Abstract:
OBJECTIVES:To investigate the prediction of 1-year survival (1-YS) in patients with metastatic colorectal cancer with use of a systematic comparative analysis of quantitative imaging biomarkers (QIBs) based on the geometric and radiomics analysis of whole liver tumor burden (WLTB) in comparison to predictions based on the tumor burden score (TBS), WLTB volume alone, and a clinical model. METHODS:A total of 103 patients (mean age: 61.0 ± 11.2 years) with colorectal liver metastases were analyzed in this retrospective study. Automatic segmentations of WLTB from baseline contrast-enhanced CT images were used. Established biomarkers as well as a standard radiomics model building were used to derive 3 prognostic models. The benefits of a geometric metastatic spread (GMS) model, the Aerts radiomics prior model of the WLTB, and the performance of TBS and WLTB volume alone were assessed. All models were analyzed in both statistical and predictive machine learning settings in terms of AUC. RESULTS:TBS showed the best discriminative performance in a statistical setting to discriminate 1-YS (AUC = 0.70, CI: [0.56, 0.90]). For the machine learning-based prediction for unseen patients, both a model of the GMS of WLTB (0.73, CI: [0.60, 0.84]) and the Aerts radiomics prior model (0.76, CI: [0.65, 0.86]) applied on the WLTB showed a numerically higher predictive performance than TBS (0.68, CI: [0.54, 0.79]), radiomics (0.65, CI: [0.55, 0.78]), WLTB volume alone (0.53, CI: [0.40. 0.66]), or the clinical model (0.56, CI: [0.43, 0.67]). CONCLUSIONS:The imaging-based GMS model may be a first step towards a more fine-grained machine learning extension of the TBS concept for risk stratification in mCRC patients without the vulnerability to technical variance of radiomics. KEY POINTS:• CT-based geometric distribution and radiomics analysis of whole liver tumor burden in metastatic colorectal cancer patients yield prognostic information. • Differences in survival are possibly attributable to the spatial distribution of metastatic lesions and the geometric metastatic spread analysis of all liver metastases may serve as robust imaging biomarker invariant to technical variation. • Imaging-based prediction models outperform clinical models for 1-year survival prediction in metastatic colorectal cancer patients with liver metastases.
journal_name
Eur Radioljournal_title
European radiologyauthors
Mühlberg A,Holch JW,Heinemann V,Huber T,Moltz J,Maurus S,Jäger N,Liu L,Froelich MF,Katzmann A,Gresser E,Taubmann O,Sühling M,Nörenberg Ddoi
10.1007/s00330-020-07192-ysubject
Has Abstractpub_date
2021-02-01 00:00:00pages
834-846issue
2eissn
0938-7994issn
1432-1084pii
10.1007/s00330-020-07192-yjournal_volume
31pub_type
杂志文章abstract::The purpose of the study was to evaluate a semiautomatic analysis tool for assessing global left ventricular myocardial function with multislice computed tomography (MSCT). We examined 33 patients with MSCT using 16x0.5 mm detector collimation and magnetic resonance imaging (MRI) on a 1.5-T scanner. MSCT data were ana...
journal_title:European radiology
pub_type: 临床试验,杂志文章
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abstract::A recent development in biomedical imaging is the non-invasive mapping of molecular events in intact tissues using fluorescence. Underpinning to this development is the discovery of bio-compatible, specific fluorescent probes and proteins and the development of highly sensitive imaging technologies for in vivo fluores...
journal_title:European radiology
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journal_title:European radiology
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