Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.
<h4>Background and objective</h4>Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the trea...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0312036 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850268733796253696 |
|---|---|
| author | Annarita Fanizzi Samantha Bove Maria Colomba Comes Erika Francesca Di Benedetto Agnese Latorre Francesco Giotta Annalisa Nardone Alessandro Rizzo Clara Soranno Alfredo Zito Raffaella Massafra |
| author_facet | Annarita Fanizzi Samantha Bove Maria Colomba Comes Erika Francesca Di Benedetto Agnese Latorre Francesco Giotta Annalisa Nardone Alessandro Rizzo Clara Soranno Alfredo Zito Raffaella Massafra |
| author_sort | Annarita Fanizzi |
| collection | DOAJ |
| description | <h4>Background and objective</h4>Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.<h4>Methods</h4>In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Disease Event at both 5-year and 10-year follow-ups of 696 female patients with a first invasive breast cancer diagnosis enrolled at IRCCS "Giovanni Paolo II" in Bari, Italy. After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. Then, we classified breast cancer patients in the two classes, namely, Invasive Disease Event and non-Invasive Disease Event, via a Support Vector Machine classifier trained on a subset of significative features previously identified.<h4>Results</h4>Both 5-year and 10-year models resulted particularly accurate in predicting breast cancer recurrence event, achieving an AUC value of 92.07% and 92.84%, an accuracy of 88.71% and 88.82%, a sensitivity of 86.83% and 88.06%, a specificity of 89.55% and 89.3%, a precision of 71.93% and 84.82%, respectively.<h4>Conclusions</h4>This is the first study proposing an approach which converts clinical information into an image-form to develop a decision support system for identifying patients at high risk of occurrence of an Invasive Disease Event, and then defining personalized oncological therapeutic treatments for breast cancer patients. |
| format | Article |
| id | doaj-art-4abe05f19af547bd8fd864ef5c3d78fe |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-4abe05f19af547bd8fd864ef5c3d78fe2025-08-20T01:53:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011911e031203610.1371/journal.pone.0312036Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.Annarita FanizziSamantha BoveMaria Colomba ComesErika Francesca Di BenedettoAgnese LatorreFrancesco GiottaAnnalisa NardoneAlessandro RizzoClara SorannoAlfredo ZitoRaffaella Massafra<h4>Background and objective</h4>Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.<h4>Methods</h4>In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Disease Event at both 5-year and 10-year follow-ups of 696 female patients with a first invasive breast cancer diagnosis enrolled at IRCCS "Giovanni Paolo II" in Bari, Italy. After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. Then, we classified breast cancer patients in the two classes, namely, Invasive Disease Event and non-Invasive Disease Event, via a Support Vector Machine classifier trained on a subset of significative features previously identified.<h4>Results</h4>Both 5-year and 10-year models resulted particularly accurate in predicting breast cancer recurrence event, achieving an AUC value of 92.07% and 92.84%, an accuracy of 88.71% and 88.82%, a sensitivity of 86.83% and 88.06%, a specificity of 89.55% and 89.3%, a precision of 71.93% and 84.82%, respectively.<h4>Conclusions</h4>This is the first study proposing an approach which converts clinical information into an image-form to develop a decision support system for identifying patients at high risk of occurrence of an Invasive Disease Event, and then defining personalized oncological therapeutic treatments for breast cancer patients.https://doi.org/10.1371/journal.pone.0312036 |
| spellingShingle | Annarita Fanizzi Samantha Bove Maria Colomba Comes Erika Francesca Di Benedetto Agnese Latorre Francesco Giotta Annalisa Nardone Alessandro Rizzo Clara Soranno Alfredo Zito Raffaella Massafra Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form. PLoS ONE |
| title | Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form. |
| title_full | Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form. |
| title_fullStr | Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form. |
| title_full_unstemmed | Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form. |
| title_short | Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form. |
| title_sort | prediction of breast cancer invasive disease events using transfer learning on clinical data as image form |
| url | https://doi.org/10.1371/journal.pone.0312036 |
| work_keys_str_mv | AT annaritafanizzi predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT samanthabove predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT mariacolombacomes predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT erikafrancescadibenedetto predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT agneselatorre predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT francescogiotta predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT annalisanardone predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT alessandrorizzo predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT clarasoranno predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT alfredozito predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform AT raffaellamassafra predictionofbreastcancerinvasivediseaseeventsusingtransferlearningonclinicaldataasimageform |