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Mohamed RM, Panthi B, Adrada BE, Boge M, Candelaria RP, Chen H, Guirguis MS, Hunt KK, Huo L, Hwang KP, Korkut A, Litton JK, Moseley TW, Pashapoor S, Patel MM, Reed B, Scoggins ME, Son JB, Thompson A, Tripathy D, Valero V, Wei P, White J, Whitman GJ, Xu Z, Yang W, Yam C, Ma J, Rauch GM. Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Sci Rep 2024; 14:16073. [PMID: 38992094 PMCID: PMC11239818 DOI: 10.1038/s41598-024-66220-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 06/28/2024] [Indexed: 07/13/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.
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Affiliation(s)
- Rania M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Koc University Hospital, Istanbul, Turkey
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mary S Guirguis
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Kelly K Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tanya W Moseley
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Miral M Patel
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Brandy Reed
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jason White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA.
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Shokeir FA, Elmokadem AH, Soliman N, Khater A, Bayoumi D. Texture analysis using non-contrast MRI to predict estrogen receptor status in breast cancer lesions: Is it feasible? Clin Radiol 2024; 79:e892-e899. [PMID: 38719689 DOI: 10.1016/j.crad.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 06/02/2024]
Abstract
PURPOSE We aimed to evaluate the feasibility of non-contrast-enhanced T1 sequence in texture analysis of breast cancer lesions to predict their estrogen receptor status. METHODS The study included 85 pathologically proven breast cancer lesions in 53 patients. Immunohistochemical studies were performed to determine the estrogen receptor status (ER). Lesions were divided into two groups: ER + ve status and ER-ve status. Texture analysis using the second-order analysis features [The Co-occurrence matrix (11 features)] was applied on both T1 and dynamic contrast-enhanced (DCE) MRI images for each lesion. Texture features gained from both T1 and DCE images were analyzed to obtain cut-off values using ROC curves to sort lesions according to their estrogen receptor status. RESULTS Angular second momentum and some of the entropy-based features showed statistically significant cut-off values in differentiation between the two groups [P-values for pre- and post-contrast images for AngSecMom (0.001, 0.008), sum entropy (0.003,0.005), and entropy (0.033,0.019), respectively]. On comparing the AUCs between pre- and post-contrast images, we found that differences were statistically insignificant. Sum of squares, sum variance, and sum average showed statistically significant cut-off points only on pre-contrast images [P-values for sum of squares (0.018), sum variance (0.024), and sum average (0.039)]. CONCLUSIONS Texture analysis features showed promising results in predicting estrogen receptor status of breast cancer lesions on non-contrast T1 images.
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Affiliation(s)
- F A Shokeir
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
| | - A H Elmokadem
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
| | - N Soliman
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
| | - A Khater
- Mansoura University Oncology Center, Elgomhoria St. 35516, Egypt.
| | - D Bayoumi
- Department of Radiology, Mansoura University, Elgomhoria St. 35516, Egypt.
