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Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma

  • Ming He
    Affiliations
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China

    Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China

    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuaifuyuan No.1, Dongcheng District, Beijing 100730, China
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  • Xinyue Chen
    Affiliations
    CT Collaboration, Siemens Healthineers, Beijing, China
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  • Michael Wels
    Affiliations
    Siemens Healthineers, Erlangen, Germany
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  • Felix Lades
    Affiliations
    Siemens Healthineers, Erlangen, Germany
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  • Yatong Li
    Affiliations
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuaifuyuan No.1, Dongcheng District, Beijing 100730, China
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  • Zaiyi Liu
    Affiliations
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China

    Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
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  • Zhengyu Jin
    Affiliations
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuaifuyuan No.1, Dongcheng District, Beijing 100730, China
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  • Huadan Xue
    Correspondence
    Address correspondence to: H.X.
    Affiliations
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuaifuyuan No.1, Dongcheng District, Beijing 100730, China
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      Objective

      To develop and validate an effective model for identifying patients with postoperative local disease recurrence of pancreatic ductal adenocarcinoma (PDAC).

      Methods

      A total of 153 patients who had undergone surgical resection of PDAC with regular postoperative follow-up were consecutively enrolled and randomly divided into training (n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up results served as the reference diagnostic criteria. Radiomics analysis of the postoperative soft-tissue was performed on a commercially available prototype software using portal vein phase image. Three models were built to characterize postoperative soft tissue: computed tomography (CT)-based radiomics, clinicoradiological, and their combination. The area under the receiver operating characteristic curves (AUC) was used to evaluate the differential diagnostic performance. A nomogram was used to select the final model with best performance. One radiologist's diagnostic choices that were made with and without the nomogram's assistance were evaluated.

      Results

      A seven-feature–combined radiomics signature was constructed as a predictor of postoperative local recurrence. The nomogram model combining the radiomics signature with postoperative CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI: 0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation cohort, the AUC for differential diagnosis was significantly improved for the combined model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533, p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical usefulness of the proposed nomogram. The diagnostic performance of the radiologist was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670, p = 0.17).

      Conclusion

      The combined model using CT radiomic features and CA 19-9 elevation effectively characterized postoperative soft tissue and potentially may improve treatment strategies and facilitate personalized treatment for PDAC after surgical resection.

      Key Words

      Abbreviations:

      AIC (akaike information criterion), AUC (area under the curve), CA 19-9 (carbohydrate antigen 19-9), CI (confidence interval), C-index (Harrell's concordance-index), CT (computed tomography), DCA (decision-curve analysis), ICC (intraclass correlation coefficient), PDAC (pancreatic ductal adenocarcinoma), ROCs (receiver operating characteristic curves)
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      References

