2018
DOI: 10.2147/cmar.s185865
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A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy

Abstract: ObjectiveThe objective of the study was to develop and validate a radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula (POPF) in patients undergoing pancreaticoduodenectomy (PD).Materials and methodsA total of 117 consecutive patients who underwent PD were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography of the above patients. The least absolute shrinkage and selection operator logistic regression was use… Show more

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Cited by 27 publications
(16 citation statements)
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“…Furthermore, they did not assess the performance of their predictive model (ie, no AUC or accuracy measures), so we were unable to directly compare our model with theirs in terms of performance. One recent study has claimed that CT radiomics produced AUC values 0.82 and 0.76 in 80 training and in 37 test subjects, respectively [41] . Our CT-FRS has shown similar predictive capability, albeit with much stronger statistical power given the large sample size.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, they did not assess the performance of their predictive model (ie, no AUC or accuracy measures), so we were unable to directly compare our model with theirs in terms of performance. One recent study has claimed that CT radiomics produced AUC values 0.82 and 0.76 in 80 training and in 37 test subjects, respectively [41] . Our CT-FRS has shown similar predictive capability, albeit with much stronger statistical power given the large sample size.…”
Section: Discussionmentioning
confidence: 99%
“…However, none of the above showed any superiority to DLS or any incremental benefit in combination with DLS, perhaps reflecting their innate correlations with DLS. One recent study 42 has claimed good results in 80 training (AUC = 0.82) and 37 test (AUC = 0.76) subjects by harnessing conventional radiomics analysis. Similarly, Kambakamba et al utilized machine learning-based texture analysis, which could achieve an impressive AUC of 0.95 in predicting POPF on 110 patients from a single institution 43 .…”
Section: Discussionmentioning
confidence: 99%
“…Second, we used one-way analysis of variance (ANOVA) to select features that were significantly different between LNM-positive group and LNM-negative group. Finally, to avoid the “curse of dimensionality”, which would lead to a large false positive result ( 23 ), the least absolute shrinkage and selection operator (LASSO) feature selection algorithm was applied to screen the most informative image features extracted from all VOIs, with penalty parameter tuning conducted by five-fold cross-validation. During the feature selection process, most of the covariate coefficients were reduced to zero, and the variables that still had a non-zero coefficient after the shrinking process were selected.…”
Section: Methodsmentioning
confidence: 99%