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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [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: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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Elsayed B, Alksas A, Shehata M, Mahmoud A, Zaky M, Alghandour R, Abdelwahab K, Abdelkhalek M, Ghazal M, Contractor S, El-Din Moustafa H, El-Baz A. Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review. Cancers (Basel) 2023; 15:5288. [PMID: 37958461 PMCID: PMC10648987 DOI: 10.3390/cancers15215288] [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: 09/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
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Affiliation(s)
- Basma Elsayed
- Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Alksas
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mona Zaky
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt;
| | - Reham Alghandour
- Medical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Khaled Abdelwahab
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohamed Abdelkhalek
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | | | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
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Liu M, Zhang Y, Li X, Hao Q, Li B, Wang R. MRI-based volume measurement methods for staging primary lower extremity lymphedema: a single-center study of asymmetric volume difference-a diagnostic study. BMC Musculoskelet Disord 2023; 24:810. [PMID: 37828475 PMCID: PMC10568749 DOI: 10.1186/s12891-023-06912-x] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 09/24/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Lower extremity lymphedema (LEL) staging is mainly assessed by systems that solely depend on physical examinations and lack quantitative assessment based on modern imaging. OBJECTIVE To explore the value of MRI-based asymmetric volume measurements in the clinical staging of primary LEL. METHODS 92 patients with unilateral primary LEL underwent MRI examinations to determine the volume of the mid-calf (Vcl) calculated using the clinical dermatome method as well as the total volume (Vmri), musculoskeletal volume (VM), and subcutaneous volume (VS) volume of the middle calves. The difference between Vmri (DVmri) and VS (DVS) of the affected and unaffected calves was obtained and defined as the asymmetric volume difference. Meanwhile, the volume of the mid-calf (Vcl) and the difference in volume (DVcl) were calculated using the clinical circumferential method. The relationship between the asymmetric volume difference and clinical staging was then evaluated. Interobserver consistency was assessed through the intraclass correlation coefficient (ICC). Volume comparisons between the three groups were performed using the one-way analysis of variance (ANOVA) or the Kruskal-Wallis test. Spearman's correlation was used to assess volume and clinical stage correlation. The receiver operating characteristic (ROC) curve was used to evaluate the value of asymmetric volume difference for clinical staging. RESULTS The asymmetric volume difference was statistically significant in stage I compared to stages II and III (p < 0.05). The asymmetric volume difference (DVmri: r = 0.753; DVS: r = 0.759) correlated more with the clinical stage than the affected Vcl (r = 0.581), Vmri (r = 0.628), VS (r = 0.743), and DVcl (r = 0.718). The area under the ROC curve (AUC) for identifying the clinical stage by the asymmetric volume difference was greater than that for the affected Vcl, Vmri, VS, and DVcl, with DVS (AUC = 0.951) having the largest area under the curve to distinguish between stages I and II. CONCLUSION MRI-based asymmetric volume difference is an adjunctive measure for LEL clinical staging with good reproducibility. DVS could be the best indicator for differentiating between stages I and II of primary LEL.
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Affiliation(s)
- Mengke Liu
- Department of Radiology, Affiliated Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Yan Zhang
- Department of Radiology, Affiliated Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Xingpeng Li
- Department of Radiology, Affiliated Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Qi Hao
- Department of Radiology, Affiliated the ninth Clinical Medical College, Peking University, Beijing, 100038, China
| | - Bin Li
- Department of MRI, Affiliated Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
| | - Rengui Wang
- Department of Radiology, Affiliated Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
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Park J, Kim MJ, Yoon JH, Han K, Kim EK, Sohn JH, Lee YH, Yoo Y. Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features. Diagnostics (Basel) 2023; 13:3031. [PMID: 37835774 PMCID: PMC10572844 DOI: 10.3390/diagnostics13193031] [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: 09/05/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. METHODS We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. RESULTS The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). CONCLUSIONS We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.
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Affiliation(s)
- Jiwoo Park
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.P.); (J.-H.Y.); (K.H.); (J.H.S.); (Y.H.L.)
| | - Min Jung Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.P.); (J.-H.Y.); (K.H.); (J.H.S.); (Y.H.L.)
| | - Jong-Hyun Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.P.); (J.-H.Y.); (K.H.); (J.H.S.); (Y.H.L.)
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.P.); (J.-H.Y.); (K.H.); (J.H.S.); (Y.H.L.)
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 06230, Republic of Korea;
| | - Joo Hyuk Sohn
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.P.); (J.-H.Y.); (K.H.); (J.H.S.); (Y.H.L.)
| | - Young Han Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.P.); (J.-H.Y.); (K.H.); (J.H.S.); (Y.H.L.)
| | - Yangmo Yoo
- Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea;
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Yoen H, Kim SY, Lee DW, Lee HB, Cho N. Prediction of Tumor Progression During Neoadjuvant Chemotherapy and Survival Outcome in Patients With Triple-Negative Breast Cancer. Korean J Radiol 2023; 24:626-639. [PMID: 37404105 DOI: 10.3348/kjr.2022.0974] [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: 12/07/2022] [Revised: 03/31/2023] [Accepted: 05/01/2023] [Indexed: 07/06/2023] Open
Abstract
OBJECTIVE To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC). MATERIALS AND METHODS This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them. RESULTS Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set. CONCLUSION The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.
