1
|
Foffano L, Vida R, Piacentini A, Molteni E, Cucciniello L, Da Ros L, Silvia B, Cereser L, Roncato R, Gerratana L, Puglisi F. Is ctDNA ready to outpace imaging in monitoring early and advanced breast cancer? Expert Rev Anticancer Ther 2024; 24:679-691. [PMID: 38855809 DOI: 10.1080/14737140.2024.2362173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024]
Abstract
INTRODUCTION Circulating tumor DNA (ctDNA) and radiological imaging are increasingly recognized as crucial elements in breast cancer management. While radiology remains the cornerstone for screening and monitoring, ctDNA holds distinctive advantages in anticipating diagnosis, recurrence, or progression, providing concurrent biological insights complementary to imaging results. AREAS COVERED This review delves into the current evidence on the synergistic relationship between ctDNA and imaging in breast cancer. It presents data on the clinical validity and utility of ctDNA in both early and advanced settings, providing insights into emerging liquid biopsy techniques like epigenetics and fragmentomics. Simultaneously, it explores the present and future landscape of imaging methodologies, particularly focusing on radiomics. EXPERT OPINION Numerous are the current technical, strategic, and economic challenges preventing the clinical integration of ctDNA analysis in the breast cancer monitoring. Understanding these complexities and devising targeted strategies is pivotal to effectively embedding this methodology into personalized patient care.
Collapse
Affiliation(s)
- Lorenzo Foffano
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Riccardo Vida
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | | | - Elisabetta Molteni
- Department of Medicine, University of Udine, Udine, Italy
- Weill Cornell Medicine, Department of Medicine, Division of Hematology-Oncology, New York, NY, USA
| | - Linda Cucciniello
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Lucia Da Ros
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Buriolla Silvia
- Department of Oncology, Santa Maria della Misericordia University Hospital, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy
| | - Lorenzo Cereser
- Department of Medicine, University of Udine, Udine, Italy
- Azienda Sanitaria-Universitaria Friuli Centrale (ASUFC), University Hospital S. Maria della Misericordia, Udine, Italy
| | | | - Lorenzo Gerratana
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Fabio Puglisi
- Department of Medicine, University of Udine, Udine, Italy
- Department of Medical Oncology, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| |
Collapse
|
2
|
Pecchi A, Bozzola C, Beretta C, Besutti G, Toss A, Cortesi L, Balboni E, Nocetti L, Ligabue G, Torricelli P. DCE-MRI Radiomic analysis in triple negative ductal invasive breast cancer. Comparison between BRCA and not BRCA mutated patients: Preliminary results. Magn Reson Imaging 2024:110214. [PMID: 39047852 DOI: 10.1016/j.mri.2024.110214] [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: 05/13/2024] [Revised: 07/17/2024] [Accepted: 07/20/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVE The research aimed to determine whether and which radiomic features from breast dynamic contrast enhanced (DCE) MRI could predict the presence of BRCA1 mutation in patients with triple-negative breast cancer (TNBC). MATERIAL AND METHODS This retrospective study included consecutive patients histologically diagnosed with TNBC who underwent breast DCE-MRI in 2010-2021. Baseline DCE-MRIs were retrospectively reviewed; percentage maps of wash-in and wash-out were computed and breast lesions were manually segmented, drawing a 5 mm-Region of Interest (ROI) inside the tumor and another 5 mm-ROI inside the contralateral healthy gland. Features for each map and each ROI were extracted with Pyradiomics-3D Slicer and considered first separately (tumor and contralateral gland) and then together. In each analysis the more important features for BRCA1 status classification were selected with Maximum Relevance Minimum Redundancy algorithm and used to fit four classifiers. RESULTS The population included 67 patients and 86 lesions (21 in BRCA1-mutated, 65 in non BRCA-carriers). The best classifiers for BRCA mutation were Support Vector Classifier and Logistic Regression in models fitted with both gland and tumor features, reaching an Area Under ROC Curve (AUC) of 0.80 (SD 0.21) and of 0.79 (SD 0.20), respectively. Three features were higher in BRCA1-mutated compared to non BRCA-mutated: Total Energy and Correlation from gray level cooccurrence matrix, both measured in contralateral gland in wash-out maps, and Root Mean Squared, selected from the wash-out map of the tumor. CONCLUSIONS This study showed the feasibility of a radiomic study with breast DCE-MRI and the potential of radiomics in predicting BRCA1 mutational status.
Collapse
Affiliation(s)
- Annarita Pecchi
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Chiara Bozzola
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Cecilia Beretta
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Giulia Besutti
- Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy; Radiology Unit, Department of Diagnostic Imaging and Laboratory Medicine, AUSL-IRCCS di Reggio Emilia, Via Risorgimento 80, 42123 Reggio Emilia, Italy.
| | - Angela Toss
- Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy; Department of Oncology and Haematology, Azienda Ospedaliero-Universitaria di Modena, Modena, Italy
| | - Laura Cortesi
- Department of Oncology and Haematology, Azienda Ospedaliero-Universitaria di Modena, Modena, Italy
| | - Erica Balboni
- Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy
| | - Luca Nocetti
- Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy
| | - Guido Ligabue
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| | - Pietro Torricelli
- Department of Radiology, University Hospital of Modena, 41224 Modena, Italy; Department of Medical and Surgical Sciences of Children and Adults, University Hospital of Modena, 41224 Modena, Italy
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Nemoto H, Saito M, Satoh Y, Komiyama T, Marino K, Aoki S, Suzuki H, Sano N, Nonaka H, Watanabe H, Funayama S, Onishi H. Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study. J Appl Clin Med Phys 2024; 25:e14322. [PMID: 38436611 PMCID: PMC11244675 DOI: 10.1002/acm2.14322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/14/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
PURPOSE Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT. METHODS Planning CT and PET images of 82 non-small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method. RESULTS The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC = 0.646), Naive Bayes with PET features (mean AUC = 0.611), and Support vector machine with CT features (mean AUC = 0.645), respectively. CONCLUSIONS We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies.
Collapse
Affiliation(s)
- Hikaru Nemoto
- Department of Advanced Biomedical ImagingUniversity of YamanashiChuoYamanashiJapan
- Department of RadiologyUniversity of YamanashiChuoYamanashiJapan
| | - Masahide Saito
- Department of RadiologyUniversity of YamanashiChuoYamanashiJapan
| | - Yoko Satoh
- Imaging CenterFujita Medical Innovation Center TokyoTokyoJapan
| | | | - Kan Marino
- Department of RadiologyUniversity of YamanashiChuoYamanashiJapan
| | - Shinichi Aoki
- Department of RadiologyUniversity of YamanashiChuoYamanashiJapan
| | - Hidekazu Suzuki
- Department of RadiologyUniversity of YamanashiChuoYamanashiJapan
| | - Naoki Sano
- Department of RadiologyUniversity of YamanashiChuoYamanashiJapan
| | - Hotaka Nonaka
- Department of RadiologyFuji City General HospitalFujiShizuokaJapan
| | - Hiroaki Watanabe
- Department of RadiologyYamanashi Central HospitalKofuYamanashiJapan
| | - Satoshi Funayama
- Department of RadiologyHamamatsu University school of medicineHamamatsuShizuokaJapan
| | - Hiroshi Onishi
- Department of RadiologyUniversity of YamanashiChuoYamanashiJapan
| |
Collapse
|
5
|
Li X, Yang L, Jiang F, Jiao X. Integration of Radiomics and Immune-Related Genes Signatures for Predicting Axillary Lymph Node Metastasis in Breast Cancer. Clin Breast Cancer 2024:S1526-8209(24)00179-4. [PMID: 39019727 DOI: 10.1016/j.clbc.2024.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/19/2024]
Abstract
BACKGROUND To develop a radiogenomics nomogram for predicting axillary lymph node (ALN) metastasis in breast cancer and reveal underlying associations between radiomics features and biological pathways. MATERIALS AND METHODS This study included 1062 breast cancer patients, 90 patients with both DCE-MRI and gene expression data. The optimal immune-related genes and radiomics features associated with ALN metastasis were firstly calculated, and corresponding feature signatures were constructed to further validate their performances in predicting ALN metastasis. The radiogenomics nomogram for predicting the risk of ALN metastasis was established by integrating radiomics signature, immune-related genes (IRG) signature, and critical clinicopathological factors. Gene modules associated with key radiomics features were identified by weighted gene co-expression network analysis (WGCNA) and submitted to functional enrichment analysis. Gene set variation analysis (GSVA) and correlation analysis were performed to investigate the associations between radiomics features and biological pathways. RESULTS The radiogenomics nomogram showed promising predictive power for predicting ALN metastasis, with AUCs of 0.973 and 0.928 in the training and testing groups, respectively. WGCNA and functional enrichment analysis revealed that gene modules associated with key radiomics features were mainly enriched in breast cancer metastasis-related pathways, such as focal adhesion, ECM-receptor interaction, and cell adhesion molecules. GSVA also identified pathway activities associated with radiomics features such as glycogen synthesis, integration of energy metabolism. CONCLUSION The radiogenomics nomogram can serve as an effective tool to predict the risk of ALN metastasis. This study provides further evidence that radiomics phenotypes may be driven by biological pathways related to breast cancer metastasis.
Collapse
Affiliation(s)
- Xue Li
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi, China
| | - Lifeng Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong, Shanxi, China
| | - Fa Jiang
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi, China
| | - Xiong Jiao
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi, China.
| |
Collapse
|
6
|
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.
