Background: Liver metastases are a leading cause of cancer-associated deaths in patients affected by colorectal cancer (CRC). The multidisciplinary strategy to treat CRC is more effective when the radiological diagnosis is accurate and early. Despite the evolving technologies in radiological accuracy, the radiological diagnosis of Colorectal Cancer Liver Metastases (CRCLM) is still a key point. The aim of our study was to define a new patient representation different by Artificial Intelligence models, using Formal Methods (FMs), to help clinicians to predict the presence of liver metastasis when still undetectable using the standard protocols. Methods: We retrospectively reviewed from 2013 to 2020 the CT scan of nine patients affected by CRC who would develop liver lesions within 4 months and 8 years. Seven patients developed liver metastases after primary staging before any liver surgery, and two patients were enrolled after R0 liver resection. Twenty-one patients were enrolled as the case control group (CCG). Regions of Interest (ROIs) were identified through manual segmentation on the medical images including only liver parenchyma and eventual benign lesions, avoiding major vessels and biliary ducts. Our predictive model was built based on formally verified radiomic features. Results: The precision of our methods is 100%, scheduling patients as positive only if they will be affected by CRCLM, showing a 93.3% overall accuracy. Recall was 77.8%. Conclusion: FMs can provide an effective early detection of CRCLM before clinical diagnosis only through non-invasive radiomic features even in very heterogeneous and small clinical samples.

Early diagnosis of liver metastases from colorectal cancer through CT radiomics and formal methods: A pilot study / Rocca, A.; Brunese, M. C.; Santone, A.; Avella, P.; Bianco, P.; Scacchi, A.; Scaglione, M.; Bellifemine, F.; Danzi, R.; Varriano, G.; Vallone, G.; Calise, F.; Brunese, L.. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 11:1(2022), p. 31. [10.3390/jcm11010031]

Early diagnosis of liver metastases from colorectal cancer through CT radiomics and formal methods: A pilot study

Scaglione M.;
2022

Abstract

Background: Liver metastases are a leading cause of cancer-associated deaths in patients affected by colorectal cancer (CRC). The multidisciplinary strategy to treat CRC is more effective when the radiological diagnosis is accurate and early. Despite the evolving technologies in radiological accuracy, the radiological diagnosis of Colorectal Cancer Liver Metastases (CRCLM) is still a key point. The aim of our study was to define a new patient representation different by Artificial Intelligence models, using Formal Methods (FMs), to help clinicians to predict the presence of liver metastasis when still undetectable using the standard protocols. Methods: We retrospectively reviewed from 2013 to 2020 the CT scan of nine patients affected by CRC who would develop liver lesions within 4 months and 8 years. Seven patients developed liver metastases after primary staging before any liver surgery, and two patients were enrolled after R0 liver resection. Twenty-one patients were enrolled as the case control group (CCG). Regions of Interest (ROIs) were identified through manual segmentation on the medical images including only liver parenchyma and eventual benign lesions, avoiding major vessels and biliary ducts. Our predictive model was built based on formally verified radiomic features. Results: The precision of our methods is 100%, scheduling patients as positive only if they will be affected by CRCLM, showing a 93.3% overall accuracy. Recall was 77.8%. Conclusion: FMs can provide an effective early detection of CRCLM before clinical diagnosis only through non-invasive radiomic features even in very heterogeneous and small clinical samples.
Early diagnosis of liver metastases from colorectal cancer through CT radiomics and formal methods: A pilot study / Rocca, A.; Brunese, M. C.; Santone, A.; Avella, P.; Bianco, P.; Scacchi, A.; Scaglione, M.; Bellifemine, F.; Danzi, R.; Varriano, G.; Vallone, G.; Calise, F.; Brunese, L.. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 11:1(2022), p. 31. [10.3390/jcm11010031]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11388/256519
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