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Rejmer C, Dihge L, Bendahl PO, Förnvik D, Dustler M, Rydén L. Preoperative prediction of nodal status using clinical data and artificial intelligence derived mammogram features enabling abstention of sentinel lymph node biopsy in breast cancer. Front Oncol 2024; 14:1394448. [PMID: 39050572 PMCID: PMC11266164 DOI: 10.3389/fonc.2024.1394448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Abstract
Introduction Patients with clinically node-negative breast cancer have a negative sentinel lymph node status (pN0) in approximately 75% of cases and the necessity of routine surgical nodal staging by sentinel lymph node biopsy (SLNB) has been questioned. Previous prediction models for pN0 have included postoperative variables, thus defeating their purpose to spare patients non-beneficial axillary surgery. We aimed to develop a preoperative prediction model for pN0 and to evaluate the contribution of mammographic breast density and mammogram features derived by artificial intelligence for de-escalation of SLNB. Materials and methods This retrospective cohort study included 755 women with primary breast cancer. Mammograms were analyzed by commercially available artificial intelligence and automated systems. The additional predictive value of features was evaluated using logistic regression models including preoperative clinical variables and radiological tumor size. The final model was internally validated using bootstrap and externally validated in a separate cohort. A nomogram for prediction of pN0 was developed. The correlation between pathological tumor size and the preoperative radiological tumor size was calculated. Results Radiological tumor size was the strongest predictor of pN0 and included in a preoperative prediction model displaying an area under the curve of 0.68 (95% confidence interval: 0.63-0.72) in internal validation and 0.64 (95% confidence interval: 0.59-0.69) in external validation. Although the addition of mammographic features did not improve discrimination, the prediction model provided a 21% SLNB reduction rate when a false negative rate of 10% was accepted, reflecting the accepted false negative rate of SLNB. Conclusion This study shows that the preoperatively available radiological tumor size might replace pathological tumor size as a key predictor in a preoperative prediction model for pN0. While the overall performance was not improved by mammographic features, one in five patients could be omitted from axillary surgery by applying the preoperative prediction model for nodal status. The nomogram visualizing the model could support preoperative patient-centered decision-making on the management of the axilla.
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Affiliation(s)
- Cornelia Rejmer
- Department of Clinical Sciences, Division of Surgery, Lund University, Lund, Sweden
| | - Looket Dihge
- Department of Clinical Sciences, Division of Surgery, Lund University, Lund, Sweden
- Department of Plastic and Reconstructive Surgery, Skåne University Hospital, Malmö, Sweden
| | - Pär-Ola Bendahl
- Department of Clinical Sciences, Division of Oncology, Lund University, Lund, Sweden
| | - Daniel Förnvik
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
- Department of Hematology, Oncology and Radiations Physics, Skåne University Hospital, Lund, Sweden
| | - Magnus Dustler
- Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden
- Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Lisa Rydén
- Department of Clinical Sciences, Division of Surgery, Lund University, Lund, Sweden
- Department of Surgery, Skåne University Hospital, Malmö, Sweden
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Hua Y, Peng Q, Han J, Fei J, Sun A. A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer. Magn Reson Imaging 2024; 110:128-137. [PMID: 38631535 DOI: 10.1016/j.mri.2024.04.019] [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: 03/03/2024] [Revised: 04/05/2024] [Accepted: 04/13/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVES To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI. MATERIALS AND METHODS A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications. RESULTS The constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model. CONCLUSION A combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer.
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Affiliation(s)
- Yuchen Hua
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiqi Peng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Junqi Han
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jie Fei
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Aimin Sun
- Nanfang Hospital Southern Medical University, Guangzhou, Guangdong, China.
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Ye X, Zhang X, Lin Z, Liang T, Liu G, Zhao P. Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer. Am J Transl Res 2024; 16:2398-2410. [PMID: 39006270 PMCID: PMC11236629 DOI: 10.62347/kepz9726] [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: 04/05/2024] [Accepted: 05/18/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer. METHODS We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model. RESULTS In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility. CONCLUSION Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.
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Affiliation(s)
- Xiaolu Ye
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Xiaoxue Zhang
- Guangzhou University of Chinese MedicineGuangzhou 510006, Guangdong, China
| | - Zhuangteng Lin
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Ting Liang
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Ge Liu
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, China
| | - Ping Zhao
- Guangzhou University of Traditional Chinese Medicine First Affiliated HospitalGuangzhou 510405, Guangdong, 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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Zhu Y, Ma Y, Zhai Z, Liu A, Wang Y, Zhang Y, Li H, Zhao M, Han P, Yin L, He N, Wu Y, Sechopoulos I, Ye Z, Caballo M. Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study. Eur Radiol 2024; 34:2576-2589. [PMID: 37782338 DOI: 10.1007/s00330-023-10256-4] [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: 10/04/2022] [Revised: 07/09/2023] [Accepted: 07/30/2023] [Indexed: 10/03/2023]
Abstract
OBJECTIVES To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer. METHODS Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics. After feature selection, support vector machine models were developed to predict ALN status and metastatic burden. To avoid overfitting on a specific patient subset, 100 randomly stratified splits were made to assign the patients to either training/fine-tuning or test set. Area under the receiver operating characteristic curve (AUC) of these radiomics models was compared to those obtained when training the models only with clinical features and combined clinical-radiomics descriptors. Ground truth was established by histopathology. RESULTS One hundred and eighteen patients had ALN metastasis (N + (≥ 1)). Of these, 74 had low burden (N + (1~2)) and 44 high burden (N + (≥ 3)). The remaining 156 patients had none (N0). AUC values across the 100 test repeats in predicting ALN status (N0/N + (≥ 1)) were 0.75 ± 0.05 (0.67~0.93, radiomics model), 0.68 ± 0.07 (0.53~0.85, clinical model), and 0.74 ± 0.05 (0.67~0.88, combined model). For metastatic burden prediction (N + (1~2)/N + (≥ 3)), AUC values were 0.65 ± 0.10 (0.50~0.88, radiomics model), 0.55 ± 0.10 (0.40~0.80, clinical model), and 0.64 ± 0.09 (0.50~0.90, combined model), with all the ranges spanning 0.5. In both cases, the radiomics model was significantly better than the clinical model (both p < 0.01) and comparable with the combined model (p = 0.56 and 0.64). CONCLUSIONS Radiomics features of primary tumors could have potential in predicting ALN metastasis in CE-CBBCT imaging. CLINICAL RELEVANCE STATEMENT The findings support potential clinical use of radiomics for predicting axillary lymph node metastasis in breast cancer patients and addressing the limited axilla coverage of cone-beam breast CT. KEY POINTS • Contrast-enhanced cone-beam breast CT-based radiomics could have potential to predict N0 vs. N + (≥ 1) and, to a limited extent, N + (1~2) vs. N + (≥ 3) from primary tumor, and this could help address the limited axilla coverage, pending future verifications on larger cohorts. • The average AUC of radiomics and combined models was significantly higher than that of clinical models but showed no significant difference between themselves. • Radiomics features descriptive of tumor texture were found informative on axillary lymph node status, highlighting a higher heterogeneity for tumor with positive axillary lymph node.
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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
| | - Zhenzhen Zhai
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Mei-Hua-Dong Road, Xiangzhou District, Zhuhai, 519000, 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
| | - 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
| | - 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
| | - 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
| | - Peng Han
- 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
| | - Lu Yin
- 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
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
- Dutch Expert Center for Screening (LRCB), PO Box 6873, Nijmegen, 6503 GJ, The Netherlands
- Technical Medicine Centre, University of Twente, PO Box 217, Enschede, 7500 AE, The Netherlands
| | - 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.
| | - Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
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Liu H, Zou L, Xu N, Shen H, Zhang Y, Wan P, Wen B, Zhang X, He Y, Gui L, Kong W. Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer. NPJ Breast Cancer 2024; 10:22. [PMID: 38472210 PMCID: PMC10933422 DOI: 10.1038/s41523-024-00628-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.
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Affiliation(s)
- Han Liu
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Liwen Zou
- Department of Mathematics, Nanjing University, Nanjing, 210008, China
| | - Nan Xu
- Department of Ultrasound, Jinling Hospital, Medical School of Nanjing University/General Hospital of Eastern Theater Command, Nanjing, 210002, China
| | - Haiyun Shen
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Yu Zhang
- Department of Mathematics, Nanjing University, Nanjing, 210008, China
| | - Peng Wan
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China
| | - Baojie Wen
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Xiaojing Zhang
- Department of Ultrasound, Taizhou Hospital Affiliated to Nanjing University of Chinese Medicine, Taizhou, 225300, China
| | - Yuhong He
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Luying Gui
- School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Wentao Kong
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, 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 2024:10.1007/s00330-024-10638-2. [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] [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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Chen W, Lin G, Kong C, Wu X, Hu Y, Chen M, Xia S, Lu C, Xu M, Ji J. Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics. Br J Radiol 2024; 97:439-450. [PMID: 38308028 DOI: 10.1093/bjr/tqad034] [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: 05/28/2023] [Revised: 09/13/2023] [Accepted: 11/20/2023] [Indexed: 02/04/2024] Open
Abstract
OBJECTIVES Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.