Collapse
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
| | | |
Collapse
|
7
|
Kim J, Choi YH, Yoon H, Lim HJ, Han JW, Lee MJ. Prediction of High-Risk Neuroblastoma Among Neuroblastic Tumors Using Radiomics Features Derived from Magnetic Resonance Imaging: A Pilot Study. Yonsei Med J 2024; 65:293-301. [PMID: 38653568 PMCID: PMC11045346 DOI: 10.3349/ymj.2023.0192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE This study aimed to predict high-risk neuroblastoma among neuroblastic tumors using radiomics features extracted from MRI. MATERIALS AND METHODS Pediatric patients (age≤18 years) diagnosed with neuroblastic tumors who had pre-treatment MR images available were enrolled from institution A from January 2010 to November 2019 (training set) and institution B from January 2016 to January 2022 (test set). Segmentation was performed with regions of interest manually drawn along tumor margins on the slice with the widest tumor area by two radiologists. First-order and texture features were extracted and intraclass correlation coefficients (ICCs) were calculated. Multivariate logistic regression (MLR) and random forest (RF) models from 10-fold cross-validation were built using these features. The trained MLR and RF models were tested in an external test set. RESULTS Thirty-two patients (M:F=23:9, 26.0±26.7 months) were in the training set and 14 patients (M:F=10:4, 33.4±20.4 months) were in the test set with radiomics features (n=930) being extracted. For 10 of the most relevant features selected, intra- and inter-observer variability was moderate to excellent (ICCs 0.633-0.911, 0.695-0.985, respectively). The area under the receiver operating characteristic curve (AUC) was 0.94 (sensitivity 67%, specificity 91%, and accuracy 84%) for the MLR model and the average AUC was 0.83 (sensitivity 44%, specificity 87%, and accuracy 75%) for the RF model from 10-fold cross-validation. In the test set, AUCs of the MLR and RF models were 0.94 and 0.91, respectively. CONCLUSION An MRI-based radiomics model can help predict high-risk neuroblastoma among neuroblastic tumors.
Collapse
Affiliation(s)
- Jisoo Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Haesung Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyun Ji Lim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jung Woo Han
- Department of Pediatric Hematology-Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea
| | - Mi-Jung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Yao J, Zhou W, Zhu Y, Zhou J, Chen X, Zhan W. Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer. Oncol Lett 2024; 27:95. [PMID: 38288042 PMCID: PMC10823315 DOI: 10.3892/ol.2024.14228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 12/19/2023] [Indexed: 01/31/2024] Open
Abstract
Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.
Collapse
Affiliation(s)
- Jiejie Yao
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Wei Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Ying Zhu
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Xiaosong Chen
- Comprehensive Breast Health Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| |
Collapse
|
10
|
Chen Y, Li J, Zhang J, Yu Z, Jiang H. Radiomic Nomogram for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer. Acad Radiol 2024; 31:788-799. [PMID: 37932165 DOI: 10.1016/j.acra.2023.10.026] [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: 08/30/2023] [Revised: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023]
Abstract
RATIONALE AND OBJECTIVES The detection of axillary lymph node metastasis (ALNM) in patients with breast cancer is a crucial determinant in the decision-making process for axillary surgery and potential therapies. The objective of this study was to develop and validate a radiomics nomogram that integrates radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with clinical factors to predict ALNM in patients with breast cancer. MATERIALS AND METHODS A total of 177 patients with breast cancer were randomly divided into a training set (n = 123) and a validation set (n = 54) using a 7:3 ratio. From the DCE-MRI images, 2818 radiomics features were extracted from the primary tumor and axillary lymph node (ALN). Subsequently, optimal features were selected through the least absolute shrinkage and selection operator algorithm to construct the Radscore. Clinical factors were identified using univariate logistic regression analysis and included in a multivariate logistic regression analysis. Using the Radscore and clinical factors, a radiomics nomogram was developed using the Support Vector Machine method. The predicting efficacy of our model was visually appraised utilizing a receiver operator characteristic (ROC) curve, while its clinical application and predictive accuracy were assessed through decision curve analysis (DCA) and calibration curves, respectively. RESULTS The results revealed Ki67, multifocality, and MRI-reported ALN status as independent risk factors for ALNM. The radiomics nomogram demonstrated good calibration and discrimination with areas under the ROC curve of 0.92 (95% confidence interval [CI], 0.88-0.97) in the training set and 0.90 (95% CI, 0.72-0.90) in the validation set. DCA revealed the clinical usefulness of the radiomics nomogram. CONCLUSION The DCE-MRI-based radiomics nomogram is a reliable tool for assessing ALNM in patients with breast cancer.
Collapse
Affiliation(s)
- Yusi Chen
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (Y.C., J.L., J.Z., H.J.)
| | - Jinping Li
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (Y.C., J.L., J.Z., H.J.)
| | - Jin Zhang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (Y.C., J.L., J.Z., H.J.)
| | - Zhuo Yu
- Huiying Medical Technology Co., Ltd, Beijing City 100192, China (Z.Y.)
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China (Y.C., J.L., J.Z., H.J.).
| |
Collapse
|
11
|
Al-Hayali A, Komeili A, Azad A, Sathiadoss P, Schieda N, Ukwatta E. Machine learning based prediction of image quality in prostate MRI using rapid localizer images. J Med Imaging (Bellingham) 2024; 11:026001. [PMID: 38435711 PMCID: PMC10905647 DOI: 10.1117/1.jmi.11.2.026001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 10/17/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024] Open
Abstract
Purpose Diagnostic performance of prostate MRI depends on high-quality imaging. Prostate MRI quality is inversely proportional to the amount of rectal gas and distention. Early detection of poor-quality MRI may enable intervention to remove gas or exam rescheduling, saving time. We developed a machine learning based quality prediction of yet-to-be acquired MRI images solely based on MRI rapid localizer sequence, which can be acquired in a few seconds. Approach The dataset consists of 213 (147 for training and 64 for testing) prostate sagittal T2-weighted (T2W) MRI localizer images and rectal content, manually labeled by an expert radiologist. Each MRI localizer contains seven two-dimensional (2D) slices of the patient, accompanied by manual segmentations of rectum for each slice. Cascaded and end-to-end deep learning models were used to predict the quality of yet-to-be T2W, DWI, and apparent diffusion coefficient (ADC) MRI images. Predictions were compared to quality scores determined by the experts using area under the receiver operator characteristic curve and intra-class correlation coefficient. Results In the test set of 64 patients, optimal versus suboptimal exams occurred in 95.3% (61/64) versus 4.7% (3/64) for T2W, 90.6% (58/64) versus 9.4% (6/64) for DWI, and 89.1% (57/64) versus 10.9% (7/64) for ADC. The best performing segmentation model was 2D U-Net with ResNet-34 encoder and ImageNet weights. The best performing classifier was the radiomics based classifier. Conclusions A radiomics based classifier applied to localizer images achieves accurate diagnosis of subsequent image quality for T2W, DWI, and ADC prostate MRI sequences.
Collapse
Affiliation(s)
- Abdullah Al-Hayali
- University of Guelph, School of Engineering, Guelph Imaging AI Lab, Guelph, Ontario, Canada
| | - Amin Komeili
- University of Calgary, Department of Biomedical Engineering, Calgary, Alberta, Canada
| | - Azar Azad
- A.I. Vali Inc., Toronto, Ontario, Canada
| | - Paul Sathiadoss
- University of Ottawa, Department of Radiology, Ottawa, Ontario, Canada
| | - Nicola Schieda
- University of Ottawa, Department of Radiology, Ottawa, Ontario, Canada
| | - Eranga Ukwatta
- University of Guelph, School of Engineering, Guelph Imaging AI Lab, Guelph, Ontario, Canada
| |
Collapse
|
12
|
Cao K, Gao F, Long R, Zhang FD, Huang CC, Cao M, Yu YZ, Sun YS. Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:583-596. [PMID: 38306089 DOI: 10.3233/xst-230332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
PURPOSE The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. RESULTS Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. CONCLUSIONS The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.
Collapse
Affiliation(s)
- Kun Cao
- Department of Radiology, Peking University Cancer Hospital and Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China
| | - Fei Gao
- AI Lab, Deepwise and League of PhD Technology Co. LTD, Beijing, China
| | - Rong Long
- Department of Radiology, Peking University Cancer Hospital and Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China
| | - Fan-Dong Zhang
- AI Lab, Deepwise and League of PhD Technology Co. LTD, Beijing, China
| | - Chen-Cui Huang
- AI Lab, Deepwise and League of PhD Technology Co. LTD, Beijing, China
| | - Min Cao
- Department of Radiology, Peking University Cancer Hospital and Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China
| | - Yi-Zhou Yu
- AI Lab, Deepwise and League of PhD Technology Co. LTD, Beijing, China
| | - Ying-Shi Sun
- Department of Radiology, Peking University Cancer Hospital and Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China
| |
Collapse
|
13
|
Gullo RL, Partridge SC, Shin HJ, Thakur SB, Pinker K. Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring. AJR Am J Roentgenol 2024; 222:e2329933. [PMID: 37850579 PMCID: PMC11196747 DOI: 10.2214/ajr.23.29933] [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] [Indexed: 10/19/2023]
Abstract
DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.