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Affiliation(s)
- Heera Yoen
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Dae-Won Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Han-Byoel Lee
- Department of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
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Wang X, Hua H, Han J, Zhong X, Liu J, Chen J. Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study. Clin Breast Cancer 2023:S1526-8209(23)00134-9. [PMID: 37321954 DOI: 10.1016/j.clbc.2023.05.010] [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: 03/16/2023] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/17/2023]
Abstract
INTRODUCTION This study evaluated the performance of primary foci of breast cancer on multiparametric magnetic resonance imaging (MRI) contributing to establish and validate radiomics-based nomograms for predicting the different pathological outcome of breast cancer patients after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS Retrospectively collected 387 patients with locally advanced breast cancer, all treated with NAC and received breast dynamic contrast-enhanced MRI (DCE-MRI) before NAC. Radiomics signatures were extracted from region of interest (ROI) on multiparametric MRI to build rad score. Clinical-pathologic data and radiological features established the clinical model. The comprehensive model featured rad-score, predictive clinical-pathologic data and radiological features, which was ultimately displayed as a nomogram. Patients were grouped in 2 different ways in accordance with the Miller-Payne (MP) grading of surgical specimens. The first grouping method: 181 patients with pathological reaction grades Ⅳ∼Ⅴ were included in the significant remission group, while 206 patients with pathological reaction grades Ⅰ∼Ⅲ were included in the nonsignificant remission group. The second grouping method: 117 patients with pathological complete response (pCR) were assigned to the pCR group, and 270 patients who failed to meet pCR were assigned to in the non-pCR group. Two combined nomograms are created from 2 grouped data for predicting different pathological responses to NAC. The area under the curves (AUC) of the receiver operating characteristic curves (ROC) were used to evaluate the performance of each model. While decision curve analysis (DCA) and calibration curves were used for estimating the clinical application value of the nomogram. RESULTS Two combined nomograms embodying rad score and clinical-pathologic data outperformed, showing good calibrations for predicting response to NAC. The combined nomogram predicting pCR showed the best performance with the AUC values of 0.97, 0.90 and 0.86 in the training, testing, and external validation cohorts respectively. The AUC values of another combined nomogram predicting significant remission: 0.98, 0.88 0.80 in the training, testing and external validation cohorts. DCA showed the comprehensive model nomogram obtained the most clinical benefit. CONCLUSIONS The combined nomogram could preoperatively predict significant remission or even pCR to NAC in breast cancer based on multiparametric MRI and clinical-pathologic data.
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Affiliation(s)
- Xiaolin Wang
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hui Hua
- Department of Thyroid Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Junqi Han
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin Zhong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Liu
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Chen
- Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China.
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Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer. PLoS One 2023; 18:e0280320. [PMID: 36649274 PMCID: PMC9844845 DOI: 10.1371/journal.pone.0280320] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 12/27/2022] [Indexed: 01/18/2023] Open
Abstract
PURPOSE To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. MATERIAL AND METHODS This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall. RESULTS Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p<0.001) and (AUC = 0.903 95% CI: 0.854, 0.952; p<0.001) respectively), outperformed that using mean tumor ADC (AUC = 0.850 (95% CI: 0.764, 0.936; p<0.001)). The AUC using all MRI data was 0.933 (95% CI: 0.836, 1.03; p<0.001). The AUC using non-MRI data was 0.919 (95% CI: 0.848, 0.99; p<0.001). The highest AUC of 0.951 (95% CI: 0.909, 0.993; p<0.001) was achieved with all MRI and all non-MRI data at all time points as inputs. CONCLUSION Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes.
