The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study

Kambakamba, Patryk; Mannil, Manoj; Herrera, P.; Müller, Philip C.; Kuemmerli, Christoph; Linecker, Michael; Spiczak, Jochen von; Hüllner, Martin; Raptis, Dimitri A.; Petrowsky, Henrik; Clavien, Pierre‐Alain; Alkadhi, Hatem

doi:10.1016/j.surg.2019.09.019

Cited by 48 publications

(49 citation statements)

References 34 publications

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“…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 . However, the present effort is the first to explore a deep-learning based method with significantly larger cohorts from different institutions, which is simpler to use and easier to interpret using one single biomarker (DLS, or the probability).…”

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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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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“…These risk factors can be categorized into 3 groups: The technically demanding group, intraoperative volume status–related group, and poor general condition group (Figure 2 ). The risk factors in the technically demanding group (soft pancreas[ 2 , 5 , 6 , 9 ], small pancreatic duct[ 6 , 9 , 29 ], extrapancreatic lesion[ 6 ], absence of preoperative pancreatitis or low lipase level[ 30 ], absence of preoperative endoscopic biliary decompression, absence of neoadjuvant radiotherapy, and high BMI[ 7 ]) indicate potential difficulty in reconstructing the pancreatic-enteric anastomosis, which could cause POPF. Patients with pancreatic cancer, chronic pancreatitis, or neoadjuvant treatment have increased pancreatic fibrosis and a lower incidence of POPF than other PD patients[ 30 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…Despite advancements in surgical technique and operative management, postoperative pancreatic fistula (POPF) is still widely considered to be the greatest contributor to major morbidity and mortality after pancreatoduodenectomy (PD), with an incidence of 10%-30%[ 1 - 5 ]. Furthermore, it frequently delays the timely delivery of adjuvant therapies, and reduces overall patient survival[ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…In ML, computational models composed of multiple processing layers learn various data representations with multiple levels of abstraction[ 13 ]. ML is currently being used in not only surgery[ 5 , 14 , 15 ] but also other areas, such as, pharmacogenomics, image classification, and medical decision support systems[ 16 - 20 ]. Therefore, for this study we aimed to develop a new risk prediction platform for POPF after PD using ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence

Han¹,

Cho²,

Ryu³

et al. 2020

WJG

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BACKGROUND Despite advancements in operative technique and improvements in postoperative managements, postoperative pancreatic fistula (POPF) is a life-threatening complication following pancreatoduodenectomy (PD). There are some reports to predict POPF preoperatively or intraoperatively, but the accuracy of those is questionable. Artificial intelligence (AI) technology is being actively used in the medical field, but few studies have reported applying it to outcomes after PD. AIM To develop a risk prediction platform for POPF using an AI model. METHODS Medical records were reviewed from 1769 patients at Samsung Medical Center who underwent PD from 2007 to 2016. A total of 38 variables were inserted into AI-driven algorithms. The algorithms tested to make the risk prediction platform were random forest (RF) and a neural network (NN) with or without recursive feature elimination (RFE). The median imputation method was used for missing values. The area under the curve (AUC) was calculated to examine the discriminative power of algorithm for POPF prediction. RESULTS The number of POPFs was 221 (12.5%) according to the International Study Group of Pancreatic Fistula definition 2016. After median imputation, AUCs using 38 variables were 0.68 ± 0.02 with RF and 0.71 ± 0.02 with NN. The maximal AUC using NN with RFE was 0.74. Sixteen risk factors for POPF were identified by AI algorithm: Pancreatic duct diameter, body mass index, preoperative serum albumin, lipase level, amount of intraoperative fluid infusion, age, platelet count, extrapancreatic location of tumor, combined venous resection, co-existing pancreatitis, neoadjuvant radiotherapy, American Society of Anesthesiologists’ score, sex, soft texture of the pancreas, underlying heart disease, and preoperative endoscopic biliary decompression. We developed a web-based POPF prediction platform, and this application is freely available at . CONCLUSION This study is the first to predict POPF with multiple risk factors using AI. This platform is reliable (AUC 0.74), so it could be used to select patients who need especially intense therapy and to preoperatively establish an effective treatment strategy.

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“…The emerging field of radiomics describes the automated extraction of a wealth of information of digital images that goes beyond their manual assessment by expert radiologists. 58 In a recent 60 We believe that these tools will help us to more precisely select the right patients for LT and better treat recurrences in the future.…”

Section: Macrovascular Invasion Of Hepatic or Portal Veins Is Presentmentioning

confidence: 99%

Recent developments and ongoing trials in transplant oncology

Moeckli

Ivanics

Claasen

et al. 2020

Liver International

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More than half a century has passed since the first successful liver transplantation (LT) by Thomas Starzl and this groundbreaking therapy has now become an established treatment for various indications. 1 This is reflected by more than 8000 LT performed in 2018 in the US alone and a 5-year post-transplant survival exceeding 75%. 2 Most transplants are still performed for end-stage liver failure. However, the proportion of LT performed for oncological indications is constantly rising and cancer now represents the main indication for >20% of all transplants worldwide, giving rise to a new field coined "transplant oncology". 2,3 This trend is attributable to great improvements in the perioperative care, the ability to decrease immunosuppression without an increase in rejection and several surgical innovations that helped to increase the donor pool. 4 It will likely continue with less patients requiring LT for viral

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The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study

Cited by 48 publications

References 34 publications

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

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

Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence

Recent developments and ongoing trials in transplant oncology

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