Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study
In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/2/352 |
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| author | Annarita Fanizzi Domenico Pomarico Angelo Paradiso Samantha Bove Sergio Diotaiuti Vittorio Didonna Francesco Giotta Daniele La Forgia Agnese Latorre Maria Irene Pastena Pasquale Tamborra Alfredo Zito Vito Lorusso Raffaella Massafra |
| author_facet | Annarita Fanizzi Domenico Pomarico Angelo Paradiso Samantha Bove Sergio Diotaiuti Vittorio Didonna Francesco Giotta Daniele La Forgia Agnese Latorre Maria Irene Pastena Pasquale Tamborra Alfredo Zito Vito Lorusso Raffaella Massafra |
| author_sort | Annarita Fanizzi |
| collection | DOAJ |
| description | In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients. |
| first_indexed | 2024-03-09T04:21:56Z |
| format | Article |
| id | doaj.art-630cea344153483e82e56707db469157 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T04:21:56Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-630cea344153483e82e56707db4691572023-12-03T13:46:10ZengMDPI AGCancers2072-66942021-01-0113235210.3390/cancers13020352Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation StudyAnnarita Fanizzi0Domenico Pomarico1Angelo Paradiso2Samantha Bove3Sergio Diotaiuti4Vittorio Didonna5Francesco Giotta6Daniele La Forgia7Agnese Latorre8Maria Irene Pastena9Pasquale Tamborra10Alfredo Zito11Vito Lorusso12Raffaella Massafra13Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyStruttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyOncologia Medica Sperimentale, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyDipartimento di Matematica, Università degli Studi di Bari, 70121 Bari, ItalyStruttura Semplice Dipartimentale di Chirurgia, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyStruttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyUnità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyStruttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyUnità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyUnità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyStruttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyUnità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyUnità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyStruttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, ItalyIn the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.https://www.mdpi.com/2072-6694/13/2/352sentinel lymph nodeearly breast cancerdecision support systemOSNAclinically negative lymph nodeCancerMath |
| spellingShingle | Annarita Fanizzi Domenico Pomarico Angelo Paradiso Samantha Bove Sergio Diotaiuti Vittorio Didonna Francesco Giotta Daniele La Forgia Agnese Latorre Maria Irene Pastena Pasquale Tamborra Alfredo Zito Vito Lorusso Raffaella Massafra Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study Cancers sentinel lymph node early breast cancer decision support system OSNA clinically negative lymph node CancerMath |
| title | Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study |
| title_full | Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study |
| title_fullStr | Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study |
| title_full_unstemmed | Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study |
| title_short | Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study |
| title_sort | predicting of sentinel lymph node status in breast cancer patients with clinically negative nodes a validation study |
| topic | sentinel lymph node early breast cancer decision support system OSNA clinically negative lymph node CancerMath |
| url | https://www.mdpi.com/2072-6694/13/2/352 |
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