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Affiliation(s)
- Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Guihan Lin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Xulu Wu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Yumin Hu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Shuiwei Xia
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui 323000, China
- Department of Radiology, School of Medicine, Lishui Hospital of Zhejiang University, Lishui 323000, China
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Tang YL, Wang B, Ou-Yang T, Lv WZ, Tang SC, Wei A, Cui XW, Huang JS. Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer. Front Oncol 2023; 13:1217309. [PMID: 37965477 PMCID: PMC10641324 DOI: 10.3389/fonc.2023.1217309] [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: 05/05/2023] [Accepted: 09/29/2023] [Indexed: 11/16/2023] Open
Abstract
Objectives To determine whether ultrasound radiomics can be used to distinguish axillary lymph nodes (ALN) metastases in breast cancer based on ALN imaging. Methods A total of 147 breast cancer patients with 41 non-metastatic lymph nodes and 109 metastatic lymph nodes were divided into a training set (105 ALN) and a validation set (45 ALN). Radiomics features were extracted from ultrasound images and a radiomics signature (RS) was built. The Intraclass correlation coefficients (ICCs), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) methods were used to select the ALN status-related features. All images were assessed by two radiologists with at least 10 years of experience in ALN ultrasound examination. The performance levels of the model and radiologists in the training and validation subgroups were then evaluated and compared. Result Radiomics signature accurately predicted the ALN status, achieved an area under the receiver operator characteristic curve of 0.929 (95%CI, 0.881-0.978) and area under curve(AUC) of 0.919 (95%CI, 95%CI, 0.841-0.997) in training and validation cohorts respectively. The radiomics model performed better than two experts' prediction of ALN status in both cohorts (P<0.05). Besides, prediction in subgroups based on baseline clinicopathological information also achieved good discrimination performance, with an AUC of 0.937, 0.918, 0.885, 0.930, and 0.913 in HR+/HER2-, HER2+, triple-negative, tumor sized ≤ 3cm and tumor sized>3 cm, respectively. Conclusion The radiomics model demonstrated a good ability to predict ALN status in patients with breast cancer, which might provide essential information for decision-making.
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Affiliation(s)
- Yu-Long Tang
- Department of Thyroid Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Bin Wang
- Department of Medical Ultrasound, Yueyang Central Hospital, Yueyang, China
| | - Tao Ou-Yang
- Department of Medical Ultrasound, Hunan Cancer Hospital/The Afliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Shi-Chu Tang
- Department of Medical Ultrasound, Hunan Cancer Hospital/The Afliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - An Wei
- Department of Ultrasound, Hunan Provincial People’s Hospital, Changsha, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiang-Sheng Huang
- Department of Thyroid Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
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Rong XC, Kang YH, Shi GF, Ren JL, Liu YH, Li ZG, Yang G. The use of mammography-based radiomics nomograms for the preoperative prediction of the histological grade of invasive ductal carcinoma. J Cancer Res Clin Oncol 2023; 149:11635-11645. [PMID: 37405478 DOI: 10.1007/s00432-023-05001-9] [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: 05/06/2023] [Accepted: 06/15/2023] [Indexed: 07/06/2023]
Abstract
BACKGROUND Accurate prediction of the grade of invasive ductal carcinoma (IDC) before treatment is vital for individualized therapy and improving patient outcomes. This study aimed to develop and validate a mammography-based radiomics nomogram that would incorporate the radiomics signature and clinical risk factors in the preoperative prediction of the histological grade of IDC. METHODS The data of 534 patients from our hospital with pathologically confirmed IDC (374 in the training cohort and 160 in the validation cohort) were retrospectively analyzed. A total of 792 radiomics features were extracted from the patients' craniocaudal and mediolateral oblique view images. A radiomics signature was generated using the least absolute shrinkage and selection operator method. Multivariate logistic regression was adopted to establish a radiomics nomogram, the utility of which was evaluated using a receiver-operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signature was found to have a significant correlation with histological grade (P < 0.01), but the efficacy of the model is limited. The radiomics nomogram, which incorporated the radiomics signature and spicule sign into mammography, showed good consistency and discrimination in both the training cohort [area under the curve (AUC) = 0.75] and the validation cohort (AUC = 0.75). The calibration curves and DCA demonstrated the clinical usefulness of the proposed radiomics nomogram model. CONCLUSIONS A radiomics nomogram based on the radiomics signature and spicule sign can be used to predict the histological grade of IDC and assist in clinical decision-making for patients with IDC.
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Affiliation(s)
- Xiao-Cui Rong
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
| | - Yi-He Kang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
| | - Gao-Feng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
| | - Jia-Liang Ren
- GE Healthcare China, Daxing District, Tongji South Road No.1, Beijing, 100176, China
| | - Yu-Hao Liu
- GE Healthcare China, Daxing District, Tongji South Road No.1, Beijing, 100176, China
| | - Zhi-Gang Li
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
| | - Guang Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
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11
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Li WB, Du ZC, Liu YJ, Gao JX, Wang JG, Dai Q, Huang WH. Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning. Front Oncol 2023; 13:1219838. [PMID: 37719009 PMCID: PMC10503049 DOI: 10.3389/fonc.2023.1219838] [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: 05/09/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Objective To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients. Methods A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models. Results Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review. Conclusion A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.
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Affiliation(s)
- Wei-Bin Li
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Department of Ultrasonic Medicine Affiliated Hospital of Xizang Minzu University, Xianyang, China
| | - Zhi-Cheng Du
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yue-Jie Liu
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jun-Xue Gao
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jia-Gang Wang
- Department of Ultrasonic Medicine of Shantou Central Hospital, Shantou, China
| | - Qian Dai
- School of Informatics, Xiamen University, Xiamen, China
| | - Wen-He Huang
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Chen X, Yang Z, Huang R, Li Y, Liao Y, Li G, Wang M, Chen X, Dai Z, Fan W. Development and validation of a point-based scoring system for predicting axillary lymph node metastasis and disease outcome in breast cancer using clinicopathological and multiparametric MRI features. Cancer Imaging 2023; 23:54. [PMID: 37264446 DOI: 10.1186/s40644-023-00564-9] [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: 10/20/2022] [Accepted: 05/01/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Axillary lymph node (ALN) metastasis is used to select treatment strategies and define the prognosis in breast cancer (BC) patients and is typically assessed using an invasive procedure. Noninvasive, simple, and reliable tools to accurately predict ALN status are desirable. We aimed to develop and validate a point-based scoring system (PSS) for stratifying the ALN metastasis risk of BC based on clinicopathological and quantitative MRI features and to explore its prognostic significance. METHODS A total of 219 BC patients were evaluated. The clinicopathological and quantitative MRI features of the tumors were collected. A multivariate logistic regression analysis was used to create the PSS. The performance of the models was evaluated using receiver operating characteristic curves, and the area under the curve (AUC) of the models was calculated. Kaplan-Meier curves were used to analyze the survival outcomes. RESULTS Clinical features, including the American Joint Committee on Cancer (AJCC) stage, T stage, human epidermal growth factor receptor-2, estrogen receptor, and quantitative MRI features, including maximum tumor diameter, Kep, Ve, and TTP, were identified as risk factors for ALN metastasis and were assigned scores for the PSS. The PSS achieved an AUC of 0.799 in the primary cohort and 0.713 in the validation cohort. The recurrence-free survival (RFS) and overall survival (OS) of the high-risk (> 19.5 points) groups were significantly shorter than those of the low-risk (≤ 19.5 points) groups in the PSS. CONCLUSION PSS could predict the ALN metastasis risk of BC. A PSS greater than 19.5 was demonstrated to be a predictor of short RFS and OS.
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Affiliation(s)
- Xiaofeng Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Ruibin Huang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, People's Republic of China
| | - Yue Li
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
| | | | - Guijin Li
- MR Application, Siemens Healthineers, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthineers, Guangzhou, 510620, China
| | - Xiangguang Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031, People's Republic of China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, Guangdong, 515041, People's Republic of China.
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China.
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, China.
