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Xu K, Hua M, Mai T, Ren X, Fang X, Wang C, Ge M, Qian H, Xu M, Zhang R. A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1475-1487. [PMID: 38424277 PMCID: PMC11300413 DOI: 10.1007/s10278-023-00923-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 03/02/2024]
Abstract
This study aims to develop an MRI-based radiomics model to assess the likelihood of recurrence in luminal B breast cancer. The study analyzed medical images and clinical data from 244 patients with luminal B breast cancer. Of 244 patients, 35 had experienced recurrence and 209 had not. The patients were randomly divided into the training set (51.5 ± 12.5 years old; n = 171) and the test set (51.7 ± 11.3 years old; n = 73) in a ratio of 7:3. The study employed univariate and multivariate Cox regression along with the least absolute shrinkage and selection operator (LASSO) regression methods to select radiomics features and calculate a risk score. A combined model was constructed by integrating the risk score with the clinical and pathological characteristics. The study identified two radiomics features (GLSZM and GLRLM) from DCE-MRI that were used to calculate a risk score. The AUCs were 0.860 and 0.868 in the training set and 0.816 and 0.714 in the testing set for 3- and 5-year recurrence risk, respectively. The combined model incorporating the risk score, pN, and endocrine therapy showed improved predictive power, with AUCs of 0.857 and 0.912 in the training set and 0.943 and 0.945 in the testing set for 3- and 5-year recurrence risk, respectively. The calibration curve of the combined model showed good consistency between predicted and measured values. Our study developed an MRI-based radiomics model that integrates clinical and radiomics features to assess the likelihood of recurrence in luminal B breast cancer. The model shows promise for improving clinical risk stratification and treatment decision-making.
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Affiliation(s)
- Kepei Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Meiqi Hua
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Ting Mai
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Xiaojing Ren
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Xiaozheng Fang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Chunjie Wang
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang Province, Hangzhou, China
| | - Min Ge
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Hua Qian
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China.
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China.
| | - Ruixin Zhang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Hospital of Traditional Chinese Medicine), Zhejiang Province, Hangzhou, China.
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Zhejiang Province, Hangzhou, China.
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Guo J, Meng W, Li Q, Zheng Y, Yin H, Liu Y, Zhao S, Ma J. Pretreatment Sarcopenia and MRI-Based Radiomics to Predict the Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Bioengineering (Basel) 2024; 11:663. [PMID: 39061745 PMCID: PMC11274092 DOI: 10.3390/bioengineering11070663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/13/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its combination with MRI radiomic signatures can improve the predictive accuracy. We collected clinical and pathological information, as well as pretreatment breast MRI and abdominal CT images, of 121 patients with TNBC who underwent NAC at our hospital between January 2012 and September 2021. The presence of pretreatment sarcopenia was assessed using the L3 skeletal muscle index. Clinical models were constructed based on independent risk factors identified by univariate regression analysis. Radiomics data were extracted on breast MRI images and the radiomics prediction models were constructed. We integrated independent risk factors and radiomic features to build the combined models. The results of this study demonstrated that sarcopenia is an independent predictive factor for NAC efficacy in TNBC. The combination of sarcopenia and MRI radiomic signatures can further improve predictive performance.
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Affiliation(s)
- Jiamin Guo
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (J.G.); (Y.Z.)
| | - Wenjun Meng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China;
| | - Qian Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (Q.L.); (Y.L.)
| | - Yichen Zheng
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (J.G.); (Y.Z.)
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd., No. 62 East Fourth Ring Middle Road, Chaoyang District, Beijing 100025, China;
| | - Ying Liu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (Q.L.); (Y.L.)
| | - Shuang Zhao
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (Q.L.); (Y.L.)
| | - Ji Ma
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China; (J.G.); (Y.Z.)