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Luo Z, Li J, Liao Y, Huang W, Li Y, Shen X. Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:611-626. [PMID: 37005907 DOI: 10.3233/xst-221352] [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
PURPOSE This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n = 72; validation dataset, n = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583-0.937), 0.706 (95% CI: 0.506-0.905), 0.751 (95% CI: 0.572-0.930), 0.796 (95% CI: 0.629-0.963), 0.828 (95% CI: 0.676-0.980), respectively. The DeLong test showed no significant difference between any pair of models (p > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). CONCLUSION Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data.
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Affiliation(s)
- Zhendong Luo
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Jing Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | | | - Wenxiao Huang
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Yulin Li
- Department of Radiology, Peking Universtiy Shenzhen Hospital, Shenzhen, China
| | - Xinping Shen
- Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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11
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O'Donnell J, Gasior S, Davey M, O'Malley E, Lowery A, McGarry J, O'Connell A, Kerin M, McCarthy P. The accuracy of breast MRI radiomic methodologies in predicting pathological complete response to neoadjuvant chemotherapy: A systematic review and network meta-analysis. Eur J Radiol 2022; 157:110561. [DOI: 10.1016/j.ejrad.2022.110561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/13/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2022]
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12
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Zhang MQ, Du Y, Zha HL, Liu XP, Cai MJ, Chen ZH, Chen R, Wang J, Wang SJ, Zhang JL, Li CY. Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy. Br J Radiol 2022; 95:20220626. [PMID: 36378247 PMCID: PMC9733610 DOI: 10.1259/bjr.20220626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/26/2022] [Accepted: 09/10/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC). METHODS A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA). RESULTS Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4-5 patients was significantly superior than the clinical model and Rad-score alone (p < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814-0.963) in the training group and 0.854 (95% CI, 0.776-0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor. CONCLUSION This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers. ADVANCES IN KNOWLEDGE Aiming at early detection of Grade 4-5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.
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Affiliation(s)
- Man-Qi Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Du
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai-Ling Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin-Pei Liu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Meng-Jun Cai
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhi-Hui Chen
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Rui Chen
- Department of Breast surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jue Wang
- Department of Breast surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shou-Ju Wang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu-Lou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
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Pesapane F, Agazzi GM, Rotili A, Ferrari F, Cardillo A, Penco S, Dominelli V, D'Ecclesiis O, Vignati S, Raimondi S, Bozzini A, Pizzamiglio M, Petralia G, Nicosia L, Cassano E. Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients With MRI-Radiomics: A Systematic Review and Meta-analysis. Curr Probl Cancer 2022; 46:100883. [PMID: 35914383 DOI: 10.1016/j.currproblcancer.2022.100883] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 12/30/2022]
Abstract
We performed a systematic review and a meta-analysis of studies using MRI-radiomics for predicting the pathological complete response in breast cancer patients undergoing neoadjuvant therapy , and we evaluated their methodological quality using the radiomics-quality-score (RQS). Random effects meta-analysis was performed pooling area under the receiver operating characteristics curves. Publication-bias was assessed using the Egger's test and visually inspecting the funnel plot. Forty-three studies were included in the qualitative review and 34 in the meta-analysis. Summary area under the receiver operating characteristics curve was 0,78 (95%CI:0,74-0,81). Heterogeneity according to the I2 statistic was substantial (71%) and there was no evidence of publication bias (P-value = 0,2). The average RQS was 12,7 (range:-1-26), with an intra-class correlation coefficient of 0.93 (95%CI:0.61-0.97). Year of publication, field intensity and synthetic RQS score do not appear to be moderators of the effect (P-value = 0.36, P-value = 0.28 and P-value = 0.92, respectively). MRI-radiomics may predict response to neoadjuvant therapy in breast cancer patients but the heterogeneity of the current studies is still substantial.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | | | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Andrea Cardillo
