ArticleLa Radiologia medica2020
CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners.
Article in La Radiologia medica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- Predicting preoperative lymph node metastasis of hilar cholangiocarcinoma based on deep learning radiomics.The ultrasound journal · 2026Article
- Preoperative prediction of lymphatic metastasis in rectal cancer using a fusion model based on multiparameter magnetic resonance imaging: a retrospective validation study.Frontiers in oncology · 2026Article
- Establishment and validation of a CT-based clinical deep learning radiomics nomogram for predicting the response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma.Frontiers in oncology · 2026Article
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- Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation.European radiology · 2024Article
- The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging.Sensors (Basel, Switzerland) · 2024Article
- CT radiomics prediction of CXCL9 expression and survival in ovarian cancer.Journal of ovarian research · 2023Article
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- Current status and future perspectives of radiomics in hepatocellular carcinoma.World journal of gastroenterology · 2023Review
- Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer.Journal of personalized medicine · 2022Article
- The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.Cancers · 2022Review
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- Integration between Novel Imaging Technologies and Modern Radiotherapy Techniques: How the Eye Drove the Chisel.Cancers · 2022Review
- Radiomics in medical imaging: pitfalls and challenges in clinical management.Japanese journal of radiology · 2022Review
- Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade.La Radiologia medica · 2022Article
- A primer on texture analysis in abdominal radiology.Abdominal radiology (New York) · 2022Review
- Structured Reporting in Radiological Settings: Pitfalls and Perspectives.Journal of personalized medicine · 2022Review
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- Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when.Infectious agents and cancer · 2022Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeTo test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC).
methodsWe included primary HCC patient undergone curative therapies, using early recurrence as endpoint. Four datasets were constructed: 109 images from hospital #1 for training (set 1: 1-mm image slice thickness), 47 images from hospital #1 for internal validation (sets 2 and 3: 1-mm and 10-mm image slice thicknesses, respectively), and 47 images from hospital #2 for external validation (set 4: vastly different from training dataset). A radiomics model was constructed. Radiomics technical reproducibility was measured by overfitting and calibration deviation in external validation dataset. The influence of slice thickness on reproducibility was evaluated in two internal validation datasets.
resultsCompared with set 1, the model in set 2 indicated favorable prediction efficiency (the area under the curve 0.79 vs. 0.80, P = 0.47) and good calibration (unreliability statistic U: P = 0.33). However, in set 4, significant overfitting (0.63 vs. 0.80, P < 0.01) and calibration deviation (U: P < 0.01) were observed. Similar poor performance was also observed in set 3 (0.56 vs. 0.80, P = 0.02; U: P < 0.01).
conclusionsCT-based radiomics has poor reproducibility between centers. Image heterogeneity, such as slice thickness, can be a significant influencing factor.
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