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Jiang T, Yang S, Wang G, Tan Y, Liu S. Development and validation of survival nomograms in elder triple-negative invasive ductal breast carcinoma patients. Expert Rev Anticancer Ther 2024; 24:193-203. [PMID: 38366359 DOI: 10.1080/14737140.2024.2320815] [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: 08/30/2023] [Accepted: 12/06/2023] [Indexed: 02/18/2024]
Abstract
BACKGROUND We aimed to develop a nomogram to predict the overall survival of elderly patients with Triple-negative invasive ductal breast carcinoma (TNIDC). RESEARCH DESIGN AND METHODS 12165 elderly patients with nonmetastatic TNIDC were retrieved from the SEER database from 2010 to 2019 and were randomly assigned to training and validation cohorts. Stepwise Cox regression analysis was used to select variables for the nomogram based on the training cohort. Univariate and multivariate Cox analyses were used to calculate the correlation between variables and prognosis of the patients. Survival analysis was performed for high- and low-risk subgroups based on risk score. RESULTS Eleven predictive factors were identified to construct our nomograms. Compared with the TNM stage, the discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.741 (95% CI 0.728-0.754) against 0.708 (95% CI 0.694-0.721) and 0.765 (95% CI 0.747-0.783) against 0.725 (95% CI 0.705-0.744) for the training and validation cohorts, respectively. Differences in OS were also observed between the high- and low-risk groups (p < 0.001). CONCLUSION The proposed nomogram provides a convenient and reliable tool for individual evaluations for elderly patients with M0_stage TNIDC. However, the model may only for Americans.
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Affiliation(s)
- Tao Jiang
- Guizhou Medical University, Guiyang, Guizhou, China
| | - Sha Yang
- Medical College, Guizhou University Medical College, Guiyang, Guizhou Province, China
| | - Guanghui Wang
- Department of Breast Surgery, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Ying Tan
- Department of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Shu Liu
- Guizhou Medical University, Guiyang, Guizhou, China
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Corredor G, Bharadwaj S, Pathak T, Viswanathan VS, Toro P, Madabhushi A. A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives. Clin Breast Cancer 2023; 23:800-812. [PMID: 37380569 PMCID: PMC10733554 DOI: 10.1016/j.clbc.2023.06.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 06/30/2023]
Abstract
Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.
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Affiliation(s)
- Germán Corredor
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH
| | - Satvika Bharadwaj
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA
| | - Tilak Pathak
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA
| | - Vidya Sankar Viswanathan
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA
| | | | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA; Atlanta VA Medical Center, Atlanta, GA.
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Song SE, Woo OH, Cho Y, Cho KR, Park KH, Kim JW. Prediction of Axillary Lymph Node Metastasis in Early-stage Triple-Negative Breast Cancer Using Multiparametric and Radiomic Features of Breast MRI. Acad Radiol 2023; 30 Suppl 2:S25-S37. [PMID: 37331865 DOI: 10.1016/j.acra.2023.05.025] [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: 04/24/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate whether machine learning (ML) approaches using breast magnetic resonance imaging (MRI)-derived multiparametric and radiomic features could predict axillary lymph node metastasis (ALNM) in stage I-II triple-negative breast cancer (TNBC). MATERIALS AND METHODS Between 2013 and 2019, 86 consecutive patients with TNBC who underwent preoperative MRI and surgery were enrolled and divided into ALNM (N = 27) and non-ALNM (n = 59) groups according to histopathologic results. For multiparametric features, kinetic features using computer-aided diagnosis (CAD), morphologic features, and apparent diffusion coefficient (ADC) values at diffusion-weighted images were evaluated. For extracting radiomic features, three-dimensional segmentation of tumors using T2-weighted images (T2WI) and T1-weighted subtraction images were respectively performed by two radiologists. Each predictive model using three ML algorithms was built using multiparametric features or radiomic features, or both. The diagnostic performances of models were compared using the DeLong method. RESULTS Among multiparametric features, non-circumscribed margin, peritumoral edema, larger tumor size, and larger angio-volume at CAD were associated with ALNM in univariate analysis. In multivariate analysis, larger angio-volume was the sole statistically significant predictor for ALNM (odds ratio = 1.33, P = 0.008). Regarding ADC values, there were no significant differences according to ALNM status. The area under the receiver operating characteristic curve for predicting ALNM was 0.74 using multiparametric features, 0.77 using radiomic features from T1-weighted subtraction images, 0.80 using radiomic features from T2WI, and 0.82 using all features. CONCLUSION A predictive model incorporating breast MRI-derived multiparametric and radiomic features may be valuable in predicting ALNM preoperatively in patients with TNBC.
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Affiliation(s)
- Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (S.E.S., Y.C., KRC)
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea (O.H.W.).
| | - Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (S.E.S., Y.C., KRC)
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (S.E.S., Y.C., KRC)
| | - Kyong Hwa Park
- Department of Oncology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (K.H.P., J.W.K.)
| | - Ju Won Kim
- Department of Oncology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea (K.H.P., J.W.K.)
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Lu D, Yan Y, Jiang M, Sun S, Jiang H, Lu Y, Zhang W, Zhou X. Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis. Front Oncol 2023; 13:1173090. [PMID: 37664048 PMCID: PMC10469000 DOI: 10.3389/fonc.2023.1173090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Purpose This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy. Materials and methods This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software. Results A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up. Conclusion This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.
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Affiliation(s)
- Dongmei Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yuke Yan
- The Second Department of General Surgery, Gansu Provincial Hospital, Lanzhou, China
| | - Min Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Shaoqin Sun
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Haifeng Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yashan Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Xing Zhou
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
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De-Colle C, Kirby A, Russell N, Shaitelman S, Currey A, Donovan E, Hahn E, Han K, Anandadas C, Mahmood F, Lorenzen E, van den Bongard D, Groot Koerkamp M, Houweling A, Nachbar M, Thorwarth D, Zips D. Adaptive radiotherapy for breast cancer. Clin Transl Radiat Oncol 2023; 39:100564. [PMID: 36632056 PMCID: PMC9826896 DOI: 10.1016/j.ctro.2022.100564] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Research in the field of local and locoregional breast cancer radiotherapy aims to maintain excellent oncological outcomes while reducing treatment-related toxicity. Adaptive radiotherapy (ART) considers variations in target and organs at risk (OARs) anatomy occurring during the treatment course and integrates these in re-optimized treatment plans. Exploiting ART routinely in clinic may result in smaller target volumes and better OAR sparing, which may lead to reduction of acute as well as late toxicities. In this review MR-guided and CT-guided ART for breast cancer patients according to different clinical scenarios (neoadjuvant and adjuvant partial breast irradiation, whole breast, chest wall and regional nodal irradiation) are reviewed and their advantages as well as challenging aspects discussed.
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Affiliation(s)
- C. De-Colle
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - A. Kirby
- Department of Radiotherapy, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Sutton, United Kingdom
| | - N. Russell
- Department of Radiotherapy, The Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - S.F. Shaitelman
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - A. Currey
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - E. Donovan
- Department of Radiation Oncology, Odette Cancer Centre - Sunnybrook Health Sciences Centre, Toronto, Canada
| | - E. Hahn
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - K. Han
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - C.N. Anandadas
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - F. Mahmood
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - E.L. Lorenzen
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | | | - M.L. Groot Koerkamp
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - A.C. Houweling
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - M. Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology. University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - D. Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology. University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - D. Zips
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
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Thawani R, Gao L, Mohinani A, Tudorica A, Li X, Mitri Z, Huang W. Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study. BMC Med Imaging 2022; 22:182. [PMID: 36266631 PMCID: PMC9585714 DOI: 10.1186/s12880-022-00908-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/15/2022] [Accepted: 09/30/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. METHODS Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. RESULTS Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. CONCLUSION Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.
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Affiliation(s)
- Rajat Thawani
- Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US.
| | - Lina Gao
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Ajay Mohinani
- Department of Internal Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Alina Tudorica
- Department of Radiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
| | - Zahi Mitri
- Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US
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