- Radiology Department, Università degli studi di Torino, Turin, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Oriana D'Ecclesiis
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Silvano Vignati
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Anna Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Pizzamiglio
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy; Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Milan, Italy
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15
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Herrero Vicent C, Tudela X, Moreno Ruiz P, Pedralva V, Jiménez Pastor A, Ahicart D, Rubio Novella S, Meneu I, Montes Albuixech Á, Santamaria MÁ, Fonfria M, Fuster-Matanzo A, Olmos Antón S, Martínez de Dueñas E. Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2022; 14:cancers14143508. [PMID: 35884572 PMCID: PMC9317428 DOI: 10.3390/cancers14143508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/07/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial, as pCR is a surrogate marker for survival. However, only 10–30% of patients achieve it. It is therefore essential to develop tools that enable to accurately predict response. Recently, different studies have demonstrated the feasibility of applying machine learning approaches to non-invasively predict pCR before NAC administration from magnetic resonance imaging (MRI) data. Some of those models are based on radiomics, an emerging field that allows the automated extraction of clinically relevant information from radiologic images. However, the research is still at an early stage and further data are needed. Here, we determine whether the combination of imaging data (radiomic features and perfusion/diffusion imaging biomarkers) extracted from multiparametric MRIs and clinical variables can improve pCR prediction to NAC compared to models only including imaging or clinical data, potentially avoiding unnecessary treatment and delays to surgery. Abstract Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR.
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Affiliation(s)
- Carmen Herrero Vicent
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
- Correspondence:
| | - Xavier Tudela
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Paula Moreno Ruiz
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Víctor Pedralva
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ana Jiménez Pastor
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Daniel Ahicart
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Silvia Rubio Novella
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Isabel Meneu
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - Ángela Montes Albuixech
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Miguel Ángel Santamaria
- Radiodiagnosis Department, The Provincial Hospital of Castellon, 12100 Castellon, Spain; (X.T.); (V.P.); (D.A.); (I.M.); (M.Á.S.)
| | - María Fonfria
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Almudena Fuster-Matanzo
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (P.M.R.); (A.J.P.); (A.F.-M.)
| | - Santiago Olmos Antón
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
| | - Eduardo Martínez de Dueñas
- Medical Oncology Department, The Provincial Hospital of Castellon, 12002 Castellon, Spain; (S.R.N.); (Á.M.A.); (M.F.); (S.O.A.); (E.M.d.D.)
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16
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Zhu X, Shen J, Zhang H, Wang X, Zhang H, Yu J, Zhang Q, Song D, Guo L, Zhang D, Zhu R, Wu J. A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study. Front Oncol 2022; 12:916526. [PMID: 35734603 PMCID: PMC9207207 DOI: 10.3389/fonc.2022.916526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 05/02/2022] [Indexed: 12/03/2022] Open
Abstract
Objective To explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST). Methods We enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) curve (AUC) and calibration curve. The clinical net benefit of the model was evaluated using decision curve analysis (DCA). Results The mpMRI RAD-score had the highest diagnostic performance, with AUC of 0.848 among the four RAD-scores. T stage, human epidermal growth factor receptor-2 (HER2) status, RAD-score, and roundness were independent factors for predicting the pCR (P < 0.05 for all). The combined nomogram model based on these factors achieved AUCs of 0.930 and 0.895 in the training cohort and validation cohort, respectively, higher than other models (P < 0.05 for all). The calibration curve showed that the predicted probabilities of the nomogram were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and validation datasets. Conclusion The combined nomogram model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.
