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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.
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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.
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Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology 2020; 295:500-515. [PMID: 32315268 DOI: 10.1148/radiol.2020192534] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.
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Affiliation(s)
- Jung Min Chang
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Jessica W T Leung
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Linda Moy
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Su Min Ha
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Woo Kyung Moon
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
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