A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy

Zhang, Wenyu; Cai, Wei; He, Baochun; Xiang, Nan; Fang, Chihua; Jia, Fucang

doi:10.2147/cmar.s185865

Cited by 27 publications

(16 citation statements)

References 31 publications

(37 reference 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%

Computed tomography-adjusted fistula risk score for predicting clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy: Training and external validation of model upgrade

Shi

Gao

et al. 2020

eBioMedicine

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Background: To develop a modified Fistula Risk Score (FRS) for predicting clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreatoduodenectomy (PD) based on both FRS and contrast-enhanced computed tomography (CE-CT). Methods: In this multicenter retrospective analysis, we focused on 990 consecutive patients with pancreatoduodenectomy performed at four institutions between 2009 and 2019. The enhanced CT-FRS model initially targeted 26 pre-and intraoperative factors, including CT descriptors, FRS elements and clinical factors, using LASSO-penalized multivariable logistic regression for predicting CR-POPF events in discovery (n = 718) and externally validated (n = 272) datasets. Probabilities generated were further correlated with histologic features of pancreatic stumps in 356 patients. C-indices were analyzed to compare the predictive potential between the original FRS and the CT-FRS. Findings: CR-POPF developed in 112 (15.6%) and 36 (13.2%) patients in discovery and validation datasets, respectively. The final CT-FRS construct, incorporating remnant pancreatic volume (RPV), stump area, fat and atrophy scores by CT, and main pancreatic duct size, offered significantly greater overall predictability than the original FRS in discovery (C-index: 0.825 vs 0.794; p = 0.04) and validation (0.807 vs 0.741; p = 0.05) cohorts. Importantly, it outperformed the FRS in patients at moderate risk levels (FRS: 3À6), showing remarkably improved C-indices (discovery: 0.729 vs 0.626 [p<0.001], validation: 0.722 vs 0.573 [p = 0.006]). CT-FRS probabilities increased in conjunction with less extensive pancreatic fibrosis (p<0.001), ample glandular acini (p<0.001), and advanced lipomatosis (p<0.001).

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Section: Discussionmentioning

confidence: 99%

Computed tomography-adjusted fistula risk score for predicting clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy: Training and external validation of model upgrade

Shi

Gao

et al. 2020

eBioMedicine

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“…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%

Prediction of clinically relevant Pancreatico-enteric Anastomotic Fistulas after Pancreatoduodenectomy using deep learning of Preoperative Computed Tomography

Mu¹,

Chang²,

Gao³

et al. 2020

Theranostics

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Rationale: Clinically relevant postoperative pancreatic fistula (CR-POPF) is among the most formidable complications after pancreatoduodenectomy (PD), heightening morbidity/mortality rates. Fistula Risk Score (FRS) is a well-developed predictor, but it is an intraoperative predictor and quantifies >50% patients as intermediate risk. Therefore, an accurate and easy-to-use preoperative index is desired. Herein, we test the hypothesis that quantitative analysis of contrast-enhanced computed tomography (CE-CT) with deep learning could predict CR-POPFs. Methods: A group of 513 patients underwent pancreatico-enteric anastomosis after PD at three institutions between 2006 and 2019 was retrospectively collected, and formed a training (70%) and a validation dataset (30%) randomly. A convolutional neural network was trained and generated a deep-learning score (DLS) to identify the patients with higher risk of CR-POPF preoperatively using CE-CT images, which was further externally tested in a prospective cohort collected from August 2018 to June 2019 at the fourth institution. The biological underpinnings of DLS were assessed using histomorphological data by multivariate linear regression analysis. Results: CR-POPFs developed in 95 patients (16.3%) in total. Compared to FRS, the DLS offered significantly greater predictability in training (AUC:0.85 [95% CI, 0.80-0.90] vs. 0.78 [95% CI, 0.72-0.84]; P = 0.03), validation (0.81 [95% CI, 0.72-0.89] vs. 0.76 [95% CI, 0.66-0.84], P = 0.05) and test (0.89 [95% CI, 0.79-0.96] vs. 0.73 [95% CI, 0.61-0.83], P < 0.001) cohorts. Especially in the challenging patients of intermediate risk (FRS: 3-6), the DLS showed significantly higher accuracy (training: 79.9% vs. 61.5% [ P = 0.005]; validation: 70.3% vs. 56.3% [ P = 0.04]; test: 92.1% vs. 65.8% [ P = 0.013]). Additionally, DLS was independently associated with pancreatic fibrosis (coefficients: -0.167), main pancreatic duct (coefficients: -0.445) and remnant volume (coefficients: 0.138) in multivariate linear regression analysis (r 2 = 0.512, P < 0.001). The user satisfaction score in the test cohort was 4 out of 5. Conclusions: Preoperative CT based deep-learning model provides a promising novel method for predicting CR-POPF occurrences after PD, especially at intermediate FRS risk level. This has a potential to be integrated into radiologic reporting system or incorporated into surgical planning software to accommodate the preferences of surgeons to optimize preoperative strategies, intraoperative decision-making, and even postoperative care.

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“…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%

A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

Liu

et al. 2021

Front. Oncol.

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PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.

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A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy

Cited by 27 publications

References 31 publications

Computed tomography-adjusted fistula risk score for predicting clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy: Training and external validation of model upgrade

Computed tomography-adjusted fistula risk score for predicting clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy: Training and external validation of model upgrade

Prediction of clinically relevant Pancreatico-enteric Anastomotic Fistulas after Pancreatoduodenectomy using deep learning of Preoperative Computed Tomography

A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

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