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01-02-2022 | Computed Tomography | Original Article

A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy

Authors: Giovanni Capretti, Cristiana Bonifacio, Crescenzo De Palma, Martina Nebbia, Caterina Giannitto, Pierandrea Cancian, Maria Elena Laino, Luca Balzarini, Nickolas Papanikolaou, Victor Savevski, Alessandro Zerbi

Published in: Updates in Surgery | Issue 1/2022

Abstract

Clinically relevant postoperative pancreatic fistula (CR-POPF) is a life-threatening complication following pancreaticoduodenectomy (PD). Individualized preoperative risk assessment could improve clinical management and prevent or mitigate adverse outcomes. The aim of this study is to develop a machine learning risk model to predict occurrence of CR-POPF after PD from preoperative computed tomography (CT) scans. A total of 100 preoperative high-quality CT scans of consecutive patients who underwent pancreaticoduodenectomy in our institution between 2011 and 2019 were analyzed. Radiomic and morphological features extracted from CT scans related to pancreatic anatomy and patient characteristics were included as variables. These data were then assessed by a machine learning classifier to assess the risk of developing CR-POPF. Among the 100 patients evaluated, 20 had CR-POPF. The predictive model based on logistic regression demonstrated specificity of 0.824 (0.133) and sensitivity of 0.571 (0.337), with an AUC of 0.807 (0.155), PPV of 0.468 (0.310) and NPV of 0.890 (0.084). The performance of the model minimally decreased utilizing a random forest approach, with specificity of 0.914 (0.106), sensitivity of 0.424 (0.346), AUC of 0.749 (0.209), PPV of 0.502 (0.414) and NPV of 0.869 (0.076). Interestingly, using the same data, the model was also able to predict postoperative overall complications and a postoperative length of stay over the median with AUCs of 0.690 (0.209) and 0.709 (0.160), respectively. These findings suggest that preoperative CT scans evaluated by machine learning may provide a novel set of information to help clinicians choose a tailored therapeutic pathway in patients candidated to pancreatoduodenectomy.

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Pratt WB, Maithel SK, Vanounou T, Huang ZS, Callery MP, Vollmer CM (2007) Clinical and economic validation of the international study group of pancreatic fistula (ISGPF) classification scheme. Ann Surg. https://doi.org/10.1097/01.sla.0000251708.70219.d2CrossRefPubMedPubMedCentral

Vollmer CM, Sanchez N, Gondek S et al (2012) A root-cause analysis of mortality following major pancreatectomy. J Gastrointest Surg. https://doi.org/10.1007/s11605-011-1753-xCrossRefPubMed

Ahmad SA, Edwards MJ, Sutton JM et al (2012) Factors influencing readmission after pancreaticoduodenectomy: a multi-institutional study of 1302 patients. Ann Surg. https://doi.org/10.1097/SLA.0b013e318265ef0bCrossRefPubMed

Williamsson C, Ansari D, Andersson R, Tingstedt B (2017) Postoperative pancreatic fistula-impact on outcome, hospital cost and effects of centralization. HPB. https://doi.org/10.1016/j.hpb.2017.01.004CrossRefPubMed

Fuks D, Piessen G, Huet E et al (2009) Life-threatening postoperative pancreatic fistula (grade C) after pancreaticoduodenectomy: incidence, prognosis, and risk factors. Am J Surg 197(6):702–709. https://doi.org/10.1016/j.amjsurg.2008.03.004CrossRefPubMed

Callery MP, Pratt WB, Kent TS, Chaikof EL, Vollmer CM (2013) A prospectively validated clinical risk score accurately predicts pancreatic fistula after pancreatoduodenectomy. J Am Coll Surg 216(1):1–14. https://doi.org/10.1016/j.jamcollsurg.2012.09.002CrossRefPubMed

Mungroop TH, Van Rijssen LB, Van Klaveren D et al (2019) Alternative fistula risk score for pancreatoduodenectomy (a-FRS): design and international external validation. Ann Surg 269(5):937–943. https://doi.org/10.1097/SLA.0000000000002620CrossRefPubMed

Chen JY, Feng J, Wang XQ, Cai SW, Dong JH, Chen YL (2015) Risk scoring system and predictor for clinically relevant pancreatic fistula after pancreaticoduodenectomy. World J Gastroenterol 21(19):5926–5933. https://doi.org/10.3748/wjg.v21.i19.5926CrossRefPubMedPubMedCentral

Kim JY, Park JS, Kim JK, Yoon DS (2013) A model for predicting pancreatic leakage after pancreaticoduodenectomy based on the international study group of pancreatic surgery classification. Korean J Hepato Biliary Pancreatic Surg 17(4):166. https://doi.org/10.14701/kjhbps.2013.17.4.166CrossRef

10.

Roberts KJ, Hodson J, Mehrzad H et al (2014) A preoperative predictive score of pancreatic fistula following pancreatoduodenectomy. HPB 16(7):620–628. https://doi.org/10.1111/hpb.12186CrossRefPubMed

11.

Yamamoto Y, Sakamoto Y, Nara S, Esaki M, Shimada K, Kosuge T (2011) A preoperative predictive scoring system for postoperative pancreatic fistula after pancreaticoduodenectomy. World J Surg 35(12):2747–2755. https://doi.org/10.1007/s00268-011-1253-xCrossRefPubMed

12.

Pratt WB, Callery MP, Vollmer CM (2008) Risk prediction for development of pancreatic fistula using the ISGPF classification scheme. World J Surg 32(3):419–428. https://doi.org/10.1007/s00268-007-9388-5CrossRefPubMed

13.