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Xu M, Yang H, Yang Q, Teng P, Hao H, Liu C, Yu S, Liu G. Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04859-z. [PMID: 37208454 DOI: 10.1007/s00432-023-04859-z] [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: 04/25/2023] [Accepted: 05/13/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma. METHODS The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted. RESULTS The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status. CONCLUSION The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Huimin Yang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Qi Yang
- Department of Radiology, The First Hospital of Jilin University, No.71 Xinmin Street, Changchun, 130012, China.
| | - Peihong Teng
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Haifeng Hao
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Chang Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China
| | - Shaonan Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China.
| | - Guifeng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, China.
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Zhou Z, Chen L, Dohopolski M, Sher D, Wang J. ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer. Phys Med Biol 2023; 68:10.1088/1361-6560/acca5b. [PMID: 37017082 PMCID: PMC11034768 DOI: 10.1088/1361-6560/acca5b] [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: 11/07/2022] [Accepted: 04/04/2023] [Indexed: 04/06/2023]
Abstract
Objective. Accurate diagnosis of lymph node metastasis (LNM) is critical in treatment management for patients with head and neck cancer. Positron emission tomography and computed tomography are routinely used for identifying LNM status. However, for small or less fluorodeoxyglucose (FDG) avid nodes, there are always uncertainties in LNM diagnosis. We are aiming to develop a reliable prediction model is for identifying LNM.Approach. In this study, a new automated and reliable multi-objective learning model (ARMO) is proposed. In ARMO, a multi-objective model is introduced to obtain balanced sensitivity and specificity. Meanwhile, confidence is calibrated by introducing individual reliability, whilst the model uncertainty is estimated by a newly defined overall reliability in ARMO. In the training stage, a Pareto-optimal model set is generated. Then all the Pareto-optimal models are used, and a reliable fusion strategy that introduces individual reliability is developed for calibrating the confidence of each output. The overall reliability is calculated to estimate the model uncertainty for each test sample.Main results. The experimental results demonstrated that ARMO obtained more promising results, which the area under the curve, accuracy, sensitivity and specificity can achieve 0.97, 0.93, 0.88 and 0.94, respectively. Meanwhile, based on calibrated confidence and overall reliability, clinicians could pay particular attention to highly uncertain predictions.Significance. In this study, we developed a unified model that can achieve balanced prediction, confidence calibration and uncertainty estimation simultaneously. The experimental results demonstrated that ARMO can obtain accurate and reliable prediction performance.
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Affiliation(s)
- Zhiguo Zhou
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA
- University of Kansas Cancer Center, Kansas City, KS, USA
| | - Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Dohopolski
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - David Sher
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Pesapane F, De Marco P, Rapino A, Lombardo E, Nicosia L, Tantrige P, Rotili A, Bozzini AC, Penco S, Dominelli V, Trentin C, Ferrari F, Farina M, Meneghetti L, Latronico A, Abbate F, Origgi D, Carrafiello G, Cassano E. How Radiomics Can Improve Breast Cancer Diagnosis and Treatment. J Clin Med 2023; 12:jcm12041372. [PMID: 36835908 PMCID: PMC9963325 DOI: 10.3390/jcm12041372] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Correspondence: ; Tel.: +39-02-574891
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rapino
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Eleonora Lombardo
- UOC of Diagnostic Imaging, Policlinico Tor Vergata University, 00133 Rome, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Priyan Tantrige
- Department of Radiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Chiara Trentin
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Mariagiorgia Farina
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Francesca Abbate
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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Tang Y, Che X, Wang W, Su S, Nie Y, Yang C. Radiomics model based on features of axillary lymphatic nodes to predict axillary lymphatic node metastasis in breast cancer. Med Phys 2022; 49:7555-7566. [PMID: 35869750 DOI: 10.1002/mp.15873] [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: 03/07/2022] [Revised: 07/10/2022] [Accepted: 07/14/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Breast cancer (BC) is among the most common cancers worldwide. Machine learning-based radiomics model could predict axillary lymph node metastasis (ALNM) of BC accurately. PURPOSE The purpose is to develop a machine learning model to predict ALNM of BC by focusing on the radiomics features of axillary lymphatic node (ALN). METHODS A group of 398 BC patients with 800 ALNs were retrospectively collected. A set of patient characteristics were obtained to form clinical factors. Three hundred and twenty-six radiomics features were extracted from each region of interest for ALN in contrast-enhanced computed tomography (CECT) image. A framework composed of four feature selection methods and 14 machine learning classification algorithms was systematically applied. A clinical model, a radiomics model, and a combined model were developed using a cross-validation approach and compared. Metrics of the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the performance of these models in the prediction of ALNM in BC. RESULTS Among the 800 cases of ALNs, there were 388 cases of positive metastasis (48.50%) and 412 cases of negative metastasis (51.50%). The baseline clinical model achieved the performance with an AUC = 0.8998 (95% CI [0.8540, 0.9457]). The radiomics model achieved an AUC = 0.9081 (95% CI [0.8640, 0.9523]). The combined model using the clinical factors and radiomics features achieved the best results with an AUC = 0.9305 (95% CI [0.8928, 0.9682]). CONCLUSIONS Combinations of feature selection methods and machine learning-based classification algorithms can develop promising predictive models to predict ALNM in BC using CECT features. The combined model of clinical factors and radiomics features outperforms both the clinical model and the radiomic model.
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Affiliation(s)
- Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaoling Che
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
| | - Weijia Wang
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Yue Nie
- Department of Radiology, Luzhou People's Hospital, Luzhou, Sichuan, China
| | - Chunmei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, Sichuan, China
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Gong X, Guo Y, Zhu T, Peng X, Xing D, Zhang M. Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1046005. [PMID: 36518318 PMCID: PMC9742555 DOI: 10.3389/fonc.2022.1046005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/11/2022] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer. MATERIALS AND METHODS Multiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman's correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity. RESULTS A total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis (P < 0.05). CONCLUSION Our results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.
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Affiliation(s)
| | | | | | | | | | - Minguang Zhang
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Lee J, Kim SH, Kim Y, Park J, Park GE, Kang BJ. Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer. Cancers (Basel) 2022; 14:cancers14184461. [PMID: 36139620 PMCID: PMC9497155 DOI: 10.3390/cancers14184461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/04/2022] [Accepted: 09/11/2022] [Indexed: 11/16/2022] Open
Abstract
This study aimed to predict early breast cancer recurrence in women under 40 years of age using radiomics signature and clinicopathological information. We retrospectively investigated 155 patients under 40 years of age with invasive breast cancer who underwent MRI and surgery. Through stratified random sampling, 111 patients were assigned as the training set, and 44 were assigned as the validation set. Recurrence-associated factors were investigated based on recurrence within 5 years during the total follow-up period. A Rad-score was generated through texture analysis (3D slicer, ver. 4.8.0) of breast MRI using the least absolute shrinkage and selection operator Cox regression model. The Rad-score showed a significant association with disease-free survival (DFS) in the training set (p = 0.003) and validation set (p = 0.020) in the Kaplan–Meier analysis. The nomogram was generated through Cox proportional hazards models, and its predictive ability was validated. The nomogram included the Rad-score and estrogen receptor negativity as predictive factors and showed fair DFS predictive ability in both the training and validation sets (C-index 0.63, 95% CI 0.45–0.79). In conclusion, the Rad-score can predict the disease recurrence of invasive breast cancer in women under 40 years of age, and the Rad-score-based nomogram showed reasonably high DFS predictive ability, especially within 2 years of surgery.
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Affiliation(s)
- Jeongmin Lee
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Sung Hun Kim
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
- Correspondence: ; Tel.: +82-2-2258-6250
| | - Yelin Kim
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
| | - Jaewoo Park
- Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
- Department of Applied Statistics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 06591, Korea
| | - Ga Eun Park
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
| | - Bong Joo Kang
- Department of Radiology, College of Medicine, Seoul Saint Mary’s Hospital, The Catholic University of Korea, Seoul 06591, Korea
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Liu Z, Huang D, Yang C, Shu J, Li J, Qin N. Efficient Axillary Lymph Node Detection Via Two-stage Spatial-information-fusion-based CNN. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106953. [PMID: 35772232 DOI: 10.1016/j.cmpb.2022.106953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/03/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Preoperative imaging diagnosis of axillary lymph node (ALN) metastasis is particularly important for breast cancer patients. This paper focuses on developing non-invasive and automatic schemes for accurate localization and classification (metastasis prediction) of ALN via contrast-enhanced computed tomography (CECT) image and deep learning models. METHODS Based on a two-stage strategy, a novel detection neural network is proposed, where the convolutional block attention module is utilized to extract spacial information and the bottleneck feature fusion module is designed for feature fusion in different scales. RESULTS Owing to the two embedded modules, the proposed convolutional neural network (CNN) model outperforms Faster R-CNN, YOLOv3, and EfficientDet in the sense that the achieved mAP is 0.454, higher than 0.247, 0.335, and 0.329, respectively. In particular, considering the function of classification only, the proposed model reaches the best performance on most indices (accuracy of 0.968, positive predictive value of 0.972, negative predictive value of 0.966, specificity of 0.983), compared with the methods that have been frequently adopted to predict ALN. In addition, the proposed CNN model has the function of locating ALN, which is lacking in existing models. CONCLUSIONS In this paper, a supervised deep learning method is proposed to detect ALN in CECT images. The positive effect of new added modules are verified, and the benefits of spatial information in ALN detection are confirmed. Further, the two subtasks called localization and classification are evaluated separately, where the proposed model achieves the best performance on most indices. The source code mentioned in this article will be released later.
