Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT)
Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiom...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/7/1816 |
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| author | Matteo Renzulli Margherita Mottola Francesca Coppola Maria Adriana Cocozza Silvia Malavasi Arrigo Cattabriga Giulio Vara Matteo Ravaioli Matteo Cescon Francesco Vasuri Rita Golfieri Alessandro Bevilacqua |
| author_facet | Matteo Renzulli Margherita Mottola Francesca Coppola Maria Adriana Cocozza Silvia Malavasi Arrigo Cattabriga Giulio Vara Matteo Ravaioli Matteo Cescon Francesco Vasuri Rita Golfieri Alessandro Bevilacqua |
| author_sort | Matteo Renzulli |
| collection | DOAJ |
| description | Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), <i>p</i>∼10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></semantics></math></inline-formula>), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status. |
| first_indexed | 2024-03-09T12:01:40Z |
| format | Article |
| id | doaj.art-673cd8133ac449a682bdf0f0d22edd29 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T12:01:40Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-673cd8133ac449a682bdf0f0d22edd292023-11-30T23:02:27ZengMDPI AGCancers2072-66942022-04-01147181610.3390/cancers14071816Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT)Matteo Renzulli0Margherita Mottola1Francesca Coppola2Maria Adriana Cocozza3Silvia Malavasi4Arrigo Cattabriga5Giulio Vara6Matteo Ravaioli7Matteo Cescon8Francesco Vasuri9Rita Golfieri10Alessandro Bevilacqua11Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, ItalyDepartment of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, ItalyDepartment of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, ItalyDepartment of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, ItalyAdvanced Research Center on Electronic Systems (ARCES), University of Bologna, 40126 Bologna, ItalyDepartment of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, ItalyDepartment of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, ItalyGeneral Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, ItalyGeneral Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant’Orsola-Malpighi Hospital, 40138 Bologna, ItalyPathology Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, ItalyDepartment of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, ItalyAdvanced Research Center on Electronic Systems (ARCES), University of Bologna, 40126 Bologna, ItalyBackground: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), <i>p</i>∼10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></semantics></math></inline-formula>), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.https://www.mdpi.com/2072-6694/14/7/1816machine learningcomputed tomographyhepatocellular carcinomaimaging biomarkersradiomics |
| spellingShingle | Matteo Renzulli Margherita Mottola Francesca Coppola Maria Adriana Cocozza Silvia Malavasi Arrigo Cattabriga Giulio Vara Matteo Ravaioli Matteo Cescon Francesco Vasuri Rita Golfieri Alessandro Bevilacqua Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT) Cancers machine learning computed tomography hepatocellular carcinoma imaging biomarkers radiomics |
| title | Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT) |
| title_full | Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT) |
| title_fullStr | Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT) |
| title_full_unstemmed | Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT) |
| title_short | Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT) |
| title_sort | automatically extracted machine learning features from preoperative ct to early predict microvascular invasion in hcc the role of the zone of transition zot |
| topic | machine learning computed tomography hepatocellular carcinoma imaging biomarkers radiomics |
| url | https://www.mdpi.com/2072-6694/14/7/1816 |
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