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Affiliation(s)
- Xuelin Zhu
- Graduate School, Tianjin Medical University, Tianjin, China.,Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.,Department of Ultrasound, Qingzhou People's Hospital, Weifang, China
| | - Jing Shen
- Graduate School, Tianjin Medical University, Tianjin, China.,Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Huanlei Zhang
- Department of Radiology, Yidu Central Hospital of Weifang, Weifang, China
| | - Xiulin Wang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.,School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Huihui Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jing Yu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qing Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Dongdong Song
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Liping Guo
- Department of Ultrasound, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Dianlong Zhang
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Ruiping Zhu
- Department of Pathology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
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Yoshida K, Kawashima H, Kannon T, Tajima A, Ohno N, Terada K, Takamatsu A, Adachi H, Ohno M, Miyati T, Ishikawa S, Ikeda H, Gabata T. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI. Magn Reson Imaging 2022; 92:19-25. [PMID: 35636571 DOI: 10.1016/j.mri.2022.05.018] [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: 06/22/2021] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE To investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS Seventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal 1 min - Signal pre)/Signal pre. Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed. RESULTS The best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features. CONCLUSIONS Pretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.
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Affiliation(s)
- Kotaro Yoshida
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Hiroko Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Takayuki Kannon
- Department of Bioinformatics and Genomics, Graduate School of Advanced Preventive Medical Sciences, Kanazawa University, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Atsushi Tajima
- Department of Bioinformatics and Genomics, Graduate School of Advanced Preventive Medical Sciences, Kanazawa University, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Naoki Ohno
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Kanako Terada
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan
| | - Atsushi Takamatsu
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan
| | - Hayato Adachi
- Division of Radiology, Kanazawa University Hospital, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan
| | - Masako Ohno
- Division of Radiology, Kanazawa University Hospital, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Tosiaki Miyati
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Satoko Ishikawa
- Department of Breast Surgery, Kanazawa University Hospital, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Hiroko Ikeda
- Diagnostic Pathology, Kanazawa University Hospital, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Toshifumi Gabata
- Department of Radiology, Kanazawa University Graduate School of Medical Sciences, 13-1, Takaramachi, Kanazawa, Ishikawa 920-8641, Japan.
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Liang X, Yu X, Gao T. Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis. Eur J Radiol 2022; 150:110247. [DOI: 10.1016/j.ejrad.2022.110247] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 02/14/2022] [Accepted: 03/07/2022] [Indexed: 12/18/2022]
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Chen K, Li H, Pan Z, Wu Z, Song E. Insights into artificial intelligence in clinical oncology: opportunities and challenges. SCIENCE CHINA. LIFE SCIENCES 2022; 65:643-647. [PMID: 34846642 DOI: 10.1007/s11427-021-2010-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/18/2021] [Indexed: 06/13/2023]
Affiliation(s)
- Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Hanwei Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Zhanpeng Pan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Zhuo Wu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
- Fountain-Valley Institute for Life Sciences, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Bioland Laboratory, Guangzhou, 510005, China.
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20
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Mao N, Shi Y, Lian C, Wang Z, Zhang K, Xie H, Zhang H, Chen Q, Cheng G, Xu C, Dai Y. Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography. Eur Radiol 2022; 32:3207-3219. [DOI: 10.1007/s00330-021-08414-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/26/2021] [Accepted: 10/13/2021] [Indexed: 12/14/2022]
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21
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Xia B, Wang H, Wang Z, Qian Z, Xiao Q, Liu Y, Shao Z, Zhou S, Chai W, You C, Gu Y. A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy. Front Genet 2021; 12:783513. [PMID: 34868273 PMCID: PMC8632946 DOI: 10.3389/fgene.2021.783513] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS. Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation. Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p<0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761-0.907) and 0.868 (95% CI, 0.787-949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709-0.743; validation group: C-index = 0.774,95% CI = 0.743-0.805). Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.
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Affiliation(s)
- Bingqing Xia
- International Peace Maternity and Child Health Hospital, Shanghai, China.,Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zhe Wang
- Shanghai United Imaging Medical Technology Co., Ltd., Shanghai, China
| | - Zhaoxia Qian
- International Peace Maternity and Child Health Hospital, Shanghai, China
| | - Qin Xiao
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yin Liu
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhimin Shao
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shuling Zhou
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weimin Chai
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chao You
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yajia Gu
- Shanghai Cancer Center, Fudan University, Shanghai, China
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22
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Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021; 127:39-56. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.