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Affiliation(s)
- Ziyi Liu
- Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Deqing Huang
- Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Chunmei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Jinhan Li
- Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Na Qin
- Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China.
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Fang Y, Kang D, Guo W, Zhang Q, Xu S, Huang X, Xi G, He J, Wu S, Li L, Han X, Chen J, Zheng L, Wang C, Chen J. Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy. JOURNAL OF BIOPHOTONICS 2022; 15:e202100365. [PMID: 35084104 DOI: 10.1002/jbio.202100365] [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: 11/25/2021] [Revised: 01/16/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Accurate identification of axillary lymph node (ALN) status is crucial for tumor staging procedure and decision making. This retrospective study of 898 participants from two institutions was conducted. The aim of this study is to evaluate the diagnostic performance of clinical parameters combined with collagen signatures (tumor-associated collagen signatures [TACS] and the TACS corresponding microscopic features [TCMF]) in predicting the probability of ALN metastasis in patients with breast cancer. These findings suggest that TACS and TCMF in the breast tumor microenvironment are both novel and independent biomarkers for the estimation of ALN metastasis. The nomogram based on independent clinical parameters combined with TACS and TCMF yields good diagnostic performance in predicting ALN status.
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Affiliation(s)
- Ye Fang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Deyong Kang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wenhui Guo
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Qingyuan Zhang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Shuoyu Xu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, China
| | - Xingxin Huang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Gangqin Xi
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Jiajia He
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Shulian Wu
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Lianhuang Li
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Xiahui Han
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Jianhua Chen
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Liqin Zheng
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Chuan Wang
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianxin Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China
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Bove S, Comes MC, Lorusso V, Cristofaro C, Didonna V, Gatta G, Giotta F, La Forgia D, Latorre A, Pastena MI, Petruzzellis N, Pomarico D, Rinaldi L, Tamborra P, Zito A, Fanizzi A, Massafra R. A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients. Sci Rep 2022; 12:7914. [PMID: 35552476 PMCID: PMC9098914 DOI: 10.1038/s41598-022-11876-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 04/29/2022] [Indexed: 12/19/2022] Open
Abstract
In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.
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Affiliation(s)
- Samantha Bove
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Cristian Cristofaro
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Gianluca Gatta
- Dipartimento Di Medicina Di Precisione, Università Della Campania "Luigi Vanvitelli", 80131, Napoli, Italy
| | - Francesco Giotta
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale Di Radiologia Senologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Agnese Latorre
- Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Irene Pastena
- Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Nicole Petruzzellis
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale Di Oncologia Per La Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
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Choi L, Ku K, Chen W, Shahait AD, Kim S. Axillary Needle Biopsy in the Era of American College of Surgeons Oncology Group (ACOSOG) Z0011: Institutional Experience With a Largely Urban Minority Population and Review of the Literature. Cureus 2022; 14:e24317. [PMID: 35607532 PMCID: PMC9122337 DOI: 10.7759/cureus.24317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2022] [Indexed: 11/24/2022] Open
Abstract
Background: The American College of Surgeons Oncology Group (ACOSOG) Z0011 trial demonstrated that sentinel lymph node biopsy (SLNB) alone is adequate for axillary control in patients with one to two positive axillary lymph nodes. However, axillary lymph node dissection (ALND) is required in patients with N1 disease diagnosed with a preoperative needle biopsy. In this report, we determined how many patients could potentially have had SNB alone based on finding only one to two positive nodes in the axilla. Methods: A retrospective review of patients with positive preoperative axillary needle biopsy undergoing ALND was used to identify rates of high volume axillary disease (>2 positive nodes). Wilcoxon’s rank-sum and Fisher’s exact test were used for statistical analysis. A review of the literature is included for comparison. Results: 73% of 51 total patients with a positive needle biopsy had >2 positive nodes on axillary dissection. The high-volume axillary disease was significantly more likely with the presence of lymphovascular invasion and extranodal extension. Conclusions: Patients with positive preoperative axillary needle biopsies have a significantly higher rate of high volume axillary disease. However, at least one-quarter of these patients will have <3 positive nodes and potentially could have been treated with SNB alone.
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Song D, Yang F, Zhang Y, Guo Y, Qu Y, Zhang X, Zhu Y, Cui S. Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer. Cancer Imaging 2022; 22:17. [PMID: 35379339 PMCID: PMC8981871 DOI: 10.1186/s40644-022-00450-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/01/2022] [Indexed: 12/20/2022] Open
Abstract
Purpose The goal of this study is to develop and validate a radiomics nomogram integrating the radiomics features from DCE-MRI and clinical factors for the preoperative diagnosis of axillary lymph node (ALN) metastasis in breast cancer patients. Procedures A total of 432 patients with breast cancer were enrolled in this retrospective study and divided into a training cohort (n = 296) and a validation cohort (n = 136). Radiomics features were extracted from the second phase of dynamic contrast enhanced (DCE) MRI images. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen optimal features and construct a radiomics signature in the training cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on the radiomics signature and clinical factors. The predictive performance of the nomogram was quantified with respect to discrimination and calibration, which was further evaluated in the independent validation cohort. Results Fourteen ALN metastasis-related features were selected to construct the radiomics signature, with an area under the curve (AUC) of 0.847 and 0.805 in the training and validation cohorts, respectively. The nomogram was established by incorporating the histological grade, multifocality, MRI report lymph node status and radiomics signature and showed good calibration and excellent performance for ALN detection (AUC of 0.907 and 0.874 in the training and validation cohorts, respectively). The decision curve, which demonstrated the radiomics nomogram, displayed promising clinical utility. Conclusions The radiomics nomogram can be used as a noninvasive and reliable tool to assist clinicians in accurately predicting ALN metastasis in breast cancer preoperatively. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00450-w.
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Affiliation(s)
- Deling Song
- Graduate Faculty, Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China.,Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang New District, Ouhai District, Wenzhou, 32000, Zhejiang, China
| | - Fei Yang
- Department of Radiology, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China
| | - Yujiao Zhang
- Department of Radiology, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China
| | - Yazhe Guo
- Department of Radiology, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China
| | - Yingwu Qu
- Department of Radiology, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China
| | - Xiaochen Zhang
- Department of Radiology, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China
| | - Yuexiang Zhu
- Department of Radiology, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China
| | - Shujun Cui
- Department of Radiology, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China.
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Wang G, Shi D, Guo Q, Zhang H, Wang S, Ren K. Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer. Front Oncol 2022; 12:843436. [PMID: 35433437 PMCID: PMC9012139 DOI: 10.3389/fonc.2022.843436] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/04/2022] [Indexed: 12/11/2022] Open
Abstract
Objectives This study aims to build radiomics model of Breast Imaging Reporting and Data System (BI-RADS) category 4 and 5 mammographic masses extracted from digital mammography (DM) for mammographic masses characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods This retrospective study included 288 female patients (age, 52.41 ± 10.31) who had BI-RADS category 4 or 5 mammographic masses with an indication for biopsy. The patients were divided into two temporal set (training set, 82 malignancies and 110 benign lesions; independent test set, 48 malignancies and 48 benign lesions). A total of 188 radiomics features were extracted from mammographic masses on the combination of craniocaudal (CC) position images and mediolateral oblique (MLO) position images. For the training set, Pearson’s correlation and the least absolute shrinkage and selection operator (LASSO) were used to select non-redundant radiomics features and useful radiomics features, respectively, and support vector machine (SVM) was applied to construct a radiomics model. The receiver operating characteristic curve (ROC) analysis was used to evaluate the classification performance of the radiomics model and to determine a threshold value with a sensitivity higher than 98% to predict the mammographic masses malignancy. For independent test set, identical threshold value was used to validate the classification performance of the radiomics model. The stability of the radiomics model was evaluated by using a fivefold cross-validation method, and two breast radiologists assessed the diagnostic agreement of the radiomics model. Results In the training set, the radiomics model obtained an area under the receiver operating characteristic curve (AUC) of 0.934 [95% confidence intervals (95% CI), 0.898–0.971], a sensitivity of 98.8% (81/82), a threshold of 0.22, and a specificity of 60% (66/110). In the test set, the radiomics model obtained an AUC of 0.901 (95% CI, 0.835–0.961), a sensitivity of 95.8% (46/48), and a specificity of 66.7% (32/48). The radiomics model had relatively stable sensitivities in fivefold cross-validation (training set, 97.39% ± 3.9%; test set, 98.7% ± 4%). Conclusion The radiomics method based on DM may help reduce the temporarily unnecessary invasive biopsies for benign mammographic masses over-classified in BI-RADS category 4 and 5 while providing similar diagnostic performance for malignant mammographic masses as biopsies.
