Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model
ABSTRACT Background Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI)‐acquired pre‐ and mid‐treatment quantitatively capture information about tumor heterogeneity as potential e...
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Wiley
2024-12-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70482 |
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| author | Maria Colomba Comes Annarita Fanizzi Samantha Bove Luca Boldrini Agnese Latorre Deniz Can Guven Serena Iacovelli Tiziana Talienti Alessandro Rizzo Francesco Alfredo Zito Raffaella Massafra |
| author_facet | Maria Colomba Comes Annarita Fanizzi Samantha Bove Luca Boldrini Agnese Latorre Deniz Can Guven Serena Iacovelli Tiziana Talienti Alessandro Rizzo Francesco Alfredo Zito Raffaella Massafra |
| author_sort | Maria Colomba Comes |
| collection | DOAJ |
| description | ABSTRACT Background Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI)‐acquired pre‐ and mid‐treatment quantitatively capture information about tumor heterogeneity as potential earlier indicators of pathological complete response (pCR) to NAC in breast cancer. Aims This study aimed to develop an ensemble deep learning‐based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre‐ and mid‐treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC. Materials and Methods Imaging data analyzed in this study referred to a cohort of 86 breast cancer patients, randomly split into training and test sets at a ratio of 8:2, who underwent NAC and for which information regarding the pCR status was available (37.2% of patients achieved pCR). We further validated our model using a subset of 20 patients selected from the publicly available I‐SPY2 trial dataset (independent test). Results The performances of the proposed model were assessed using standard evaluation metrics, and promising results were achieved: area under the curve (AUC) value of 91.4%, accuracy value of 82.4%, a specificity value of 80.0%, a sensitivity value of 85.7%, precision value of 75.0%, F‐score value of 80.0%, and G‐mean value of 82.8%. The results obtained from the independent test show an AUC of 81.3%, an accuracy of 80.0%, a specificity value of 76.9%, a sensitivity of 85.0%, a precision of 66.7%, an F‐score of 75.0%, and a G‐mean of 81.2%. Discussion As far as we know, our research is the first proposal using ViTs on DCE‐MRI exams to monitor pCR over time during NAC. Conclusion Finally, the changes in DCE‐MRI at pre‐ and mid‐treatment could affect the accuracy of pCR prediction to NAC. |
| format | Article |
| id | doaj-art-4c618ba6281f4e87ba73cd474cb672b8 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
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| series | Cancer Medicine |
| spelling | doaj-art-4c618ba6281f4e87ba73cd474cb672b82025-01-20T10:51:32ZengWileyCancer Medicine2045-76342024-12-011324n/an/a10.1002/cam4.70482Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based ModelMaria Colomba Comes0Annarita Fanizzi1Samantha Bove2Luca Boldrini3Agnese Latorre4Deniz Can Guven5Serena Iacovelli6Tiziana Talienti7Alessandro Rizzo8Francesco Alfredo Zito9Raffaella Massafra10Laboratorio di Biostatistica e Bioinformatica I.R.C.C.S. Istituto Tumori “Giovanni Paolo II” Bari ItalyLaboratorio di Biostatistica e Bioinformatica I.R.C.C.S. Istituto Tumori “Giovanni Paolo II” Bari ItalyLaboratorio di Biostatistica e Bioinformatica I.R.C.C.S. Istituto Tumori “Giovanni Paolo II” Bari ItalyUnità Operativa Complessa di Radioterapia Oncologica Fondazione Policlinico Universitario Agostino Gemelli I.R.C.C.S Rome ItalyUnità Operativa Complessa di Oncologia Medica I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Bari Bari ItalyDepartment of Medical Oncology Hacettepe University, Cancer Institute Ankara TurkeyTrial Office, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II” Bari Bari ItalyUnità Operativa Complessa di Oncologia Medica I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Bari Bari ItalyStruttura Semplice Dipartimentale di Oncologia Medica per la Presa in Carico Globale del Paziente Oncologico “Don Tonino Bello” I.R.C.C.S. Istituto Tumori “Giovanni Paolo II” Bari ItalyUnità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II” Bari ItalyLaboratorio di Biostatistica e Bioinformatica I.R.C.C.S. Istituto Tumori “Giovanni Paolo II” Bari ItalyABSTRACT Background Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI)‐acquired pre‐ and mid‐treatment quantitatively capture information about tumor heterogeneity as potential earlier indicators of pathological complete response (pCR) to NAC in breast cancer. Aims This study aimed to develop an ensemble deep learning‐based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre‐ and mid‐treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC. Materials and Methods Imaging data analyzed in this study referred to a cohort of 86 breast cancer patients, randomly split into training and test sets at a ratio of 8:2, who underwent NAC and for which information regarding the pCR status was available (37.2% of patients achieved pCR). We further validated our model using a subset of 20 patients selected from the publicly available I‐SPY2 trial dataset (independent test). Results The performances of the proposed model were assessed using standard evaluation metrics, and promising results were achieved: area under the curve (AUC) value of 91.4%, accuracy value of 82.4%, a specificity value of 80.0%, a sensitivity value of 85.7%, precision value of 75.0%, F‐score value of 80.0%, and G‐mean value of 82.8%. The results obtained from the independent test show an AUC of 81.3%, an accuracy of 80.0%, a specificity value of 76.9%, a sensitivity of 85.0%, a precision of 66.7%, an F‐score of 75.0%, and a G‐mean of 81.2%. Discussion As far as we know, our research is the first proposal using ViTs on DCE‐MRI exams to monitor pCR over time during NAC. Conclusion Finally, the changes in DCE‐MRI at pre‐ and mid‐treatment could affect the accuracy of pCR prediction to NAC.https://doi.org/10.1002/cam4.70482breast cancerensemble modelNeoadjuvant chemotherapypathological complete responsevision transformers |
| spellingShingle | Maria Colomba Comes Annarita Fanizzi Samantha Bove Luca Boldrini Agnese Latorre Deniz Can Guven Serena Iacovelli Tiziana Talienti Alessandro Rizzo Francesco Alfredo Zito Raffaella Massafra Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model Cancer Medicine breast cancer ensemble model Neoadjuvant chemotherapy pathological complete response vision transformers |
| title | Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model |
| title_full | Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model |
| title_fullStr | Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model |
| title_full_unstemmed | Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model |
| title_short | Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model |
| title_sort | monitoring over time of pathological complete response to neoadjuvant chemotherapy in breast cancer patients through an ensemble vision transformers based model |
| topic | breast cancer ensemble model Neoadjuvant chemotherapy pathological complete response vision transformers |
| url | https://doi.org/10.1002/cam4.70482 |
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