        • Siegel RL
        • Miller KD
        • Jemal A.
        Cancer statistics, 2019.
        CA Cancer J Clin. 2019; 69: 7-34
        • Groot VP
        • Rezaee N
        • Wu W
        • et al.
        Patterns, timing, and predictors of recurrence following pancreatectomy for pancreatic ductal adenocarcinoma.
        Ann Surg. 2018; 267: 936-945
        • Jones RP
        • Psarelli EE
        • Jackson R
        • et al.
        Patterns of recurrence after resection of pancreatic ductal adenocarcinoma: a secondary analysis of the ESPAC-4 randomized adjuvant chemotherapy trial.
        JAMA Surg. 2019; 154: 1038-1048
        • Kayahara M
        • Nagakawa T
        • Ueno K
        • et al.
        An evaluation of radical resection for pancreatic cancer based on the mode of recurrence as determined by autopsy and diagnostic imaging.
        Cancer. 1993; 72: 2118-2123
        • Neoptolemos JP
        • Stocken DD
        • Friess H
        • et al.
        A randomized trial of chemoradiotherapy and chemotherapy after resection of pancreatic cancer.
        N Engl J Med. 2004; 350: 1200-1210
        • Hishinuma S
        • Ogata Y
        • Tomikawa M
        • et al.
        Patterns of recurrence after curative resection of pancreatic cancer, based on autopsy findings.
        J Gastrointest Surg. 2006; 10: 511-518
        • Tempero MA
        • Malafa MP
        • Al-Hawary M
        • et al.
        Pancreatic adenocarcinoma, Version 2.2017, NCCN clinical practice guidelines in oncology.
        J Natl Compr Canc Netw. 2017; 15: 1028-1061
        • Strobel O
        • Hartwig W
        • Hackert T
        • et al.
        Re-resection for isolated local recurrence of pancreatic cancer is feasible, safe, and associated with encouraging survival.
        Ann Surg Oncol. 2013; 20: 964-972
        • Ruarus AH
        • Vroomen L
        • Geboers B
        • et al.
        Percutaneous irreversible electroporation in locally advanced and recurrent pancreatic cancer (PANFIRE-2): a multicenter, prospective, single-arm, phase II study.
        Radiology. 2020; 294: 212-220
        • Groot VP
        • van Santvoort HC
        • Rombouts SJ
        • et al.
        Systematic review on the treatment of isolated local recurrence of pancreatic cancer after surgery; re-resection, chemoradiotherapy and SBRT.
        HPB. 2017; 19: 83-92
        • Javadi S
        • Karbasian N
        • Bhosale P
        • et al.
        Imaging findings of recurrent pancreatic cancer following resection.
        Abdom Radiol. 2018; 43: 489-496
        • Bluemke DA
        • Abrams RA
        • Yeo CJ
        • et al.
        Recurrent pancreatic adenocarcinoma: spiral CT evaluation following the Whipple procedure.
        Radiographics. 1997; 17: 303-313
        • Kovac JD
        • Mayer P
        • Hackert T
        • et al.
        The time to and type of pancreatic cancer recurrence after surgical resection: is prediction possible?.
        Acad Radiol. 2019; 26: 775-781
        • Groot VP
        • Daamen LA
        • Hagendoorn J
        • et al.
        Current strategies for detection and treatment of recurrence of pancreatic ductal adenocarcinoma after resection: a nationwide survey.
        Pancreas. 2017; 46 (-e5): e73
        • Daamen LA
        • Groot VP
        • Goense L
        • et al.
        The diagnostic performance of CT versus FDG PET-CT for the detection of recurrent pancreatic cancer: a systematic review and meta-analysis.
        Eur J Radiol. 2018; 106: 128-136
        • Daamen LA
        • Groot VP
        • Heerkens HD
        • et al.
        Systematic review on the role of serum tumor markers in the detection of recurrent pancreatic cancer.
        HPB. 2018; 20: 297-304
        • Sperti C
        • Pasquali C
        • Bissoli S
        • et al.
        Tumor relapse after pancreatic cancer resection is detected earlier by 18-FDG PET than by CT.
        J Gastrointest Surg. 2010; 14: 131-140
        • Ruf J
        • Lopez Hänninen E
        • Oettle H
        • et al.
        Detection of recurrent pancreatic cancer: comparison of FDG-PET with CT/MRI.
        Pancreatology. 2005; 5: 266-272
        • Parakh A
        • Patino M
        • Muenzel D
        • et al.
        Role of rapid kV-switching dual-energy CT in assessment of post-surgical local recurrence of pancreatic adenocarcinoma.
        Abd Radiol (New York). 2018; 43: 497-504
        • Sandrasegaran K
        • Lin Y
        • Asare-Sawiri M
        • et al.
        CT texture analysis of pancreatic cancer.
        Eur Radiol. 2019; 29: 1067-1073
        • Xie T
        • Wang X
        • Li M
        • et al.
        Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection.
        Eur Radiol. 2020; 30: 2513-2524
        • Tempero MA
        • Malafa MP
        • Al-Hawary M
        • et al.
        Pancreatic adenocarcinoma, Version 2.2021, NCCN clinical practice guidelines in oncology.
        J Natl Compr Canc Netw. 2021; 19: 439-457
        • