapy. The marginal benefit of risk surveillance is the most effective intervention among nine interventions, which was influenced by the prevalence of schistosomiasis and cost. CONCLUSIONS In the elimination phase of the national schistosomiasis program, emphasizing risk surveillance holds significant importance in terms of cost-saving.
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Affiliation(s)
- Qin Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Jin-Xin Zheng
- Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School, Shanghai, 200025, China
| | - Tie-Wu Jia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Xin-Yu Feng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Chao Lv
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research and Shanghai Jiao Tong University School of Medicine, One Health Center, Shanghai Jiao Tong University and The Edinburgh University, Shanghai, 200025, China
| | - Li-Juan Zhang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Guo-Jing Yang
- School of Tropical Medicine, Hainan Medical University, Haikou, 571199, China
| | - Jing Xu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China.
- School of Global Health, Chinese Center for Tropical Diseases Research and Shanghai Jiao Tong University School of Medicine, One Health Center, Shanghai Jiao Tong University and The Edinburgh University, Shanghai, 200025, China.
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Li H, Chen XL, Liu H, Liu YS, Li ZL, Pang MH, Pu H. MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study. Eur Radiol 2023; 33:7561-7572. [PMID: 37160427 DOI: 10.1007/s00330-023-09723-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 03/07/2023] [Accepted: 03/20/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer. MATERIALS AND METHODS A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. RESULTS For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)-based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively. CONCLUSION Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk. CLINICAL RELEVANCE STATEMENT Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery. KEY POINTS • Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer. • The predictive performance of the clinical-radiomics nomogram was not improved compared with the combined intra- and peritumoral radiomics model for predicting TD. • The prognostic model constructed by combining intra- and peritumoral radscore-based TD and MRI-reported LNM showed good performance for assessing 3-year RFS.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, 610000, China
| | | | - Yi-Sha Liu
- Department of Pathology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Zhen-Lin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ming-Hui Pang
- Department of Gastrointestinal Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China.
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [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: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Yala A, Hughes KS. Rethinking Risk Modeling with Machine Learning. Ann Surg Oncol 2023; 30:6950-6952. [PMID: 37574515 DOI: 10.1245/s10434-023-14144-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023]
Affiliation(s)
- Adam Yala
- UC Berkeley, Berkeley, USA.
- UCSF, San Francisco, USA.
| | - Kevin S Hughes
- Surgical Oncology, Medical University of South Carolina, Charleston, USA
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Xiang Y, Hu Y, Chen C, Zhi H, Zhang Z, Lu M, Chen X, Luo Z, Chen S, Dias-Neto E, Pizzini P, Chen X, Chen X, Zhuang Y, Dong Q. Radiomics based on machine learning algorithms could predict prognosis and postoperative chemotherapy benefits of patients with gastric cancer: a retrospective cohort study. J Gastrointest Oncol 2023; 14:2048-2063. [PMID: 37969820 PMCID: PMC10643584 DOI: 10.21037/jgo-23-627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/20/2023] [Indexed: 11/17/2023] Open
Abstract
Background Traditional clinical characteristics have certain limitations in evaluating cancer prognosis. The radiomics features provide information on tumor morphology, tissue texture, and hemodynamics, which can accurately reflect personalized predictions. This study investigated the clinical value of radiomics features on contrast-enhanced computed tomography (CT) images in predicting prognosis and postoperative chemotherapy benefits for patients with gastric cancer (GC). Methods For this study, 171 GC patients who underwent radical gastrectomy and pathology confirmation of the malignancy at the First Affiliated Hospital of Wenzhou Medical University were retrospectively enrolled. The general information, pathological characteristics, and postoperative chemotherapy information were collected. Patients were also monitored through telephone interviews or outpatient treatment. GC patients were randomly divided into the developing cohort (n=120) and validation cohort (n=51). The intra-tumor areas of interest inside the tumors were delineated, and 1,218 radiomics features were extracted. The optimal radiomics risk score (RRS) was constructed using 8 machine learning algorithms and 29 algorithm combinations. Furthermore, a radiomics nomogram that included clinicopathological characteristics was constructed and validated through univariate and multivariate Cox analyses. Results Eleven prognosis-related features were selected, and an RRS was constructed. Kaplan-Meier curve analysis showed that the RRS had a high prognostic ability in the developing and validation cohorts (log-rank P<0.01). The RRS was higher in patients with a larger tumor size (≥3 cm), higher Charlson score (≥2), and higher clinical stage (Stages III and IV) (all P<0.001). Furthermore, GC patients with a higher RRS significantly benefited from postoperative chemotherapy. The results of univariate and multivariate Cox regression analyses demonstrated that the RRS was an independent risk factor for overall survival (OS) and disease-free survival (DFS) (P<0.001). A visual nomogram was established based on the significant factors in multivariate Cox analysis (P<0.05). The C-index was 0.835 (0.793-0.877) for OS and 0.733 (0.677-0.789) for DFS in the developing cohort. The calibration curve also showed that the nomogram had good agreement. Conclusions A nomogram that combines the RRS and clinicopathological characteristics could serve as a novel noninvasive preoperative prediction model with the potential to accurately predict the prognosis and chemotherapy benefits of GC patients.