Collapse
Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, University of Washington, Seattle, WA, USA 98109, USA
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, South Korea
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| |
Collapse
|
14
|
Campana A, Gandomkar Z, Giannotti N, Reed W. The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review. J Med Radiat Sci 2023; 70:462-478. [PMID: 37534540 PMCID: PMC10715343 DOI: 10.1002/jmrs.709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/16/2023] [Indexed: 08/04/2023] Open
Abstract
Radiomics is an emerging field that aims to extract and analyse a comprehensive set of quantitative features from medical images. This scoping review is focused on MRI-based radiomic features for the molecular profiling of breast tumours and the implications of this work for predicting patient outcomes. A thorough systematic literature search and outcome extraction were performed to identify relevant studies published in MEDLINE/PubMed (National Centre for Biotechnology Information), EMBASE and Scopus from 2015 onwards. The following information was retrieved from each article: study purpose, study design, extracted radiomic features, machine learning technique(s), sample size/characteristics, statistical result(s) and implications on patient outcomes. Based on the study purpose, four key themes were identified in the included 63 studies: tumour subtype classification (n = 35), pathologically complete response (pCR) prediction (n = 15), lymph node metastasis (LNM) detection (n = 7) and recurrence rate prediction (n = 6). In all four themes, reported accuracies widely varied among the studies, for example, area under receiver characteristics curve (AUC) for detecting LNM ranged from 0.72 to 0.91 and the AUC for predicting pCR ranged from 0.71 to 0.99. In all four themes, combining radiomic features with clinical data improved the predictive models. Preliminary results of this study showed radiomics potential to characterise the whole tumour heterogeneity, with clear implications for individual-targeted treatment. However, radiomics is still in the pre-clinical phase, currently with an insufficient number of large multicentre studies and those existing studies are often limited by insufficient methodological transparency and standardised workflow. Consequently, the clinical translation of existing studies is currently limited.
Collapse
Affiliation(s)
- Annalise Campana
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Ziba Gandomkar
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Nicola Giannotti
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| |
Collapse
|
15
|
Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
Collapse
Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
| |
Collapse
|
16
|
Zhao J, Zhang Q, Liu M, Zhao X. MRI-based radiomics approach for the prediction of recurrence-free survival in triple-negative breast cancer after breast-conserving surgery or mastectomy. Medicine (Baltimore) 2023; 102:e35646. [PMID: 37861556 PMCID: PMC10589522 DOI: 10.1097/md.0000000000035646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023] Open
Abstract
To explore the value of a radiomics signature and develop a nomogram combined with a radiomics signature and clinical factors for predicting recurrence-free survival in triple-negative breast cancer patients. We enrolled 151 patients from the cancer imaging archive who underwent preoperative contrast-enhanced magnetic resonance imaging. They were assigned to training, validation and external validation cohorts. Image features with coefficients not equal to zero in the 10-fold cross-validation were selected to generate a radiomics signature. Based on the optimal cutoff value of the radiomics signature determined by maximally selected log-rank statistics, patients were stratified into high- and low-risk groups in the training and validation cohorts. Kaplan-Meier survival analysis was performed for both groups. Kaplan-Meier survival distributions in these groups were compared using log-rank tests. Univariate and multivariate Cox regression analyses were used to construct clinical and combined models. Concordance index was used to assess the predictive performance of the 3 models. Calibration of the combined model was assessed using calibration curves. Four image features were selected to generate the radiomics signature. The Kaplan-Meier survival distributions of patients in the 2 groups were significantly different in the training (P < .001) and validation cohorts (P = .001). The C-indices of the radiomics model, clinical model, and combined model in the training and validation cohorts were 0.772, 0.700, 0.878, and 0.744, 0.574, 0.777, respectively. The C-indices of the radiomics model, clinical model, and combined model in the external validation cohort were 0.778, 0.733, 0.822, respectively. The calibration curves of the combined model showed good calibration. The radiomics signature can predict recurrence-free survival of patients with triple-negative breast cancer and improve the predictive performance of the clinical model.
Collapse
Affiliation(s)
- Jingwei Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qi Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Muqing Liu
- Department of Radiology, Chaoyang Central Hospital, Chaoyang, China
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
17
|
Wang Z, Zhang H, Lin F, Zhang R, Ma H, Shi Y, Yang P, Zhang K, Zhao F, Mao N, Xie H. Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study. Acad Radiol 2023; 30 Suppl 2:S133-S142. [PMID: 37088646 DOI: 10.1016/j.acra.2023.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 04/25/2023]
Abstract
RATIONALE AND OBJECTIVES This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis. MATERIALS AND METHODS A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly. RESULTS In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor + 10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor + 10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists. CONCLUSION The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.
Collapse
Affiliation(s)
- Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000; Institute of medical imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Ran Zhang
- Artificial Intelligence and Clinical Innovation Institute, Huiying Medical Technology Co., Ltd, P. R. China, 100192
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Ping Yang
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Feng Zhao
- School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000.
| |
Collapse
|
18
|
Chen Y, Wang L, Dong X, Luo R, Ge Y, Liu H, Zhang Y, Wang D. Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer. J Digit Imaging 2023; 36:1323-1331. [PMID: 36973631 PMCID: PMC10042410 DOI: 10.1007/s10278-023-00818-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/09/2023] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions were studied. The included patients were divided into two cohorts by time (training/testing cohort, n = 366/122). Deep learning features were extracted from diffusion-weighted imaging-quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI (DCE-MRI) by a pretrained neural network of DenseNet121. After the selection of both radiomic and clinicopathological features, deep learning signature and a nomogram were built for independent validation. Twenty-three deep learning features were automatically selected in the training cohort to establish the deep learning signature of ALNM. Three clinicopathological factors, including LN palpability (odds ratio (OR) = 6.04; 95% confidence interval (CI) = 3.06-12.54, P = 0.004), tumor size in MRI (OR = 1.45, 95% CI = 1.18-1.80, P = 0.104), and Ki-67 (OR = 1.01; 95% CI = 1.00-1.02, P = 0.099), were selected and combined with radiomic signature to build a combined nomogram. The nomogram showed excellent predictive ability for ALNM (AUC 0.80 and 0.71 in training and testing cohorts, respectively). The sensitivity, specificity, and accuracy were 65%, 80%, and 75%, respectively, in the testing cohort. MRI-based deep learning radiomics in patients with breast cancer could be used to predict ALNM, providing a noninvasive approach to structuring the treatment strategy.
Collapse
Affiliation(s)
- Yanhong Chen
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Lijun Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Xue Dong
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Ran Luo
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Yaqiong Ge
- Department of Medicine, GE Healthcare, No. 1, Huatuo Road, 210000, Shanghai, China
| | - Huanhuan Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Yuzhen Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China.
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China.
| |
Collapse
|
19
|
Xu YH, Lu P, Gao MC, Wang R, Li YY, Song JX. Progress of magnetic resonance imaging radiomics in preoperative lymph node diagnosis of esophageal cancer. World J Radiol 2023; 15:216-225. [PMID: 37545645 PMCID: PMC10401402 DOI: 10.4329/wjr.v15.i7.216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/11/2023] [Accepted: 06/30/2023] [Indexed: 07/24/2023] Open
Abstract
Esophageal cancer, also referred to as esophagus cancer, is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China. Accurately determining the status of lymph nodes is crucial for developing treatment plans, defining the scope of intraoperative lymph node dissection, and ascertaining the prognosis of patients with esophageal cancer. Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging (MRI) have improved the effectiveness of MRI for assessing lymph node involvement, making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting. Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed. The features, such as shape, texture, and waveform, are associated with the cancer phenotype and tumor microenvironment. When these features correlate with the clinical disease outcomes, they form the basis for specific and reliable clinical evidence. This study aimed to review the potential clinical applications of MRI-based radiomics in studying the lymph nodes affected by esophageal cancer. The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer, enabling a more personalized and effectual approach.
Collapse
Affiliation(s)
- Yan-Han Xu
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Peng Lu
- Department of Imaging, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Ming-Cheng Gao
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rui Wang
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Yang-Yang Li
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Jian-Xiang Song
- Department of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| |
Collapse
|
20
|
Zhao F, Huang K, Sun Z, Chen X, He X, Wang B, Xin C. Consistent Learning-Based Breast Tumor Segmentation and Its Application in Sentinel Lymph Node Metastasis Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083326 DOI: 10.1109/embc40787.2023.10340091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Accurate staging of lymph nodes provides crucial diagnostic information for breast cancer patients, where segmentation is of great importance by localizing and visualizing the breast tumor of interest. Nevertheless, current segmentation methods perform average when facing large span of tumor sizes, degraded image quality, blurred tumor boundaries, and resulting noise during manual annotation. Therefore, we develop a Multi-scale RepVGG-based Segmentation Network (MPSegNet) to segment breast tumor from MR images. In particular, we construct a consistent learning framework for the MPSegNet to alleviate the impact of noisy labels upon segmentation results. The rationale is that different views covering the same breast tumors are supposed to generate identical segmentation predictions. Then, we predict SLN metastasis given segmented breast tumors, where we evaluate the relationships between the predictive performance and tumor segmentations under different consistencies. The results show the superiority of our method over other state-of-the-art methods. A high consistency among multiple views can boost the segmentation performance during consistent learning. However, the optimal segmentation does not produce the best SLN metastatic prediction results, implying that the dependence of classification upon segmentation needs to be elaborately investigated further.Clinical Relevance- This study facilitates more accurate segmentation of breast tumors with consistent learning, and provides an initial analysis between tumor segmentation and subsequent prediction of SLN metastasis, which has potential significance for the precise medical care of breast cancer patients.
Collapse
|
21
|
Hwang KP, Elshafeey NA, Kotrotsou A, Chen H, Son JB, Boge M, Mohamed RM, Abdelhafez AH, Adrada BE, Panthi B, Sun J, Musall BC, Zhang S, Candelaria RP, White JB, Ravenberg EE, Tripathy D, Yam C, Litton JK, Huo L, Thompson AM, Wei P, Yang WT, Pagel MD, Ma J, Rauch GM. A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer 2023; 5:e230009. [PMID: 37505106 PMCID: PMC10413296 DOI: 10.1148/rycan.230009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/18/2023] [Accepted: 06/03/2023] [Indexed: 07/29/2023]
Abstract
Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.