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Affiliation(s)
- Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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23
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Bian T, Wu Z, Lin Q, Mao Y, Wang H, Chen J, Chen Q, Fu G, Cui C, Su X. Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer Using Preoperative MRI-Based Radiomics. J Magn Reson Imaging 2021; 55:772-784. [PMID: 34453461 DOI: 10.1002/jmri.27910] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/20/2021] [Accepted: 08/20/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Evaluating tumor-infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored. PURPOSE To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level. STUDY TYPE Retrospective. POPULATION A total of 154 patients with breast cancer were divided into a training cohort (N = 87) and a test cohort (N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results. FIELD STRENGTH/SEQUENCE 3.0 T; axial T2-weighted imaging (fast spin echo), diffusion-weighted imaging (spin echo-echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo). ASSESSMENT A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated. STATISTICAL TESTS The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t-tests and chi-squared tests or Fisher's exact test, Hosmer-Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant. RESULTS The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9. DATA CONCLUSION The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Tiantian Bian
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zengjie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qing Lin
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yan Mao
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haibo Wang
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jingjing Chen
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qianqian Chen
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Guangming Fu
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chunxiao Cui
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaohui Su
- Breast Disease Center, The Affiliated Hospital of Qingdao University, Qingdao, China
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Grimm LJ. Radiomics: A Primer for Breast Radiologists. JOURNAL OF BREAST IMAGING 2021; 3:276-287. [PMID: 38424774 DOI: 10.1093/jbi/wbab014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Indexed: 03/02/2024]
Abstract
Radiomics has a long-standing history in breast imaging with computer-aided detection (CAD) for screening mammography developed in the late 20th century. Although conventional CAD had widespread adoption, the clinical benefits for experienced breast radiologists were debatable due to high false-positive marks and subsequent increased recall rates. The dramatic growth in recent years of artificial intelligence-based analysis, including machine learning and deep learning, has provided numerous opportunities for improved modern radiomics work in breast imaging. There has been extensive radiomics work in mammography, digital breast tomosynthesis, MRI, ultrasound, PET-CT, and combined multimodality imaging. Specific radiomics outcomes of interest have been diverse, including CAD, prediction of response to neoadjuvant therapy, lesion classification, and survival, among other outcomes. Additionally, the radiogenomics subfield that correlates radiomics features with genetics has been very proliferative, in parallel with the clinical validation of breast cancer molecular subtypes and gene expression assays. Despite the promise of radiomics, there are important challenges related to image normalization, limited large unbiased data sets, and lack of external validation. Much of the radiomics work to date has been exploratory using single-institution retrospective series for analysis, but several promising lines of investigation have made the leap to clinical practice with commercially available products. As a result, breast radiologists will increasingly be incorporating radiomics-based tools into their daily practice in the near future. Therefore, breast radiologists must have a broad understanding of the scope, applications, and limitations of radiomics work.