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Affiliation(s)
| | - Dafa Shi
- Xiang’an Hospital, Xiamen University, Xiamen, China
| | - Qiu Guo
- Xiang’an Hospital, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Xiang’an Hospital, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Xiang’an Hospital, Xiamen University, Xiamen, China
| | - Ke Ren
- Xiang’an Hospital, Xiamen University, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- *Correspondence: Ke Ren,
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Tang Y, Yang CM, Su S, Wang WJ, Fan LP, Shu J. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma. BMC Cancer 2021; 21:1268. [PMID: 34819043 PMCID: PMC8611922 DOI: 10.1186/s12885-021-08947-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 11/01/2021] [Indexed: 12/15/2022] Open
Abstract
Background Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Methods A group of 100 patients diagnosed with ECC was included. The ECC status of all patients was confirmed by pathology. A total of 1200 radiomics features were extracted from axial T1 weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and apparent diffusion coefficient (ADC) images. A systematical framework considering combinations of five feature selection methods and ten machine learning classification algorithms (classifiers) was developed and investigated. The predictive capabilities for DD and LNM were evaluated in terms of area under precision recall curve (AUPRC), area under the receiver operating characteristic (ROC) curve (AUC), negative predictive value (NPV), accuracy (ACC), sensitivity, and specificity. The prediction performance among models was statistically compared using DeLong test. Results For DD prediction, the feature selection method joint mutual information (JMI) and Bagging Classifier achieved the best performance (AUPRC = 0.65, AUC = 0.90 (95% CI 0.75–1.00), ACC = 0.85 (95% CI 0.69–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.88 (95% CI 0.64–0.97)), and the radiomics signature was composed of 5 selected features. For LNM prediction, the feature selection method minimum redundancy maximum relevance and classifier eXtreme Gradient Boosting achieved the best performance (AUPRC = 0.95, AUC = 0.98 (95% CI 0.94–1.00), ACC = 0.90 (95% CI 0.77–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.94 (95% CI 0.72–0.99)), and the radiomics signature was composed of 30 selected features. However, these two chosen models were not significantly different to other models of higher AUC values in DeLong test, though they were significantly different to most of all models. Conclusion MRI radiomics analysis based on machine learning demonstrated good predictive accuracies for DD and LNM of ECC. This shed new light on the noninvasive diagnosis of ECC.
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Affiliation(s)
- Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, Sichuan, China
| | - Chun Mei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, 646000, Sichuan, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China
| | - Wei Jia Wang
- School of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu, 610054, Sichuan, China
| | - Li Ping Fan
- Department of Ultrasound, The Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Luzhou, 646000, Sichuan, China.
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, 646000, Sichuan, China.
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Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110372] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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Yang C, Dong J, Liu Z, Guo Q, Nie Y, Huang D, Qin N, Shu J. Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography. Front Oncol 2021; 11:726240. [PMID: 34616678 PMCID: PMC8488257 DOI: 10.3389/fonc.2021.726240] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/27/2021] [Indexed: 12/29/2022] Open
Abstract
Background The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately. Purpose The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer. Methods We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC). Results The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer. Conclusion The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.
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Affiliation(s)
- Chunmei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jing Dong
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ziyi Liu
- The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, China
| | - Qingxi Guo
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yue Nie
- Department of Radiology, Luzhou People's Hospital, Luzhou, China
| | - Deqing Huang
- The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, China
| | - Na Qin
- The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Modi A, Purohit P, Gadwal A, Ukey S, Roy D, Fernandes S, Banerjee M. In-Silico Analysis of Differentially Expressed Genes and Their Regulating microRNA Involved in Lymph Node Metastasis in Invasive Breast Carcinoma. Cancer Invest 2021; 40:55-72. [PMID: 34396887 DOI: 10.1080/07357907.2021.1969574] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Axillary nodal metastasis is related to poor prognosis in breast cancer (BC). Key candidate genes in BC lymph node metastasis have been identified from Gene Expression Omnibus datasets and explored through functional enrichment database for annotation, visualization and integrated discovery (DAVID) , protein-protein interaction by Search Tool for the Retrieval of Interacting Genes and proteins (STRING), network visualization (Cytoscape), survival analysis (GEPIA, KM Plotter), and target prediction (miRNet). A total of 102 overlapping differentially expressed genes were found. In-silico survival and expression analyses revealed six candidate hub genes, Desmocollin 3 (DSC3), KRT5, KRT6B, KRT17, KRT81, and SERPINB5, to be significantly associated with nodal metastasis and overall survival, and 83 MicroRNA (miRNAs), which may be potential diagnostic markers and therapeutic targets in BC patients.
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Affiliation(s)
- Anupama Modi
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Purvi Purohit
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Ashita Gadwal
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Shweta Ukey
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Dipayan Roy
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Sujoy Fernandes
- Department of Radiation Oncology, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Mithu Banerjee
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
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Jiang T, Su W, Zhao Y, Li Q, Huang P. Non-invasive prediction of lymph node status for patients with early-stage invasive breast cancer based on a morphological feature from ultrasound images. Quant Imaging Med Surg 2021; 11:3399-3407. [PMID: 34341718 DOI: 10.21037/qims-20-1201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 04/01/2021] [Indexed: 12/12/2022]
Abstract
Background This study aimed to estimate the value of a morphological feature on ultrasound (US) for preoperative diagnosis of axillary lymph node (ALN) status in patients with early-stage invasive breast cancer (ESIBC). Methods In this retrospective work, a total of 239 ESIBC patients, were recruited, and their preoperative US images and postoperative pathology results were collected. The relationship between US images based on morphological features and ALN metastasis was investigated. The tumor circularity and US-reported ALN status were developed as a nomogram to predict the ALN status. Results Among the 239 participants, 82 (34.31%) had ALN metastasis, and 157 (65.69%) did not. There was a statistically significant difference in tumors between participants diagnosed with and without ALN metastasis. The median value was 0.47 vs. 0.62 (P<0.001) in the training group, respectively, and the value was 0.50 vs. 0.60 (P<0.001) in the validation group, respectively. The clinical model nomogram was shown to have high efficiency in predicting ALN status among our research population. The area under the curve (AUC) was 0.89 in the training group and 0.90 in the validation group and the accuracy was 85.79% and 81.63%, respectively. Conclusions The clinical model nomogram based on tumor circularity and US-reported ALN status is a non-invasive approach for ALN metastasis prediction in ESIBC patients with high efficacy.
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Affiliation(s)
- Tao Jiang
- Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Weiwei Su
- Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanan Zhao
- Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qunying Li
- Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Pintong Huang
- Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future. ACTA ACUST UNITED AC 2021; 28:2351-2372. [PMID: 34202321 PMCID: PMC8293249 DOI: 10.3390/curroncol28040217] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/14/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022]
Abstract
Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.
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Kanbayti IH, Rae WID, McEntee MF, Gandomkar Z, Ekpo EU. Clinicopathologic breast cancer characteristics: predictions using global textural features of the ipsilateral breast mammogram. Radiol Phys Technol 2021; 14:248-261. [PMID: 34076829 DOI: 10.1007/s12194-021-00622-6] [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: 12/18/2020] [Revised: 05/25/2021] [Accepted: 05/28/2021] [Indexed: 11/25/2022]
Abstract
Radiomic features from mammograms have been shown to predict breast cancer (BC) risk; however, their contribution to BC characteristics has not yet been explored. This study included 184 women with BC between January 2012 and April 2017. A set of 33 global radiomic features were extracted from the ipsilateral breast mammogram. Associations between radiomic features and BC characteristics were investigated by univariate logistic regression analysis, and receiver-operating characteristic curve analysis was employed to evaluate the predictive performance of radiomic features. Histogram-based features (mean, 70th percentile, and 30th percentile) weakly differentiated progesterone status and tumor size (AUC range: 0.627-0.652, p ≤ 0.007). One gray level run length matrix (GLRLM)-based feature achieved an AUC of 0.68 in discriminating lymph-node status, and the fractal dimension achieved an AUC of 0.65 in predicting tumor size. After stratifying by age at BC diagnosis and baseline percent density (PD), the average predictive performance of the abovementioned features improved from 0.652 to 0.707 for baseline PD adjustment, and from 0.652 to 0.674 for age at BC diagnosis. Higher predictive performances were found for GLRLM-based features in predicting lymph-node status among younger women with high baseline PD (AUC range: 0.710-0.863), and for fractal features in predicting tumor size among patients with low PD (AUC: 0.704). Global radiomic features from the ipsilateral breast mammogram can predict lymph-node status and tumor size among certain categories of women and should be considered as a non-invasive tool for clinical decision-making in BC-affected women and for forecasting disease progression.