Collapse
Affiliation(s)
- Ken-Pin Hwang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Nabil A. Elshafeey
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Aikaterini Kotrotsou
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Huiqin Chen
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jong Bum Son
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Medine Boge
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rania M. Mohamed
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Abeer H. Abdelhafez
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Beatriz E. Adrada
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Bikash Panthi
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jia Sun
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Benjamin C. Musall
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Shu Zhang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rosalind P. Candelaria
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jason B. White
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Elizabeth E. Ravenberg
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Debu Tripathy
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Clinton Yam
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jennifer K. Litton
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Lei Huo
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Alastair M. Thompson
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Peng Wei
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Wei T. Yang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Mark D. Pagel
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jingfei Ma
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Gaiane M. Rauch
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| |
Collapse
|
22
|
Lee HJ, Nguyen AT, Song MW, Lee JE, Park SB, Jeong WG, Park MH, Lee JS, Park I, Lim HS. Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography. Korean J Radiol 2023; 24:498-511. [PMID: 37271204 DOI: 10.3348/kjr.2022.0731] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/30/2023] [Accepted: 04/30/2023] [Indexed: 06/06/2023] Open
Abstract
OBJECTIVE To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. MATERIALS AND METHODS This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. RESULTS Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. CONCLUSION CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.
Collapse
Affiliation(s)
- Hyo-Jae Lee
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Anh-Tien Nguyen
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Myung Won Song
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Seol Bin Park
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Won Gi Jeong
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Min Ho Park
- Department of Surgery, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Ji Shin Lee
- Department of Pathology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea
- Department of Data Science, Chonnam National University, Gwangju, Korea
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea.
| |
Collapse
|
23
|
Zhou Z, Chen Y, Zhao F, Sun Z, Zhu L, Yu H, Wang W. Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis: a retrospective study. Acta Radiol 2023; 64:951-961. [PMID: 35765225 DOI: 10.1177/02841851221107626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Non-invasive imaging technologies for assessing axillary lymph node (ALN) metastasis of breast cancer are needed in clinical practice. PURPOSE To explore the clinical value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) for predicting ALN metastasis of breast cancer. MATERIAL AND METHODS A total of 194 patients with pathologically confirmed breast cancer who underwent IVIM and DKI examination were reviewed retrospectively. The IVIM derived parameters of D, D*, and f and DKI-derived parameters of MD and MK were measured. The independent samples t-test was used to compare the parameters between the ALN metastasis and non-ALN metastasis groups. Receiver operating characteristic (ROC) curve analysis was also performed. RESULTS The D and MD in the ALN metastasis group were significantly lower than those in the non-ALN metastasis group (P < 0.001, P < 0.001). The D*, f, and MK were higher in the ALN metastasis group than in the non-ALN metastasis group (P = 0.015, P = 0.014, and P = 0.001, respectively). The area under the ROC curve (AUC) of D (0.768) was highest. In addition, the diagnostic efficiency of both IVIM and DKI were higher than that of the conventional MRI (P = 0.002, P = 0.048). The diagnostic efficiency of IVIM + DKI were higher than that of the IVIM or DKI alone (P = 0.021, P = 0.004). CONCLUSION IVIM and DKI can be used for predicting breast cancer ALN metastasis with D as the most meaningful parameter.
Collapse
Affiliation(s)
- Zhe Zhou
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Yueqin Chen
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Fan Zhao
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Zhanguo Sun
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Laimin Zhu
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Hao Yu
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| | - Weiwei Wang
- Department of Medical Imaging, 562122The Affiliated Hospital of Jining Medical University, Jining, PR China
| |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
Cho P, Park CS, Park GE, Kim SH, Kim HS, Oh SJ. Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer. Diagnostics (Basel) 2023; 13:diagnostics13030513. [PMID: 36766617 PMCID: PMC9914452 DOI: 10.3390/diagnostics13030513] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/20/2023] [Accepted: 01/21/2023] [Indexed: 02/01/2023] Open
Abstract
This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and cortical thickness and assessed eccentric cortical thickening, loss of fatty hilum, irregular margin, asymmetry in shape or number, and rim sign of ALNs on DW-MRI and categorized them into benign or suspicious ALNs. Pathologic reports were used as a reference standard. Statistical analysis was performed using the Mann-Whitney U test and chi-square test. Overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of DW-MRI were calculated. The ADC of metastatic ALNs was 0.905 × 10-3 mm2/s, and that of benign ALNs was 0.991 × 10-3 mm2/s (p = 0.243). All morphologic features showed significant difference between the two groups. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the final categorization on DW-MRI were 77.1%, 93.3%, 79.4%, 92.5%, and 86.2%, respectively. Our results suggest that morphologic evaluation of ALNs on DWI can discriminate metastatic ALNs from benign. The ADC value of metastatic ALNs was lower than that of benign nodes, but the difference was not statistically significant.
Collapse
Affiliation(s)
- Pyeonghwa Cho
- Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea School of Medicine, Seoul 21431, Republic of Korea
| | - Chang Suk Park
- Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea School of Medicine, Seoul 21431, Republic of Korea
- Correspondence: ; Tel.: +82-32-280-7305; Fax: +82-32-280-5192
| | - Ga Eun Park
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea School of Medicine, Seoul 06591, Republic of Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea School of Medicine, Seoul 06591, Republic of Korea
| | - Hyeon Sook Kim
- Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea School of Medicine, Seoul 21431, Republic of Korea
| | - Se-Jeong Oh
- Department of General Surgery, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea School of Medicine, Seoul 21431, Republic of Korea
| |
Collapse
|
26
|
Haraguchi T, Kobayashi Y, Hirahara D, Kobayashi T, Takaya E, Nagai MT, Tomita H, Okamoto J, Kanemaki Y, Tsugawa K. Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:627-640. [PMID: 37038802 DOI: 10.3233/xst-230009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548-0.982), 0.801 (0.597-1.000), and 0.779 (0.567-0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548-0.982), 0.757 (0.538-0.977), and 0.779 (0.567-0.992), respectively. CONCLUSIONS Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.
Collapse
Affiliation(s)
- Takafumi Haraguchi
- Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Yasuyuki Kobayashi
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Daisuke Hirahara
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
- Department of AI Research Lab, Harada Academy, Higashitaniyama, Kagoshima, Kagoshima, Japan
| | - Tatsuaki Kobayashi
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Eichi Takaya
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
- AI Lab, Tohoku University Hospital, Seiryomachi, Aoba-ku, Sendai, Miyagi, Japan
- School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, Japan
| | - Mariko Takishita Nagai
- Division of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Hayato Tomita
- Department of Radiology, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Jun Okamoto
- Department of Radiology, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Yoshihide Kanemaki
- Department of Radiology, Breast and Imaging Center, St. Marianna University School of Medicine, Manpukuji, Asao-ku, Kawasaki, Kanagawa, Japan
| | - Koichiro Tsugawa
- Division of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| |
Collapse
|
27
|
Zhang J, Zhang Z, Mao N, Zhang H, Gao J, Wang B, Ren J, Liu X, Zhang B, Dou T, Li W, Wang Y, Jia H. Radiomics nomogram for predicting axillary lymph node metastasis in breast cancer based on DCE-MRI: A multicenter study. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:247-263. [PMID: 36744360 DOI: 10.3233/xst-221336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
OBJECTIVES This study aims to develop and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. METHODS This retrospective study included 263 patients with histologically proven invasive breast cancer and who underwent DCE-MRI examination before surgery in two hospitals. All patients had a defined ALN status based on pathological examination results. Regions of interest (ROIs) of the primary tumor and ipsilateral ALN were manually drawn. A total of 1,409 radiomics features were initially computed from each ROI. Next, the low variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to extract the radiomics features. The selected radiomics features were used to establish the radiomics signature of the primary tumor and ALN. A radiomics nomogram model, including the radiomics signature and the independent clinical risk factors, was then constructed. The predictive performance was evaluated by the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) by using the training and testing sets. RESULTS ALNM rates of the training, internal testing, and external testing sets were 43.6%, 44.3% and 32.3%, respectively. The nomogram, including clinical risk factors (tumor diameter) and radiomics signature of the primary tumor and ALN, showed good calibration and discrimination with areas under the ROC curves of 0.884, 0.822, and 0.813 in the training, internal and external testing sets, respectively. DCA also showed that radiomics nomogram displayed better clinical predictive usefulness than the clinical or radiomics signature alone. CONCLUSIONS The radiomics nomogram combined with clinical risk factors and DCE-MRI-based radiomics signature may be used to predict ALN metastasis in a noninvasive manner.