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Affiliation(s)
- Lars J Grimm
- Duke University, Department of Radiology, Durham, NC, USA
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25
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Montemezzi S, Benetti G, Bisighin MV, Camera L, Zerbato C, Caumo F, Fiorio E, Zanelli S, Zuffante M, Cavedon C. 3T DCE-MRI Radiomics Improves Predictive Models of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Front Oncol 2021; 11:630780. [PMID: 33959498 PMCID: PMC8093630 DOI: 10.3389/fonc.2021.630780] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/30/2021] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES To test whether 3T MRI radiomics of breast malignant lesions improves the performance of predictive models of complete response to neoadjuvant chemotherapy when added to other clinical, histological and radiological information. METHODS Women who consecutively had pre-neoadjuvant chemotherapy (NAC) 3T DCE-MRI between January 2016 and October 2019 were retrospectively included in the study. 18F-FDG PET-CT and histological information obtained through lesion biopsy were also available. All patients underwent surgery and specimens were analyzed. Subjects were divided between complete responders (Pinder class 1i or 1ii) and non-complete responders to NAC. Geometric, first order or textural (higher order) radiomic features were extracted from pre-NAC MRI and feature reduction was performed. Five radiomic features were added to other available information to build predictive models of complete response to NAC using three different classifiers (logistic regression, support vector machines regression and random forest) and exploring the whole set of possible feature selections. RESULTS The study population consisted of 20 complete responders and 40 non-complete responders. Models including MRI radiomic features consistently showed better performance compared to combinations of other clinical, histological and radiological information. The AUC (ROC analysis) of predictors that did not include radiomic features reached up to 0.89, while all three classifiers gave AUC higher than 0.90 with the inclusion of radiomic information (range: 0.91-0.98). CONCLUSIONS Radiomic features extracted from 3T DCE-MRI consistently improved predictive models of complete response to neo-adjuvant chemotherapy. However, further investigation is necessary before this information can be used for clinical decision making.
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Affiliation(s)
| | - Giulio Benetti
- Medical Physics Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | | | - Lucia Camera
- Radiology Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Chiara Zerbato
- Radiology Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Francesca Caumo
- Radiology Unit, Istituto Oncologico Veneto – IRCCS, Padova, Italy
| | - Elena Fiorio
- Pathology Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Sara Zanelli
- Pathology Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Michele Zuffante
- Nuclear Medicine Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Carlo Cavedon
- Medical Physics Unit, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
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26
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Kim SY, Cho N, Choi Y, Lee SH, Ha SM, Kim ES, Chang JM, Moon WK. Factors Affecting Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: Development and Validation of a Predictive Nomogram. Radiology 2021; 299:290-300. [PMID: 33754824 DOI: 10.1148/radiol.2021203871] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background There is an increasing need to develop a more accurate prediction model for pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Purpose To develop a nomogram based on MRI and clinical-pathologic variables to predict pCR. Materials and Methods In this single-center retrospective study, consecutive women with stage II-III breast cancer who underwent NAC followed by surgery between January 2011 and December 2017 were considered for inclusion. The women were divided into a development cohort between January 2011 and September 2015 and a validation cohort between October 2015 and December 2017. Clinical-pathologic data were collected, and mammograms and MRI scans obtained before and after NAC were analyzed. Logistic regression analyses were performed to identify independent variables associated with pCR in the development cohort from which the nomogram was created. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results A total of 359 women (mean age, 49 years ± 10 [standard deviation]) were in the development cohort and 351 (49 years ± 10) in the validation cohort. Hormone receptor negativity (odds ratio [OR], 3.1; 95% CI: 1.4, 7.1; P = .006), high Ki-67 index (OR, 1.05; 95% CI: 1.03, 1.07; P < .001), and post-NAC MRI variables, including small tumor size (OR, 0.6; 95% CI: 0.4, 0.9; P = .03), low lesion-to-background parenchymal signal enhancement ratio (OR, 0.2; 95% CI: 0.1, 0.6; P = .004), and absence of enhancement in the tumor bed (OR, 3.8; 95% CI: 1.4, 10.5; P = .009) were independently associated with pCR. The nomogram incorporating these variables showed good discrimination (AUC, 0.90; 95% CI: 0.86, 0.94) and calibration abilities (calibration slope, 0.91; 95% CI: 0.69, 1.13) in the independent validation cohort. Conclusion A nomogram incorporating hormone receptor status, Ki-67 index, and MRI variables showed good discrimination and calibration abilities in predicting pathologic complete response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco and Ponsiglione in this issue.
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Affiliation(s)
- Soo-Yeon Kim
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
| | - Nariya Cho
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
| | - Yunhee Choi
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
| | - Su Hyun Lee
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
| | - Su Min Ha
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
| | - Eun Sil Kim
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
| | - Jung Min Chang
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
| | - Woo Kyung Moon
- From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.)
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