Sandini M, Bernasconi DP, Fior D et al (2016) A high visceral adipose tissue-to-skeletal muscle ratio as a determinant of major complications after pancreatoduodenectomy for cancer. Nutrition 32(11–12):1231–1237. https://doi.org/10.1016/j.nut.2016.04.002CrossRefPubMed

14.

Pecorelli N, Carrara G, De Cobelli F et al (2016) Effect of sarcopenia and visceral obesity on mortality and pancreatic fistula following pancreatic cancer surgery. Br J Surg. https://doi.org/10.1002/bjs.10063CrossRefPubMed

15.

Wellner UF, Kayser G, Lapshyn H et al (2010) A simple scoring system based on clinical factors related to pancreatic texture predicts postoperative pancreatic fistula preoperatively. HPB 12(10):696–702. https://doi.org/10.1111/j.1477-2574.2010.00239.xCrossRefPubMedPubMedCentral

16.

Sandini M, Bernasconi DP, Ippolito D et al (2015) Preoperative computed tomography to predict and stratify the risk of severe pancreatic fistula after pancreatoduodenectomy. Med (US) 94(31):1–7. https://doi.org/10.1097/MD.0000000000001152CrossRef

17.

Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762. https://doi.org/10.1038/nrclinonc.2017.141CrossRefPubMed

18.

Tempero MA, Malafa MP, Chiorean EG et al (2019) Pancreatic adenocarcinoma, version 1.2019 featured updates to the NCCN guidelines. JNCCN J Natl Compr Cancer Netw. 17(3):203–210. https://doi.org/10.6004/jnccn.2019.0014CrossRef

19.

Dindo D, Demartines N, Clavien P (2004) Classification of Surgical Complications. Ann Surg 240(2):205–213. https://doi.org/10.1097/01.sla.0000133083.54934.aeCrossRefPubMedPubMedCentral

20.

Bassi C, Marchegiani G, Dervenis C et al (2017) The 2016 update of the International Study Group (ISGPS) definition and grading of postoperative pancreatic fistula: 11 Years After. Surg (US) 161(3):584–591. https://doi.org/10.1016/j.surg.2016.11.014CrossRef

21.

Pecorelli N, Carrara G, De Cobelli F et al (2016) Effect of sarcopenia and visceral obesity on mortality and pancreatic fistula following pancreatic cancer surgery. Br J Surg 103(4):434–442. https://doi.org/10.1002/bjs.10063CrossRefPubMed

22.

Haralick RM, Dinstein I, Shanmugam K (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621. https://doi.org/10.1109/TSMC.1973.4309314CrossRef

23.

Xu DH, Kurani AS, Furst JD, Raicu DS (2004) Run-length encoding for volumetric texture. Image Process Proc Fourth IASTED Int Conf Vis Imaging 27:534–539

24.

Amadasun M, King R (1989) Texural features corresponding to texural properties. IEEE Trans Syst Man Cybern. https://doi.org/10.1109/21.44046CrossRef

25.

Thibault G, Fertil B, Navarro C et al (2014) Texture indexes and gray level size zone matrix application to cell nuclei classification. Pattern Recognit Inf Process 2009:140–145

26.

Mitsiopoulos N, Baumgartner RN, Heymsfield SB, Lyons W, Gallagher D, Ross R (1998) Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol 85(1):115–122. https://doi.org/10.1152/jappl.1998.85.1.115CrossRefPubMed

27.

Van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res. https://doi.org/10.1158/0008-5472.CAN-17-0339CrossRefPubMedPubMedCentral

28.

Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305

29.

Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B. https://doi.org/10.1111/j.2517-6161.1996.tb02080.xCrossRef

30.

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. https://doi.org/10.1613/jair.953CrossRef

31.

Shen W, Punyanitya M, Wang ZM et al (2004) Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol. https://doi.org/10.1152/japplphysiol.00744.2004CrossRefPubMed

32.

Mourtzakis M, Prado CMM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE (2008) A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. https://doi.org/10.1139/H08-075CrossRefPubMed

33.

Bihorac A, Ozrazgat-Baslanti T, Ebadi A et al (2019) MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg 269(4):652–662. https://doi.org/10.1097/SLA.0000000000002706CrossRefPubMed

34.

Merath K, Hyer JM, Mehta R et al (2020) Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J Gastrointest Surg. https://doi.org/10.1007/s11605-019-04338-2CrossRefPubMed

35.

Kambakamba P, Mannil M, Herrera PE et al (2020) The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: a proof-of-principle study. Surg (US) 167(2):448–454. https://doi.org/10.1016/j.surg.2019.09.019CrossRef

36.

Han IW, Cho K, Ryu Y et al (2020) Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence. World J Gastroenterol. https://doi.org/10.3748/WJG.V26.I30.4453CrossRefPubMedPubMedCentral

Title: A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy
Authors: Giovanni Capretti
Cristiana Bonifacio
Crescenzo De Palma
Martina Nebbia
Caterina Giannitto
Pierandrea Cancian
Maria Elena Laino
Luca Balzarini
Nickolas Papanikolaou
Victor Savevski
Alessandro Zerbi
Publication date: 01-02-2022
Publisher: Springer International Publishing
Keywords: Computed Tomography
Pancreatoduodenostomy
Pancreatoduodenostomy
Computed Tomography
Published in: Updates in Surgery / Issue 1/2022
Print ISSN: 2038-131X
Electronic ISSN: 2038-3312
DOI: https://doi.org/10.1007/s13304-021-01174-5

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A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy

Abstract

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