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Multi-Center Follow-up Study to Develop a Classification System Which Differentiates Mucinous Cystic Neoplasm of the Liver and Benign Hepatic Cyst Using Machine Learning
- Source :
- Academic Radiology. 29:1149-1156
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- Rationale and Objectives To date, no clinically useful classification system has been developed for reliably differentiating mucinous cystic neoplasm (MCN) from a benign hepatic cyst (BHC) in the liver. The objective was to use machine learning and a multi-center study design to develop and assess the performance of a novel classification system for predicting whether a hepatic cystic lesion represents MCN or BHC. Materials and Methods A multi-center cohort study identified 154 surgically resected hepatic cystic lesions in 154 subjects which were pathologic confirmed as MCN (43) or BHC (111). Readers at each institution recorded seven pre-determined imaging features previously identified as potential differentiating features from prior publications. The contribution of each of these features to differentiating MCN from BHC was assessed by machine learning to develop an optimal classification system. Results Although several of the assessed imaging features demonstrated statistical significance, only 3 imaging features were found by machine learning to significantly contribute to a potential classification system: (1) solid enhancing nodule (2) all septations arising from an external macro-lobulation (3) whether the lesion was solitary or one of multiple cystic liver lesions. The optimal classification system had only four categories and correctly identified 144/154 lesion (93.5%). Conclusion This multi-center follow-up study was able to use machine learning to develop a highly accurate classification system for differentiation of hepatic MCN from BHC, which could be readily applied to clinical practice.
- Subjects :
- Cysts
business.industry
Liver Diseases
Follow up studies
Nodule (medicine)
Machine learning
computer.software_genre
Cystic Neoplasm
Cohort Studies
Machine Learning
Pancreatic Neoplasms
Clinical Practice
Lesion
Cystic lesion
Liver
medicine
Humans
Radiology, Nuclear Medicine and imaging
Artificial intelligence
Hepatic Cyst
medicine.symptom
business
Liver cysts
computer
Follow-Up Studies
Subjects
Details
- ISSN :
- 10766332
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- Academic Radiology
- Accession number :
- edsair.doi.dedup.....cdfa4bd02b37dcf3fe0c24888aba7562