Collapse
Affiliation(s)
- Jiwen Zhang
- Department of First Clinical Medicine, Shanxi Medical University, Taiyuan, China
| | - Zhongsheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Jing Gao
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Bin Wang
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jianlin Ren
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xin Liu
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Binyue Zhang
- Department of First Clinical Medicine, Shanxi Medical University, Taiyuan, China
| | - Tingyao Dou
- Department of First Clinical Medicine, Shanxi Medical University, Taiyuan, China
| | - Wenjuan Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Yanhong Wang
- Department of Microbiology and immunology, Shanxi Medical University, Taiyuan, China
| | - Hongyan Jia
- Department of Breast Surgery, First Hospital of Shanxi Medical University, Taiyuan, China
| |
Collapse
|
28
|
Zhang JY, Xu BH. Factors Predicting Positive Sentinel Lymph Node Biopsy in Clinically Node-Negative Breast Cancer [Letter]. BREAST CANCER (DOVE MEDICAL PRESS) 2022; 14:489-490. [PMID: 36575688 PMCID: PMC9790141 DOI: 10.2147/bctt.s400958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 12/17/2022] [Indexed: 12/29/2022]
Affiliation(s)
- Jia-Yu Zhang
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China
| | - Bao-Hua Xu
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China,China-Japan Friendship Hospital Stomatology Center, Beijing, People’s Republic of China,Correspondence: Bao-Hua Xu, China-Japan Friendship Hospital, 2 Cherry Blossom Garden East Street, Chaoyang District, Beijing, 100029, People’s Republic of China, Email
| |
Collapse
|
29
|
Cheng Y, Xu S, Wang H, Wang X, Niu S, Luo Y, Zhao N. Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI. Front Oncol 2022; 12:1047572. [PMID: 36578933 PMCID: PMC9792138 DOI: 10.3389/fonc.2022.1047572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
Purpose This study aims to investigate values of intra- and peri-tumoral regions in the mammography and magnetic resonance imaging (MRI) image for prediction of sentinel lymph node metastasis (SLNM) in invasive breast cancer (BC). Methods This study included 208 patients with invasive BC between Spe. 2017 and Apr. 2021. All patients underwent preoperative digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI) scans. Radiomics features were extracted from manually outlined intratumoral regions, and automatically dilated peritumoral tumor regions in each modality. The least absolute shrinkage and selection operator (LASSO) regression was used to select key features from each region to develop radiomics signatures (RSs). Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and negative predictive value (NPV) were calculated to evaluate performance of the RSs. Results Intra- and peri-tumoral regions of BC can provide complementary information on the SLN status. In each modality, the Com-RSs derived from combined intra- and peri-tumoral regions always yielded higher AUCs than the Intra-RSs or Peri-RSs. A total of 10 and 11 features were identified as the most important predictors from mammography (DM plus DBT) and MRI (DCE-MRI plus DWI), respectively. The DCE-MRI plus DWI generated higher AUCs compared with DM plus DBT in the training (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.897 vs. 0.846) and validation (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.826 vs. 0.786) cohort. Conclusions Radiomics features from intra- and peri-tumoral regions can provide complementary information to identify the SLNM in both mammography and MRI. The DCE-MRI plus DWI generated lower specificity, but higher AUC, accuracy, sensitivity and negative predictive value compared with DM plus DBT.
Collapse
Affiliation(s)
- Yuan Cheng
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Shu Xu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Haotian Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Shuxian Niu
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Nannan Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China,*Correspondence: Nannan Zhao,
| |
Collapse
|
30
|
Zeng P, Qu C, Liu J, Cui J, Liu X, Xiu D, Yuan H. Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma. Acta Radiol 2022:2841851221142552. [DOI: 10.1177/02841851221142552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background The preoperative prediction of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) is essential in prognosis and treatment strategy formulation. Purpose To compare the performance of computed tomography (CT) and magnetic resonance imaging (MRI) radiomics models for the preoperative prediction of LNM in PDAC. Material and Methods In total, 160 consecutive patients with PDAC were retrospectively included, who were divided into the training and validation sets (ratio of 8:2). Two radiologists evaluated LNM basing on morphological abnormalities. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, and multiphase contrast enhanced MRI and multiphase CT, respectively. Overall, 1184 radiomics features were extracted from each volume of interest drawn. Only features with an intraclass correlation coefficient ≥0.75 were included. Three sequential feature selection steps—variance threshold, variance thresholding and least absolute shrinkage selection operator—were repeated 20 times with fivefold cross-validation in the training set. Two radiomics models based on multiphase CT and multiparametric MRI were built with the five most frequent features. Model performance was evaluated using the area under the curve (AUC) values. Results Multiparametric MRI radiomics model achieved improved AUCs (0.791 and 0.786 in the training and validation sets, respectively) than that of the CT radiomics model (0.672 and 0.655 in the training and validation sets, respectively) and of the radiologists’ assessment (0.600–0.613 and 0.560–0.587 in the training and validation sets, respectively). Conclusion Multiparametric MRI radiomics model may serve as a potential tool for preoperatively evaluating LNM in PDAC and had superior predictive performance to multiphase CT-based model and radiologists’ assessment.
Collapse
Affiliation(s)
- Piaoe Zeng
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Chao Qu
- Department of General Surgery, Peking University Third Hospital, Beijing, PR China
| | - Jianfang Liu
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, PR China
| | - Xiaoming Liu
- Department of Research and Development, Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, PR China
| | - Dianrong Xiu
- Department of General Surgery, Peking University Third Hospital, Beijing, PR China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, PR China
| |
Collapse
|
31
|
Zhang X, Liu M, Ren W, Sun J, Wang K, Xi X, Zhang G. Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model. Front Oncol 2022; 12:1069733. [PMID: 36561533 PMCID: PMC9763602 DOI: 10.3389/fonc.2022.1069733] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/15/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose To develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer. Methods Clinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks. The idea of a weighted voting method in ensemble learning was employed, and then T2WI, DWI, and DCE-MRI models were used as the base models to construct a multiparametric MRI model. The entire dataset was randomly divided into training sets and testing sets (the training set 202 cases, including 78 ALNM, 124 non-ALNM; the testing set 50 cases, including 20 ALNM, 30 non-ALNM). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of models were calculated. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic performance of each model for axillary lymph node metastasis, and the DeLong test was performed, P< 0.05 statistically significant. Results For the assessment of axillary lymph node status in invasive breast cancer on the test set, multiparametric MRI models yielded an AUC of 0.913 (95% CI, 0.799-0.974); T2WI-based model yielded an AUC of 0.908 (95% CI, 0.792-0.971); DWI-based model achieved an AUC of 0.702 (95% CI, 0.556-0.823); and the AUC of the DCE-MRI-based model was 0.572 (95% CI, 0.424-0.711). The improvement in the diagnostic performance of the multiparametric MRI model compared with the DWI and DCE-MRI-based models were significant (P< 0.01 for both). However, the increase was not meaningful compared with the T2WI-based model (P = 0.917). Conclusion Multiparametric MRI image analysis based on an ensemble CNN model with deep learning is of practical application and extension for preoperative prediction of axillary lymph node metastasis in invasive breast cancer.
Collapse
Affiliation(s)
- Xiaodong Zhang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Menghan Liu
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Wanqing Ren
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Jingxiang Sun
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Kesong Wang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Xiaoming Xi
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Guang Zhang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,*Correspondence: Guang Zhang,
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Liu P, Liang X, Liao S, Lu Z. Pattern Classification for Ovarian Tumors by Integration of Radiomics and Deep Learning Features. Curr Med Imaging 2022; 18:1486-1502. [PMID: 35578861 DOI: 10.2174/1573405618666220516122145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/21/2022] [Accepted: 03/03/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Ovarian tumor is a common female genital tumor, among which malignant tumors have a poor prognosis. The survival rate of 70% of patients with ovarian cancer is less than 5 years, while benign ovarian tumor is better, so the early diagnosis of ovarian cancer is important for the treatment and prognosis of patients. OBJECTIVES Our aim is to establish a classification model for ovarian tumors. METHODS We extracted radiomics and deep learning features from patients'CT images. The four-step feature selection algorithm proposed in this paper was used to obtain the optimal combination of features, then, a classification model was developed by combining those selected features and support vector machine. The receiver operating characteristic curve and an area under the curve (AUC) analysis were used to evaluate the performance of the classification model in both the training and test cohort. RESULTS The classification model, which combined radiomics features with deep learning features, demonstrated better classification performance with respect to the radiomics features model alone in training cohort (AUC 0.9289 vs. 0.8804, P < 0.0001, accuracy 0.8970 vs. 0.7993, P < 0.0001), and significantly improve the performance in the test cohort (AUC 0.9089 vs. 0.8446, P = 0.001, accuracy 0.8296 vs. 0.7259, P < 0.0001). CONCLUSION The experiments showed that deep learning features play an active role in the construction of classification model, and the proposed classification model achieved excellent classification performance, which can potentially become a new auxiliary diagnostic tool.
Collapse
Affiliation(s)
- Pengfei Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510000, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xiaokang Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510000, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Shengwu Liao
- Nanfang Hospital Southern Medical University, Guangzhou, China
| | - Zhentai Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510000, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| |
Collapse
|
34
|
Yin H, Bai L, Jia H, Lin G. Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning. Thorac Cancer 2022; 13:3183-3191. [PMID: 36203226 PMCID: PMC9663668 DOI: 10.1111/1759-7714.14673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND To evaluate the performances of multiparametric MRI-based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. METHODS A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast-enhanced T1 -weighted imaging (T1 C), Apparent diffusion coefficient (ADC), and T2 -weighted imaging (T2 W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. RESULTS For the separation of each subtype from other subtypes on the testing set, the T1 C-based models yielded AUCs from 0.762 to 0.920; the ADC-based models yielded AUCs from 0.686 to 0.851; and the T2 W-based models achieved AUCs from 0.639 to 0.697. CONCLUSION T1 C-based models performed better than ADC-based models and T2 W-based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2-enriched subtypes were better than that of luminal A and luminal B subtypes.