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Affiliation(s)
- Ibrahem H Kanbayti
- Diagnostic Radiography Technology Department, Faculty of Applied Medical Sciences, King Abdul-Aziz University, Jeddah, Saudi Arabia.
- Medical Image Optimization and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Campus C4 75 East Street, Sydney, NSW 2141, Australia.
| | - William I D Rae
- Medical Image Optimization and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Campus C4 75 East Street, Sydney, NSW 2141, Australia
| | - Mark F McEntee
- Medical Image Optimization and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Campus C4 75 East Street, Sydney, NSW 2141, Australia
- Department of Medicine Roinn Na Sláinte, Brookfield Health Sciences, UG 12 Áras Watson, Galway, T12 AK54, Ireland
| | - Ziba Gandomkar
- Medical Image Optimization and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Campus C4 75 East Street, Sydney, NSW 2141, Australia
| | - Ernest U Ekpo
- Medical Image Optimization and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Campus C4 75 East Street, Sydney, NSW 2141, Australia
- Orange Radiology, Laboratories and Research Centre, Calabar, Nigeria
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Progression of Metastasis through Lymphatic System. Cells 2021; 10:cells10030627. [PMID: 33808959 PMCID: PMC7999434 DOI: 10.3390/cells10030627] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/08/2021] [Accepted: 03/09/2021] [Indexed: 02/06/2023] Open
Abstract
Lymph nodes are the most common sites of metastasis in cancer patients. Nodal disease status provides great prognostic power, but how lymph node metastases should be treated is under debate. Thus, it is important to understand the mechanisms by which lymph node metastases progress and how they can be targeted to provide therapeutic benefits. In this review, we focus on delineating the process of cancer cell migration to and through lymphatic vessels, survival in draining lymph nodes and further spread to other distant organs. In addition, emerging molecular targets and potential strategies to inhibit lymph node metastasis are discussed.
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A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case. MATHEMATICS 2021. [DOI: 10.3390/math9040410] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.
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Samiei S, Granzier RWY, Ibrahim A, Primakov S, Lobbes MBI, Beets-Tan RGH, van Nijnatten TJA, Engelen SME, Woodruff HC, Smidt ML. Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer. Cancers (Basel) 2021; 13:757. [PMID: 33673071 PMCID: PMC7917661 DOI: 10.3390/cancers13040757] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/03/2021] [Accepted: 02/08/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51-68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41-0.74 and 0.48-0.89 in the training cohorts, respectively, and between 0.30-0.98 and 0.37-0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.
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Affiliation(s)
- Sanaz Samiei
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (S.S.); (S.M.E.E.); (M.L.S.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (A.I.); (S.P.); (M.B.I.L.); (T.J.A.v.N.); (H.C.W.)
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
| | - Renée W. Y. Granzier
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (S.S.); (S.M.E.E.); (M.L.S.)
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
| | - Abdalla Ibrahim
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (A.I.); (S.P.); (M.B.I.L.); (T.J.A.v.N.); (H.C.W.)
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
- The D-Lab, Department of Precision Medicine, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire de Liege, Rue de Gaillarmont 600, 4030 Liege, Belgium
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Sergey Primakov
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (A.I.); (S.P.); (M.B.I.L.); (T.J.A.v.N.); (H.C.W.)
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
- The D-Lab, Department of Precision Medicine, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Marc B. I. Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (A.I.); (S.P.); (M.B.I.L.); (T.J.A.v.N.); (H.C.W.)
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
- Department of Medical Imaging, Zuyderland Medical Center, P.O. Box 5500, 6130 MB Sittard-Geleen, The Netherlands
| | - Regina G. H. Beets-Tan
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
- Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Thiemo J. A. van Nijnatten
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (A.I.); (S.P.); (M.B.I.L.); (T.J.A.v.N.); (H.C.W.)
| | - Sanne M. E. Engelen
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (S.S.); (S.M.E.E.); (M.L.S.)
| | - Henry C. Woodruff
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (A.I.); (S.P.); (M.B.I.L.); (T.J.A.v.N.); (H.C.W.)
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
- The D-Lab, Department of Precision Medicine, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Marjolein L. Smidt
- Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (S.S.); (S.M.E.E.); (M.L.S.)
- GROW-School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
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Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study. Cancers (Basel) 2021; 13:cancers13020352. [PMID: 33477893 PMCID: PMC7833376 DOI: 10.3390/cancers13020352] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 01/14/2021] [Accepted: 01/15/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients. Abstract In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.
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Song BI. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer 2021; 28:664-671. [PMID: 33454875 DOI: 10.1007/s12282-020-01202-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/02/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS A total of 100 consecutive IDC patients who underwent surgical resection of primary tumor with sentinel lymph-node biopsy and/or ALN dissection without any neoadjuvant treatment were analyzed. Volume of interests (VOIs) were drawn more than 2.5 of standardized uptake value in the primary tumor on the PET scan using 3D slicer. Pyradiomics package was used for the extraction of texture features in python. The radiomics prediction model for ALN metastasis was developed in 75 patients of the training cohort and validated in 25 patients of the test cohort. XGBoost algorithm was utilized to select features and build radiomics model. The sensitivity, specificity, and accuracy of the predictive model were calculated. RESULTS ALN metastasis was found in 43 patients (43%). The sensitivity, specificity, and accuracy of F-18 FDG PET/CT for the diagnosis of ALN metastasis in the entire patients were 55.8%, 93%, and 77%, respectively. The radiomics model for the prediction of ALN metastasis was successfully developed. The sensitivity, specificity, and accuracy of the radiomics model for the prediction of ALN metastasis in the test cohorts were 90.9%, 71.4%, and 80%, respectively. CONCLUSION The machine learning-based radiomics model showed good sensitivity for the prediction of ALN metastasis and could assist the preoperative individualized prediction of ALN status in patients with IDC.
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Affiliation(s)
- Bong-Il Song
- Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, Republic of Korea.
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Lee YW, Huang CS, Shih CC, Chang RF. Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks. Comput Biol Med 2020; 130:104206. [PMID: 33421823 DOI: 10.1016/j.compbiomed.2020.104206] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/28/2020] [Accepted: 12/28/2020] [Indexed: 11/25/2022]
Abstract
Deep learning (DL) algorithms have been proven to be very effective in a wide range of computer vision applications, such as segmentation, classification, and detection. DL models can automatically assess complex medical image scenes without human intervention and can be applied as a second reader to provide an additional opinion for the physician. To predict the axillary lymph node (ALN) metastatic status in patients with early-stage breast cancer, a deep learning-based computer-aided prediction system for ultrasound (US) images was proposed. A total of 153 women with breast tumor US images were involved in this study; there were 59 patients with metastasis and 94 patients without ALN metastasis. A deep learning-based computer-aided prediction (CAP) system using the tumor region and peritumoral tissue in ultrasound (US) images were employed to determine the ALN status in breast cancer. First, we adopted Mask R-CNN as our tumor detection and segmentation model to obtain the tumor localization and region. Second, the peritumoral tissue was extracted from the US image, which reflects metastatic progression. Third, we used the DL model to predict ALN metastasis. Finally, the simple linear iterative clustering (SLIC) superpixel segmentation method and the LIME explanation algorithm were employed to explain how the model makes decisions. The experimental results indicated that the DL model had the best prediction performance on tumor regions with 3 mm thick peritumoral tissue, and the accuracy, sensitivity, specificity, and AUC were 81.05% (124/153), 81.36% (48/59), 80.85% (76/94), and 0.8054, respectively. The results indicated that the proposed CAP system could help determine the ALN status in patients with early-stage breast cancer. The results reveal that the proposed CAP model, which combines primary tumor and peritumoral tissue, is an effective method to predict the ALN status in patients with early-stage breast cancer.
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Affiliation(s)
- Yan-Wei Lee
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617, Taiwan
| | - Chiun-Sheng Huang
- Department of Surgery, College of Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, 100, Taiwan
| | - Chung-Chih Shih
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, 10617, Taiwan; MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, 10617, Taiwan.