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Affiliation(s)
- Yilan Xiang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuanbo Hu
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Chenbin Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huaiqing Zhi
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhao Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingdong Lu
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xietao Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhixian Luo
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Sian Chen
- Department of Emergency, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Emmanuel Dias-Neto
- Laboratory of Medical Genomics, A. C. Camargo Cancer Center, São Paulo, SP, Brazil
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Paolo Pizzini
- Department of Digestive Surgery, European Institute of Oncology-IRCCS, Milan, Italy
| | - Xinxin Chen
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaodong Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuandi Zhuang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiantong Dong
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Fang M, Lei M, Chen X, Cao H, Duan X, Yuan H, Guo L. Radiomics-based ultrasound models for thyroid nodule differentiation in Hashimoto's thyroiditis. Front Endocrinol (Lausanne) 2023; 14:1267886. [PMID: 37937055 PMCID: PMC10627229 DOI: 10.3389/fendo.2023.1267886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/25/2023] [Indexed: 11/09/2023] Open
Abstract
Background Previous models for differentiating benign and malignant thyroid nodules(TN) have predominantly focused on the characteristics of the nodules themselves, without considering the specific features of the thyroid gland(TG) in patients with Hashimoto's thyroiditis(HT). In this study, we analyzed the clinical and ultrasound radiomics(USR) features of TN in patients with HT and constructed a model for differentiating benign and malignant nodules specifically in this population. Methods We retrospectively collected clinical and ultrasound data from 227 patients with TN and concomitant HT(161 for training, 66 for testing). Two experienced sonographers delineated the TG and TN regions, and USR features were extracted using Python. Lasso regression and logistic analysis were employed to select relevant USR features and clinical data to construct the model for differentiating benign and malignant TN. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis(DCA). Results A total of 1,162 USR features were extracted from TN and the TG in the 227 patients with HT. Lasso regression identified 14 features, which were used to construct the TN score, TG score, and TN+TG score. Univariate analysis identified six clinical predictors: TI-RADS, echoic type, aspect ratio, boundary, calcification, and thyroid function. Multivariable analysis revealed that incorporating USR scores improved the performance of the model for differentiating benign and malignant TN in patients with HT. Specifically, the TN+TG score resulted in the highest increase in AUC(from 0.83 to 0.94) in the clinical prediction model. Calibration curves and DCA demonstrated higher accuracy and net benefit for the TN+TG+clinical model. Conclusion USR features of both the TG and TN can be utilized for differentiating benign and malignant TN in patients with HT. These findings highlight the importance of considering the entire TG in the evaluation of TN in HT patients, providing valuable insights for clinical decision-making in this population.
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Affiliation(s)
- Mengyuan Fang
- Department of Ultrasound, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, China
| | - Mengjie Lei
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Institute of Clinical Medicine, The First Affiliated Hospital of University of South, Hengyang, Hunan, China
| | - Xuexue Chen
- Department of Ultrasound, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Hong Cao
- Department of Ultrasound, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, China
| | - Xingxing Duan
- Department of Ultrasound, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, China
| | - Hongxia Yuan
- Department of Ultrasound, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, China
| | - Lili Guo
- Department of Ultrasound, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, China
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