Collapse
Affiliation(s)
- Haolin Yin
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Lutian Bai
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Huihui Jia
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Guangwu Lin
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| |
Collapse
|
35
|
Alsumai TS, Alhazzaa N, Alshamrani A, Assiri S, Alhefdhi A. Factors Predicting Positive Sentinel Lymph Node Biopsy in Clinically Node-Negative Breast Cancer. BREAST CANCER (DOVE MEDICAL PRESS) 2022; 14:323-334. [PMID: 36237483 PMCID: PMC9553108 DOI: 10.2147/bctt.s373005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/22/2022] [Indexed: 11/06/2022]
Abstract
Purpose Sentinel lymph node (SLN) biopsy (SLNB) is the standard tool to stage the axilla of breast cancer patients. This study aimed to identify the predictors of positive SLNB in patients with clinically node-negative breast cancer. Patients and Methods A retrospective, single-institution cohort of patients with early-stage breast cancer without clinically identifiable axillary lymphadenopathy was chosen from January 2010 to December 2018. Logistic regression was used to identify possible predictors of positive SLNB. Results Four hundred and seventy patients were identified; their mean age was 50±11 years. Most patients had the following characteristics: invasive ductal carcinoma (n=382, 81.3%), unilateral tumor (n=461, 98.1%), unifocal disease (n=351, 74.7%), intermediate grade (n=276, 59.0%), and estrogen and progesterone receptor positivity with human epidermal growth factor receptor 2 negativity (n=305, 64.9%). The mean size of the breast mass was 2.3±1.5 cm. SLNB was positive in 128 (27.2%) cases. The mean number of SLNs was 2±1.2. Axillary lymph node dissection was performed in 109 patients. The mean number of lymph nodes removed was 15±6. In 66 (60.6%) of the 109 patients with metastatic axillary nodes, only the SLNs were found to be positive. The number of SLNs, tumor size, tumor grade, receptor status, prominent axillary lymph nodes, and lymphovascular invasion predicted positive SLNB (P = 0.01, 0.03, 0.03, and 0.04 and <0.001 and <0.001, respectively). Conclusion Our results suggest that a number of histopathological and radiological characteristics of breast cancer can predict SLNB positivity in clinically node-negative breast cancer patients.
Collapse
Affiliation(s)
- Thuraya S Alsumai
- Department of Surgery, Section of Breast & Endocrine Surgery, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia,Correspondence: Thuraya S Alsumai, Department of Surgery, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia, Tel +966 565433996, Email
| | - Norah Alhazzaa
- Department of Surgery, Section of Breast & Endocrine Surgery, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | | | - Sarah Assiri
- Department of Surgery, Section of Breast & Endocrine Surgery, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Amal Alhefdhi
- Department of Surgery, Section of Breast & Endocrine Surgery, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia,Faculty of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| |
Collapse
|
36
|
Santucci D, Faiella E, Gravina M, Cordelli E, de Felice C, Beomonte Zobel B, Iannello G, Sansone C, Soda P. CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI. Cancers (Basel) 2022; 14:cancers14194574. [PMID: 36230497 PMCID: PMC9558949 DOI: 10.3390/cancers14194574] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 12/05/2022] Open
Abstract
Simple Summary Breast cancer represents the most frequent cancer in women in the world. The state of the axillary lymph node is considered an independent prognostic factor and is currently evaluated only with invasive methods. Deep learning approaches, especially the ones based on convolutional neural networks, offer a valid, non-invasive alternative, allowing extraction of large amounts of the quantitative data that are used to build predictive models. The aim of our work is to evaluate the influence of the peritumoral parenchyma through different bounding box techniques on the prediction of the axillary lymph node in breast cancer patients using a deep learning artificial intelligence approach. Abstract Background: The axillary lymph node status (ALNS) is one of the most important prognostic factors in breast cancer (BC) patients, and it is currently evaluated by invasive procedures. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), highlights the physiological and morphological characteristics of primary tumor tissue. Deep learning approaches (DL), such as convolutional neural networks (CNNs), are able to autonomously learn the set of features directly from images for a specific task. Materials and Methods: A total of 155 malignant BC lesions evaluated via DCE-MRI were included in the study. For each patient’s clinical data, the tumor histological and MRI characteristics and axillary lymph node status (ALNS) were assessed. LNS was considered to be the final label and dichotomized (LN+ (27 patients) vs. LN− (128 patients)). Based on the concept that peritumoral tissue contains valuable information about tumor aggressiveness, in this work, we analyze the contributions of six different tumor bounding options to predict the LNS using a CNN. These bounding boxes include a single fixed-size box (SFB), a single variable-size box (SVB), a single isotropic-size box (SIB), a single lesion variable-size box (SLVB), a single lesion isotropic-size box (SLIB), and a two-dimensional slice (2DS) option. According to the characteristics of the volumes considered as inputs, three different CNNs were investigated: the SFB-NET (for the SFB), the VB-NET (for the SVB, SIB, SLVB, and SLIB), and the 2DS-NET (for the 2DS). All the experiments were run in 10-fold cross-validation. The performance of each CNN was evaluated in terms of accuracy, sensitivity, specificity, the area under the ROC curve (AUC), and Cohen’s kappa coefficient (K). Results: The best accuracy and AUC are obtained by the 2DS-NET (78.63% and 77.86%, respectively). The 2DS-NET also showed the highest specificity, whilst the highest sensibility was attained by the VB-NET based on the SVB and SIB as bounding options. Conclusion: We have demonstrated that a selective inclusion of the DCE-MRI’s peritumoral tissue increases accuracy in the lymph node status prediction in BC patients using CNNs as a DL approach.
Collapse
Affiliation(s)
- Domiziana Santucci
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-medico”, Via Alvaro del Portillo, 21, 00128 Rome, Italy
- Department of Radiology, Sant’Anna Hospital, Via Ravona, 22042 Como, Italy
- Correspondence:
| | - Eliodoro Faiella
- Department of Radiology, Sant’Anna Hospital, Via Ravona, 22042 Como, Italy
| | - Michela Gravina
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80131 Naples, Italy
| | - Ermanno Cordelli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-medico”, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Carlo de Felice
- Department of Radiology, University of Rome “Sapienza”, Viale del Policlinico, 155, 00161 Rome, Italy
| | - Bruno Beomonte Zobel
- Department of Radiology, University of Rome “Campus Bio-medico”, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Giulio Iannello
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-medico”, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Carlo Sansone
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80131 Naples, Italy
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome “Campus Bio-medico”, Via Alvaro del Portillo, 21, 00128 Rome, Italy
- Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Universitetstorget, 490187 Umeå, Sweden
| |
Collapse
|
37
|
Fang S, Zhu J, Wang Y, Zhou J, Wang G, Xu W, Zhang W. The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer. BMC Med Imaging 2022; 22:163. [PMID: 36088299 PMCID: PMC9464403 DOI: 10.1186/s12880-022-00891-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/31/2022] [Indexed: 12/28/2022] Open
Abstract
Abstract
Background
This study aims to estimate the amount of axillary lymph node (ALN) involvement in early-stage breast cancer utilizing a field of view (FOV) optimized and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) approach, as well as a whole-lesion histogram analysis.
Methods
This retrospective analysis involved 81 individuals with invasive breast cancer. The patients were divided into three groups: N0 (negative ALN metastasis), N1–2 (low metastatic burden with 1–2 ALNs), and N≥3 (heavy metastatic burden with ≥ 3 ALNs) based on their sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Histogram parameters of apparent diffusion coefficient (ADC) depending basically on FOCUS DWI were performed using 3D-Slicer software for whole lesions. The typical histogram characteristics for N0, N1–2, and N≥ 3 were compared to identify the significantly different parameters. To determine the diagnostic efficacy of significantly different factors, the area under their receiver operating characteristic (ROC) curves was examined.
Results
There were significant differences in the energy, maximum, 90 percentile, range, and lesion size among N0, N1–2, and N≥ 3 groups (P < 0.05). The energy differed significantly between N0 and N1–2 groups (P < 0.05), and some certain ADC histogram parameters and lesion sizes differed significantly between N0 and N≥3, or N1–2 and N≥3 groups. For ROC analysis, the energy yielded the best diagnostic performance in distinguishing N0 and N1–2 groups from N≥3 group with an AUC value of0.853. All parameters revealed excellent inter-observer agreement with inter-reader consistencies data ranging from0.919 to 0.982.
Conclusion
By employing FOCUS DWI method, the analysis of whole-lesion ADC histogram quantitatively provides a non-invasive way to evaluate the degree of ALN metastatic spread in early-stage breast cancer.
Collapse
|
38
|
Xu J, Li J. A model for the early identification of sentinel lymph node metastasis in patients with breast cancer based on contrast‑enhanced ultrasound and clinical features. Oncol Lett 2022; 24:378. [PMID: 36238843 PMCID: PMC9494614 DOI: 10.3892/ol.2022.13498] [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: 02/14/2022] [Accepted: 08/25/2022] [Indexed: 11/06/2022] Open
Abstract
The present study was designed to establish a model for the early identification of sentinel lymph node (SLN) metastasis in patients with breast cancer (BC). The SLN metastasis predictive model was established with a retrospective training set of 365 patients with BC and was re-evaluated using a prospective validation set of 402 patients with BC. The multivariable analysis indicated that the tumor diameter [odds ratio (OR), 1.189; 95% confidence interval (CI), 1.124-1.257; P<0.001], menopause (OR, 1.011; 95% CI, 0.603-1.436; P<0.001), estrogen receptor (ER) expression (OR, 3.199; 95% CI, 1.077-6.567; P=0.043) and contrast-enhanced ultrasonography (CEUS) type (OR, 10.563; 95% CI, 6.890-28.372; P<0.001) were independent predictors of SLN status in patients with BC. The SLN metastasis predictive model was as follows: (0.173 × tumor diameter)-(4.490 × menopause) + (2.322 × ER) + (5.445 × CEUS type)-1.9521. In the training set, the model was highly sensitive (83.6%) and specific (94.3%) for the early identification of SLN metastasis. Similarly, in the validation set, the model was highly sensitive (70.4%) and specific (89.5%) for the early identification of SLN metastasis in patients with BC. Overall, in the present study, a model was successfully established to predict SLN metastasis in patients with BC that includes tumor diameter, menopausal status, ER expression and CEUS detection.