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Caballo M, Pangallo DR, Sanderink W, Hernandez AM, Lyu SH, Molinari F, Boone JM, Mann RM, Sechopoulos I. Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging. Med Phys 2020; 48:313-328. [PMID: 33232521 PMCID: PMC7898616 DOI: 10.1002/mp.14610] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 09/07/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose To develop and evaluate the diagnostic performance of an algorithm for multi‐marker radiomic‐based classification of breast masses in dedicated breast computed tomography (bCT) images. Methods Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well‐established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single‐feature‐based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple‐step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). Results The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80–0.96). Conclusions The proposed multi‐marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.
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Affiliation(s)
- Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Domenico R Pangallo
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands.,Biolab, Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy
| | - Wendelien Sanderink
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Andrew M Hernandez
- Department of Radiology, University of California Davis, Sacramento, CA, 95817, USA
| | - Su Hyun Lyu
- Department of Biomedical Engineering, University of California Davis, Sacramento, CA, 95817, USA
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunication, Politecnico di Torino, Torino, 10129, Italy
| | - John M Boone
- Department of Radiology, University of California Davis, Sacramento, CA, 95817, USA.,Department of Biomedical Engineering, University of California Davis, Sacramento, CA, 95817, USA
| | - Ritse M Mann
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands.,Dutch Expert Center for Screening (LRCB), PO Box 6873, Nijmegen, 6503 GJ, The Netherlands
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Lee SH, Park H, Ko ES. Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review. Korean J Radiol 2020; 21:779-792. [PMID: 32524780 PMCID: PMC7289696 DOI: 10.3348/kjr.2019.0855] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/31/2020] [Accepted: 02/28/2020] [Indexed: 02/06/2023] Open
Abstract
Recent advances in computer technology have generated a new area of research known as radiomics. Radiomics is defined as the high throughput extraction and analysis of quantitative features from imaging data. Radiomic features provide information on the gray-scale patterns, inter-pixel relationships, as well as shape and spectral properties of radiological images. Moreover, these features can be used to develop computational models that may serve as a tool for personalized diagnosis and treatment guidance. Although radiomics is becoming popular and widely used in oncology, many problems such as overfitting and reproducibility issues remain unresolved. In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues.
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Affiliation(s)
- Seung Hak Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea.,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
| | - Eun Sook Ko
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Qiu X, Jiang Y, Zhao Q, Yan C, Huang M, Jiang T. Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer? JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2020; 39:1897-1905. [PMID: 32329142 PMCID: PMC7540260 DOI: 10.1002/jum.15294] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/12/2020] [Accepted: 03/25/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVES This work aimed to investigate whether quantitative radiomics imaging features extracted from ultrasound (US) can noninvasively predict breast cancer (BC) metastasis to axillary lymph nodes (ALNs). METHODS Presurgical B-mode US data of 196 patients with BC were retrospectively studied. The cases were divided into the training and validation cohorts (n = 141 versus 55). The elastic net regression technique was used for selecting features and building a signature in the training cohort. A linear combination of the selected features weighted by their respective coefficients produced a radiomics signature for each individual. A radiomics nomogram was established based on the radiomics signature and US-reported ALN status. In a receiver operating characteristic curve analysis, areas under the curves (AUCs) were determined for assessing the accuracy of the prediction model in predicting ALN metastasis in both cohorts. The clinical value was assessed by a decision curve analysis. RESULTS In all, 843 radiomics features per case were obtained from expert-delineated lesions on US imaging in this study. Through radiomics feature selection, 21 features were selected to constitute the radiomics signature for predicting ALN metastasis. Area under the curve values of 0.778 and 0.725 were obtained in the training and validation cohorts, respectively, indicating moderate predictive ability. The radiomics nomogram comprising the radiomics signature and US-reported ALN status showed the best performance for ALN detection in the training cohort (AUC, 0.816) but moderate performance in the validation cohort (AUC, 0.759). The decision curve showed that both the radiomics signature and nomogram displayed good clinical utility. CONCLUSIONS This pilot radiomics study provided a noninvasive method for predicting presurgical ALN metastasis status in BC.
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Affiliation(s)
- Xiaoying Qiu
- Departments of UltrasonographyFirst Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
| | - Yongluo Jiang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineSun Yat‐sen University Cancer CenterGuangzhouChina
| | - Qiyu Zhao
- Departments of UltrasonographyFirst Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
- Hepatobiliary and Pancreatic SurgeryFirst Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
| | - Chunhong Yan
- Departments of UltrasonographyFirst Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
| | - Min Huang
- Departments of UltrasonographyFirst Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
| | - Tian'an Jiang
- Departments of UltrasonographyFirst Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
- Hepatobiliary and Pancreatic SurgeryFirst Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
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Liu M, Mao N, Ma H, Dong J, Zhang K, Che K, Duan S, Zhang X, Shi Y, Xie H. Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer. Cancer Imaging 2020; 20:65. [PMID: 32933585 PMCID: PMC7493182 DOI: 10.1186/s40644-020-00342-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 09/02/2020] [Indexed: 12/13/2022] Open
Abstract
Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.
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Affiliation(s)
- Meijie Liu
- School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000.,Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Jianjun Dong
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000
| | | | - Xuexi Zhang
- GE Healthcare, China, Shanghai, P. R. China, 200000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000.
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, No. 20 Yuhuangding road, Yantai, Shandong, P. R. China, 264000.
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Tan H, Gan F, Wu Y, Zhou J, Tian J, Lin Y, Wang M. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence. Acad Radiol 2020; 27:1217-1225. [PMID: 31879160 DOI: 10.1016/j.acra.2019.11.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/01/2019] [Accepted: 11/03/2019] [Indexed: 12/13/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of radiomics method based on the fat-suppressed T2 sequence for preoperative predicting axillary lymph node (ALN) metastasis in breast carcinoma. MATERIALS AND METHODS The data of 329 invasive breast cancer patients were divided into the primary cohort (n = 269) and validation cohort (n = 60). Radiomics features were extracted from the fat-suppressed T2-weighted images on breast MRI, and ALN metastasis-related radiomics feature selection was performed using Mann-Whitney U-test and support vector machines with recursive feature elimination; then a radiomics signature was constructed by linear support vector machine. The predictive models were constructed using a linear regression model based on the clinicopathologic factors and radiomics signature, and nomogram was used for a visual prediction of the combined model. The predictive performances are evaluated with the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. RESULTS A total of 647 radiomics features were extracted from each patient. About 23 ALN metastasis-related radiomics features were selected to construct the radiomics signature, including 17 texture features, 5 first-order statistical features, and one shape feature; patient age, tumor size, HER2 status, and vascular cancer thrombus accompanied or not were selected to construct the cilinicopathologic feature model. The sensitivity, specificity, accuracy, and are under the curve value of radiomics signature, clinicopathologic feature model, and the nomogram were 65.22%, 81.08%, 75.00%, and 0.819 (95% confidence interval [CI]: 0.776-0.861), 30.44%, 81.08%, 61.67%, and 0.605 (95% CI: 0.571-0.624) and 60.87%, 89.19%, 78.33%, and 0.810 (95% CI: 0.761-0.855), respectively. CONCLUSION Radiomics methods based on the fat-suppressed T2 sequence and the nomogram are helpful for preoperative accurate predicting ALN metastasis.
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Affiliation(s)
- Hongna Tan
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, 7 Road, Weiwu Road, Jinshui District, Zhengzhou 450003, Henan, China
| | - Fuwen Gan
- Collaborative Innovation Center for Internet Healthcare & School of Information Engineering, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Yaping Wu
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, 7 Road, Weiwu Road, Jinshui District, Zhengzhou 450003, Henan, China
| | - Jing Zhou
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, 7 Road, Weiwu Road, Jinshui District, Zhengzhou 450003, Henan, China
| | - Jie Tian
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yusong Lin
- Collaborative Innovation Center for Internet Healthcare & School of Information Engineering, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, 7 Road, Weiwu Road, Jinshui District, Zhengzhou 450003, Henan, China.
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MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability. Sci Rep 2020; 10:14163. [PMID: 32843663 PMCID: PMC7447771 DOI: 10.1038/s41598-020-70940-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 07/31/2020] [Indexed: 12/11/2022] Open
Abstract
Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19–0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability.
