A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients
Abstract In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated pe...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-11876-4 |
| _version_ | 1828221895985594368 |
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| author | Samantha Bove Maria Colomba Comes Vito Lorusso Cristian Cristofaro Vittorio Didonna Gianluca Gatta Francesco Giotta Daniele La Forgia Agnese Latorre Maria Irene Pastena Nicole Petruzzellis Domenico Pomarico Lucia Rinaldi Pasquale Tamborra Alfredo Zito Annarita Fanizzi Raffaella Massafra |
| author_facet | Samantha Bove Maria Colomba Comes Vito Lorusso Cristian Cristofaro Vittorio Didonna Gianluca Gatta Francesco Giotta Daniele La Forgia Agnese Latorre Maria Irene Pastena Nicole Petruzzellis Domenico Pomarico Lucia Rinaldi Pasquale Tamborra Alfredo Zito Annarita Fanizzi Raffaella Massafra |
| author_sort | Samantha Bove |
| collection | DOAJ |
| description | Abstract In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients. |
| first_indexed | 2024-04-12T16:47:42Z |
| format | Article |
| id | doaj.art-21667fa02e724bf7860830faad46bc80 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T16:47:42Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-21667fa02e724bf7860830faad46bc802022-12-22T03:24:31ZengNature PortfolioScientific Reports2045-23222022-05-0112111010.1038/s41598-022-11876-4A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patientsSamantha Bove0Maria Colomba Comes1Vito Lorusso2Cristian Cristofaro3Vittorio Didonna4Gianluca Gatta5Francesco Giotta6Daniele La Forgia7Agnese Latorre8Maria Irene Pastena9Nicole Petruzzellis10Domenico Pomarico11Lucia Rinaldi12Pasquale Tamborra13Alfredo Zito14Annarita Fanizzi15Raffaella Massafra16Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Dipartimento Di Medicina Di Precisione, Università Della Campania “Luigi Vanvitelli”Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Unità Operativa Complessa Di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Oncologia Per La Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Unità Operativa Complessa Di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Struttura Semplice Dipartimentale Di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”Abstract In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.https://doi.org/10.1038/s41598-022-11876-4 |
| spellingShingle | Samantha Bove Maria Colomba Comes Vito Lorusso Cristian Cristofaro Vittorio Didonna Gianluca Gatta Francesco Giotta Daniele La Forgia Agnese Latorre Maria Irene Pastena Nicole Petruzzellis Domenico Pomarico Lucia Rinaldi Pasquale Tamborra Alfredo Zito Annarita Fanizzi Raffaella Massafra A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients Scientific Reports |
| title | A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients |
| title_full | A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients |
| title_fullStr | A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients |
| title_full_unstemmed | A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients |
| title_short | A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients |
| title_sort | ultrasound based radiomic approach to predict the nodal status in clinically negative breast cancer patients |
| url | https://doi.org/10.1038/s41598-022-11876-4 |
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