Collapse
Affiliation(s)
- Juan Xu
- Department of Interventional Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei 061000, P.R. China
| | - Junzhi Li
- Department of Interventional Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei 061000, P.R. China
| |
Collapse
|
39
|
Mammography radiomics features at diagnosis and progression-free survival among patients with breast cancer. Br J Cancer 2022; 127:1886-1892. [PMID: 36050449 DOI: 10.1038/s41416-022-01958-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 08/06/2022] [Accepted: 08/09/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The associations between mammographic radiomics and breast cancer clinical endpoints are unclear. We aimed to identify mammographic radiomics features associated with breast cancer prognosis. METHODS Nested from a large breast cancer cohort in our institution, we conducted an extreme case-control study consisting of 207 cases with any invasive disease-free survival (iDFS) endpoint <5 years and 207 molecular subtype-matched controls with >5-year iDFS. A total of 632 radiomics features in craniocaudal (CC) and mediolateral oblique (MLO) views were extracted from pre-treatment mammography. Logistic regression was used to identify iDFS-associated features with multiple testing corrections (Benjamini-Hochberg method). In a subsample with RNA-seq data (n = 96), gene set enrichment analysis was employed to identify pathways associated with lead features. RESULTS We identified 15 iDFS-associated features from CC-view yet none from MLO-view. S(1,-1)SumAverg and WavEnLL_s-6 were the lead ones and associated with favourable (OR 0.64, 95% CI 0.42-0.87, P = 0.01) and poor iDFS (OR 1.53, 95% CI 1.31-1.76, P = 0.01), respectively. Both features were associated with eight pathways (primarily involving cell cycle regulation) in tumour but not adjacent normal tissues. CONCLUSION Our findings suggest mammographic radiomics features are associated with breast cancer iDFS, potentially through pathways involving cell cycle regulation.
Collapse
|
40
|
Wang Z, Sun H, Li J, Chen J, Meng F, Li H, Han L, Zhou S, Yu T. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI. J Magn Reson Imaging 2022; 56:700-709. [PMID: 35108415 DOI: 10.1002/jmri.28082] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (MRI) is widely used in breast cancer screening. Accurate prediction of the axillary lymph nodes metastasis (ALNM) is essential for breast cancer surgery and treatment. However, there is no mature and effective discerning method for ALNM based on multiparametric MRI. PURPOSE To evaluate the ALNM using T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, respectively, and construct a quantitative ALNM discerning model of integrated multiparametric MRI. STUDY TYPE Retrospective. POPULATION Three-hundred forty-eight breast cancer patients, 163 with ALNM (99.39% females), and 185 without ALNM (100% females). The dataset was randomly divided into the training set (315 cases) and the testing set (33 cases). FIELD STRENGTH/SEQUENCE 1.5 T; T1WI (VIBRANT), T2WI (FSE), and DWI (echo planar imaging [EPI]). ASSESSMENT The lesion region of interest images were cropped and sent to a pretrained ResNet50 network. Then, the results of different sequences were sent to a classifier for ensemble learning to construct the ALNM model of multiparametric MRI. STATISTICAL TESTS Performance indicators such as accuracy, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were calculated. Student's t-test, chi-square test, Fisher's exact test, and Delong test were performed, and P < 0.05 was considered statistically significant. RESULTS T2WI performed the best among the three sequences, and achieved the accuracy and AUC of 0.933/0.989 in the testing set. Compared to T1WI with the accuracy and AUC of 0.691/0.806, the increase is significant. While compared to DWI with the accuracy and AUC of 0.800/0.910, the improvement is not significant (P = 0.126). After integrating three sequences, the accuracy and AUC improved to 0.970 and 0.996. DATA CONCLUSION T2WI performed better than DWI and T1WI in discerning ALNM in this breast cancer dataset. The proposed quantitative model of integrated multiparametric MRI could effectively help the ALNM diagnosis. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 2.
Collapse
Affiliation(s)
- Zijian Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Hang Sun
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Jing Li
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Jing Chen
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Fancong Meng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Hong Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Lu Han
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Shi Zhou
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| |
Collapse
|
41
|
Di Paola V, Mazzotta G, Pignatelli V, Bufi E, D’Angelo A, Conti M, Panico C, Fiorentino V, Pierconti F, Kilburn-Toppin F, Belli P, Manfredi R. Beyond N Staging in Breast Cancer: Importance of MRI and Ultrasound-based Imaging. Cancers (Basel) 2022; 14:cancers14174270. [PMID: 36077805 PMCID: PMC9454572 DOI: 10.3390/cancers14174270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 12/29/2022] Open
Abstract
The correct N-staging in breast cancer is crucial to tailor treatment and stratify the prognosis. N-staging is based on the number and the localization of suspicious regional nodes on physical examination and/or imaging. Since clinical examination of the axillary cavity is associated with a high false negative rate, imaging modalities play a central role. In the presence of a T1 or T2 tumor and 0–2 suspicious nodes, on imaging at the axillary level I or II, a patient should undergo sentinel lymph node biopsy (SLNB), whereas in the presence of three or more suspicious nodes at the axillary level I or II confirmed by biopsy, they should undergo axillary lymph node dissection (ALND) or neoadjuvant chemotherapy according to a multidisciplinary approach, as well as in the case of internal mammary, supraclavicular, or level III axillary involved lymph nodes. In this scenario, radiological assessment of lymph nodes at the time of diagnosis must be accurate. False positives may preclude a sentinel lymph node in an otherwise eligible woman; in contrast, false negatives may lead to an unnecessary SLNB and the need for a second surgical procedure. In this review, we aim to describe the anatomy of the axilla and breast regional lymph node, and their diagnostic features to discriminate between normal and pathological nodes at Ultrasound (US) and Magnetic Resonance Imaging (MRI). Moreover, the technical aspects, the advantage and limitations of MRI versus US, and the possible future perspectives are also analyzed, through the analysis of the recent literature.
Collapse
Affiliation(s)
- Valerio Di Paola
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Correspondence: or
| | - Giorgio Mazzotta
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Vincenza Pignatelli
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Enida Bufi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Anna D’Angelo
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Marco Conti
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Camilla Panico
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Vincenzo Fiorentino
- Institute of Pathology, Università Cattolica del S. Cuore, Fondazione Policlinico “A. Gemelli”, 00168 Rome, Italy
| | - Francesco Pierconti
- Institute of Pathology, Università Cattolica del S. Cuore, Fondazione Policlinico “A. Gemelli”, 00168 Rome, Italy
| | - Fleur Kilburn-Toppin
- Cambridge Breast Unit, Cambridge University Hospital NHS Foundation Trust, Addenbrookes’ Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - Paolo Belli
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Riccardo Manfredi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| |
Collapse
|
42
|
Li C, Li W, Liu C, Zheng H, Cai J, Wang S. Artificial intelligence in multi-parametric magnetic resonance imaging: A review. Med Phys 2022; 49:e1024-e1054. [PMID: 35980348 DOI: 10.1002/mp.15936] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Peng Cheng Laboratory, Shenzhen, 518066, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| |
Collapse
|
43
|
Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers.
Collapse
|
44
|
Mendez AM, Fang LK, Meriwether CH, Batasin SJ, Loubrie S, Rodríguez-Soto AE, Rakow-Penner RA. Diffusion Breast MRI: Current Standard and Emerging Techniques. Front Oncol 2022; 12:844790. [PMID: 35880168 PMCID: PMC9307963 DOI: 10.3389/fonc.2022.844790] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.
Collapse
Affiliation(s)
- Ashley M. Mendez
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Lauren K. Fang
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Claire H. Meriwether
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Summer J. Batasin
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Stéphane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Ana E. Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Rebecca A. Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, CA, United States,Department of Bioengineering, University of California San Diego, La Jolla, CA, United States,*Correspondence: Rebecca A. Rakow-Penner,
| |
Collapse
|
45
|
Zhang Y, Zhang KY, Jia HD, Fang X, Lin TT, Wei C, Qian LT, Dong JN. Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer. Acad Radiol 2022; 29:1048-1057. [PMID: 34654623 DOI: 10.1016/j.acra.2021.08.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/22/2021] [Accepted: 08/27/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and texture parameters of primary lesions and lymph nodes for predicting pelvic lymph node metastasis in patients with cervical cancer. MATERIALS AND METHODS A total of 143 patients with cervical cancer confirmed by surgical pathology were analyzed retrospectively and 125 patients were enrolled in primary lesions study, 83 patients and 134 lymph nodes were enrolled in lymph nodes study. Patients and lymph nodes were randomly divided into training group and test group at a ratio of 2: 1. The IVIM-DWI parameters and 3D texture features of primary lesions and lymph nodes of all patients were measured. The least absolute shrinkage and selection operator algorithm, spearman's correlation analysis, independent two-sample t-test and Mann-Whitney U-test were used to select texture parameters. Multivariate Logistic regression analysis and receiver operating characteristic curves were used to model and evaluate diagnostic performances. RESULTS In primary lesions study, model 1 was constructed by combining f value, original_shape_Sphericity and original_firstorder_Mean of primary lesions. In lymph nodes study, model 2 was constructed by combining short diameter, circular enhancement and rough margin of lymph nodes. Model 3 was constructed by combining ADC, f value and original_glszm_Small Area Emphasis of lymph nodes. The areas under curve of model 1, 2 and 3 in training group and test group were 0.882, 0.798, 0.907 and 0.862, 0.771, 0.937 respectively. CONCLUSION Models based on IVIM-DWI and texture parameters of primary lesions and lymph nodes both performed well in diagnosing pelvic lymph node metastasis of cervical cancer and were superior to morphological features of lymph nodes. Especially, parameters of lymph nodes showed higher diagnostic efficiency and clinical significance.