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Jiang X, Zou X, Sun J, Zheng A, Su C. A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer. CONTRAST MEDIA & MOLECULAR IMAGING 2020; 2020:5418364. [PMID: 32922222 PMCID: PMC7468630 DOI: 10.1155/2020/5418364] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/06/2020] [Indexed: 02/05/2023]
Abstract
Objectives To develop and validate a radiomics-based nomogram with texture features from mammography for the prognostic prediction in patients with early-stage triple-negative breast cancer (TNBC). Methods The study included 200 consecutive patients with TNBC (training cohort: n = 133, validation cohort: n = 67). A total of 136 mammography-derived textural features were extracted, and LASSO (least absolute shrinkage and selection operator) was applied to select features for building the radiomics score (Rad-score). After univariate and multivariate logistic regression, a radiomics-based nomogram was constructed with independent prognostic factors. The discrimination and calibration power were assessed, and further the clinical applicability of the nomograms was evaluated. Results Among the 136 mammography-derived textural features, fourteen were used to build the Rad-score after LASSO regression. A radiomics nomogram that incorporates Rad-score and pN stage was constructed. This nomogram achieved a C-index of 0.873 (95% CI: 0.758-0.989) for predicting iDFS (invasive disease-free survival), which outperformed the clinical model. Moreover, it is feasible to stratify patients into high-risk and low-risk groups based on the optimal cut-off point of Rad-score. The validations of the nomogram confirmed favorable discrimination and considerable predictive efficiency. Conclusions The radiomics nomogram that incorporates Rad-score and pN stage exhibited favorable performance in the prediction of iDFS in patients with early-stage TNBCs.
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Affiliation(s)
- Xian Jiang
- Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, China
| | - Xiuhe Zou
- Department of Thyroid Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Sun
- Department of Integrated Chinese and Western Medicine, Qingdao Central Hospital, Qingdao University, Qingdao, Shandong, China
| | - Aiping Zheng
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Chao Su
- State Key Laboratory of Biotherapy and Cancer Center/Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study. Eur Radiol 2020; 30:6732-6739. [DOI: 10.1007/s00330-020-07016-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 04/06/2020] [Accepted: 06/05/2020] [Indexed: 12/13/2022]
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Tan H, Wu Y, Bao F, Zhou J, Wan J, Tian J, Lin Y, Wang M. Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer. Br J Radiol 2020; 93:20191019. [PMID: 32401540 PMCID: PMC7336077 DOI: 10.1259/bjr.20191019] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. METHODS 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. RESULTS 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591-0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts). CONCLUSION The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making. ADVANCES IN KNOWLEDGE ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.
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Affiliation(s)
- Hongna Tan
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Yaping Wu
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Fengchang Bao
- Department of Hematology, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Jing Zhou
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
| | - Jianzhong Wan
- Collaborative Innovation Center for Internet Healthcare & School of Software, Zhengzhou University, Zhengzhou, Henan, China, 450052
| | - Jie Tian
- Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
| | - Yusong Lin
- Collaborative Innovation Center for Internet Healthcare & School of Software, Zhengzhou University, Zhengzhou, Henan, China, 450052
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, Henan, China, 450003
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Liu Z, Li R, Liang K, Chen J, Chen X, Li X, Li R, Zhang X, Yi L, Long W. Value of digital mammography in predicting lymphovascular invasion of breast cancer. BMC Cancer 2020; 20:274. [PMID: 32245448 PMCID: PMC7119272 DOI: 10.1186/s12885-020-6712-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 03/03/2020] [Indexed: 12/15/2022] Open
Abstract
Background Lymphovascular invasion (LVI) has never been revealed by preoperative scans. It is necessary to use digital mammography in predicting LVI in patients with breast cancer preoperatively. Methods Overall 122 cases of invasive ductal carcinoma diagnosed between May 2017 and September 2018 were enrolled and assigned into the LVI positive group (n = 42) and the LVI negative group (n = 80). Independent t-test and χ2 test were performed. Results Difference in Ki-67 between the two groups was statistically significant (P = 0.012). Differences in interstitial edema (P = 0.013) and skin thickening (P = 0.000) were statistically significant between the two groups. Multiple factor analysis showed that there were three independent risk factors for LVI: interstitial edema (odds ratio [OR] = 12.610; 95% confidence interval [CI]: 1.061–149.922; P = 0.045), blurring of subcutaneous fat (OR = 0.081; 95% CI: 0.012–0.645; P = 0.017) and skin thickening (OR = 9.041; 95% CI: 2.553–32.022; P = 0.001). Conclusions Interstitial edema, blurring of subcutaneous fat, and skin thickening are independent risk factors for LVI. The specificity of LVI prediction is as high as 98.8% when the three are used together.
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Affiliation(s)
- Zhuangsheng Liu
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China
| | - Ruqiong Li
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China
| | - Keming Liang
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China
| | - Junhao Chen
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China
| | - Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China
| | - Xiaoping Li
- Department of Gastrointestinal Surgery, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, Guangdong, China
| | - Ronggang Li
- Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, Guangdong, China
| | - Xin Zhang
- Department of Clinical Experimental Center, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, Guangdong, China
| | - Lilei Yi
- Department of Radiology, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China.
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Wang Z, Cheng Y, Chen S, Shao H, Chen X, Wang Z, Wang Y, Zhou H, Chen T, Lin N, Ye Z. Novel prognostic nomograms for female patients with breast cancer and bone metastasis at presentation. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:197. [PMID: 32309344 PMCID: PMC7154431 DOI: 10.21037/atm.2020.01.37] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background There is a paucity of literature about prognostic evaluation for patients with breast cancer (BC) and bone metastasis at presentation. To date, little is known about how to accurately predict the prognosis of BC patients with bone metastasis at presentation. Thus, an accurate prediction tool of prognosis in this population is urgently needed. Our goal is to construct novel and prognostic nomograms for BC patients with bone metastasis at presentation. Methods We searched Surveillance, Epidemiology, and End Results (SEER) database for BC patients with bone metastasis at presentation between 2010 and 2016. Multivariate analysis was performed to obtain significantly independent variables. Then, novel prognostic nomograms were constructed based on those independent predictors. Results Tumor grade, histological type, primary tumor size, tumor subtype, surgery, chemotherapy and number of metastatic organs except bone were recognized as significantly independent variables of both overall survival (OS) and cancer-specific survival (CSS). Then those significant variables were integrated to construct nomograms for 3- and 5-year survival. Calibration plots for the 3- and 5-year survival in training and validation sets showed that the prediction curve was close to a 45 degree slash. The C-indices of OS in training and validation cohorts were 0.705 and 0.678, respectively. Similar results were observed for CSS in training and validation cohorts. Conclusions Our proposed nomograms can effectively and accurately predict the prognosis of BC patients with bone metastasis at presentation, which provide a basis for individual treatments for metastatic lesions.
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Affiliation(s)
- Zhan Wang
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Yonggang Cheng
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Shi Chen
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China.,Department of Orthopaedics, Ninghai First Hospital, Ninghai 315600, China
| | - Haiyu Shao
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Xiaowei Chen
- Department of Orthopaedics, Jingning Shezu Autonomous County People's Hospital, Lishui 323500, China
| | - Zenan Wang
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Yucheng Wang
- Graduate School of Hebei North University, Zhangjiakou 075000, China
| | - Hao Zhou
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Tao Chen
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Nong Lin
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Zhaoming Ye
- Department of Orthopaedics, Centre for Orthopaedic Research, Orthopedics Research Institute of Zhejiang University, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
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Marino MA, Avendano D, Zapata P, Riedl CC, Pinker K. Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools. Oncologist 2019; 25:e231-e242. [PMID: 32043792 PMCID: PMC7011661 DOI: 10.1634/theoncologist.2019-0427] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 08/12/2019] [Indexed: 12/26/2022] Open
Abstract
The detection of lymph node metastasis affects the management of patients with primary breast cancer significantly in terms of staging, treatment, and prognosis. The main goal for the radiologist is to determine and detect the presence of metastatic disease in nonpalpable axillary lymph nodes with a positive predictive value that is high enough to initially select patients for upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with different imaging modalities, but ultrasound is the method of choice for evaluating axillary lymph nodes and for performing image-guided lymph node interventions. This review aims to provide a comprehensive overview of the available imaging modalities for lymph node assessment in patients diagnosed with primary breast cancer. IMPLICATIONS FOR PRACTICE: The detection of lymph node metastasis affects the management of patients with primary breast cancer. The main goal for the radiologist is to detect lymph node metastasis in patients to allow for the selection of patients who should undergo upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with mammography, computed tomography, and magnetic resonance imaging, but ultrasonography is the imaging modality of choice for evaluating axillary lymph nodes. A normal axillary lymph node is characterized by a reniform shape, a maximal cortical thickness of 3 mm without focal bulging, smooth margins, and, depending on size, a discernable central fatty hilum.
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Affiliation(s)
- Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of MessinaMessinaItaly
| | - Daly Avendano
- Department of Breast Imaging, Breast Cancer Center TecSalud, Instituto Tecnológico de Estudios Superiores (ITESM) MonterreyNuevo LeonMexico
| | - Pedro Zapata
- Department of Radiology, San Felipe de Jesus HospitalMonterreyNuevo LeonMexico
| | - Christopher C. Riedl
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
- Molecular and Gender Imaging Service, Department of Biomedical Imaging and Image‐guided Therapy, Medical University of ViennaViennaAustria
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