Collapse
|
46
|
Ma M, Jiang Y, Qin N, Zhang X, Zhang Y, Wang X, Wang X. A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI. Front Oncol 2022; 12:884599. [PMID: 35734587 PMCID: PMC9207247 DOI: 10.3389/fonc.2022.884599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To develop a radiomics model based on preoperative dynamic contrast-enhanced MRI (DCE-MRI) to identify sentinel lymph node (SLN) metastasis in breast cancer (BC) patients. Materials and Methods The MRI images and clinicopathological data of 142 female primary BC patients from January 2017 to December 2018 were included in this study. The patients were randomly divided into the training and testing cohorts at a ratio of 7:3. Four types of radiomics models were built: 1) a radiomics model based on the region of interest (ROI) of breast tumor; 2) a radiomics model based on the ROI of intra- and peri-breast tumor; 3) a radiomics model based on the ROI of axillary lymph node (ALN); 4) a radiomics model based on the ROI of ALN and breast tumor. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to assess the performance of the three radiomics models. The technique for order of preference by similarity to ideal solution (TOPSIS) through decision matrix analysis was used to select the best model. Results Models 1, 2, 3, and 4 yielded AUCs of 0.977, 0.999, 0.882, and 1.000 in the training set and 0.699, 0.817, 0.906, and 0.696 in the testing set, respectively, in terms of predicting SLN metastasis. Model 3 had the highest AUC in the testing cohort, and only the difference from Model 1 was statistically significant (p = 0.022). DCA showed that Model 3 yielded a greater net benefit to predict SLN metastasis than the other three models in the testing cohort. The best model analyzed by TOPSIS was Model 3, and the method's names for normalization, dimensionality reduction, feature selection, and classification are mean, principal component analysis (PCA), ANOVA, and support vector machine (SVM), respectively. Conclusion ALN radiomics feature extraction on DCE-MRI is a potential method to evaluate SLN status in BC patients.
Collapse
Affiliation(s)
- Mingming Ma
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yuan Jiang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Naishan Qin
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co., Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co., Ltd., Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
| |
Collapse
|
47
|
Tanaka S, Kadoya N, Sugai Y, Umeda M, Ishizawa M, Katsuta Y, Ito K, Takeda K, Jingu K. A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy. Sci Rep 2022; 12:8899. [PMID: 35624113 PMCID: PMC9142601 DOI: 10.1038/s41598-022-12170-z] [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: 08/16/2021] [Accepted: 05/05/2022] [Indexed: 12/14/2022] Open
Abstract
Early regression—the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)—is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coordinates, necessitating adaptive radiotherapy (ART). We developed a deep learning-based radiomics (DLR) approach to predict early head and neck tumor regression and thereby facilitate ART. Primary gross tumor volume (GTVp) was monitored in 96 patients and nodal GTV (GTVn) in 79 patients during treatment. All patients underwent two computed tomography (CT) scans: one before the start of radiotherapy for initial planning and one during radiotherapy for boost planning. Patients were assigned to regression and nonregression groups according to their median tumor regression rate (ΔGTV/treatment day from initial to boost CT scan). We input a GTV image into the convolutional neural network model, which was pretrained using natural image datasets, via transfer learning. The deep features were extracted from the last fully connected layer. To clarify the prognostic power of the deep features, machine learning models were trained. The models then predicted the regression and nonregression of GTVp and GTVn and evaluated the predictive performance by 0.632 + bootstrap area under the curve (AUC). Predictive performance for GTVp regression was highest using the InceptionResNetv2 model (mean AUC = 0.75) and that for GTVn was highest using NASNetLarge (mean AUC = 0.73). Both models outperformed the handcrafted radiomics features (mean AUC = 0.63 for GTVp and 0.61 for GTVn) or clinical factors (0.64 and 0.67, respectively). DLR may facilitate ART for improved radiation side-effects and target coverage.
Collapse
Affiliation(s)
- Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Yuto Sugai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Mariko Umeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Miyu Ishizawa
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| |
Collapse
|
48
|
Wu X, Guo Y, Sa Y, Song Y, Li X, Lv Y, Xing D, Sun Y, Cong Y, Yu H, Jiang W. Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer. Front Oncol 2022; 12:823897. [PMID: 35615151 PMCID: PMC9125761 DOI: 10.3389/fonc.2022.823897] [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: 11/28/2021] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph node (NSLN) metastasis and axillary tumor burden among breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs).Materials and MethodsBreast cancer patients with 1–2 positive SLNs who underwent axillary lymph node dissection (ALND) and contrast-enhanced spectral mammography (CESM) examination were enrolled between 2018 and 2021. CESM-based radiomics and deep learning features of tumors were extracted. The correlation analysis, least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were used for further feature selection. Models based on the selected features and clinical risk factors were constructed with multivariate logistic regression. Finally, two radiomics nomograms were proposed for predicting NSLN metastasis and the probability of high axillary tumor burden.ResultsA total of 182 patients [53.13 years ± 10.03 (standard deviation)] were included. For predicting the NSLN metastasis status, the radiomics nomogram built by 5 selected radiomics features and 3 clinical risk factors including the number of positive SLNs, ratio of positive SLNs, and lymphovascular invasion (LVI), achieved the area under the receiver operating characteristic curve (AUC) of 0.85 [95% confidence interval (CI): 0.71–0.99] in the testing set and 0.82 (95% CI: 0.67–0.97) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUC values of the developed radiomics nomogram are 0.82 (95% CI: 0.66–0.97) in the testing set and 0.77 (95% CI: 0.62–0.93) in the temporal validation cohort.DiscussionCESM images contain useful information for predicting NSLN metastasis and axillary tumor burden of breast cancer patients. Radiomics can inspire the potential of CESM images to identify lymph node metastasis and improve predictive performance.
Collapse
Affiliation(s)
- Xiaoqian Wu
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Yu Guo
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Yu Sa
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Yipeng Song
- Department of Radiotherapy, Yantai Yuhuangding Hospital, Yantai, China
| | - Xinghua Li
- Department of Radiotherapy, Yantai Yuhuangding Hospital, Yantai, China
| | - Yongbin Lv
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Dong Xing
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Yan Sun
- Department of Otorhinolaryngology–Head and Neck Surgery, Yuhuangding Hospital of Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Yizi Cong
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Yantai, China
- *Correspondence: Wei Jiang, ; Yizi Cong, ; Hui Yu,
| | - Hui Yu
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- *Correspondence: Wei Jiang, ; Yizi Cong, ; Hui Yu,
| | - Wei Jiang
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Department of Radiotherapy, Yantai Yuhuangding Hospital, Yantai, China
- *Correspondence: Wei Jiang, ; Yizi Cong, ; Hui Yu,
| |
Collapse
|
49
|
Jha AK, Mithun S, Purandare NC, Kumar R, Rangarajan V, Wee L, Dekker A. Radiomics: a quantitative imaging biomarker in precision oncology. Nucl Med Commun 2022; 43:483-493. [PMID: 35131965 DOI: 10.1097/mnm.0000000000001543] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.
Collapse
Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Nilendu C Purandare
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Science, New Delhi, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| |
Collapse
|
50
|
Yang G, Yang F, Zhang F, Wang X, Tan Y, Qiao Y, Zhang H. Radiomics Profiling Identifies the Value of CT Features for the Preoperative Evaluation of Lymph Node Metastasis in Papillary Thyroid Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12051119. [PMID: 35626275 PMCID: PMC9139816 DOI: 10.3390/diagnostics12051119] [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: 03/18/2022] [Revised: 04/24/2022] [Accepted: 04/26/2022] [Indexed: 12/10/2022] Open
Abstract
Background: The aim of this study was to identify the increased value of integrating computed tomography (CT) radiomics analysis with the radiologists’ diagnosis and clinical factors to preoperatively diagnose cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients. Methods: A total of 178 PTC patients were randomly divided into a training (n = 125) and a test cohort (n = 53) with a 7:3 ratio. A total of 2553 radiomic features were extracted from noncontrast, arterial contrast-enhanced and venous contrast-enhanced CT images of each patient. Principal component analysis (PCA) and Pearson’s correlation coefficient (PCC) were used for feature selection. Logistic regression was employed to build clinical–radiological, radiomics and combined models. A nomogram was developed by combining the radiomics features, CT-reported lymph node status and clinical factors. Results: The radiomics model showed a predictive performance similar to that of the clinical–radiological model, with similar areas under the curve (AUC) and accuracy (ACC). The combined model showed an optimal predictive performance in both the training (AUC, 0.868; ACC, 86.83%) and test cohorts (AUC, 0.878; ACC, 83.02%). Decision curve analysis demonstrated that the combined model has good clinical application value. Conclusions: Embedding CT radiomics into the clinical diagnostic process improved the diagnostic accuracy. The developed nomogram provides a potential noninvasive tool for LNM evaluation in PTC patients.
Collapse
Affiliation(s)
- Guoqiang Yang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Fan Yang
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
| | - Fengyan Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Xiaochun Wang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Yan Tan
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Ying Qiao
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
- Correspondence: (Y.Q.); (H.Z.)
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
- Correspondence: (Y.Q.); (H.Z.)
| |
Collapse
|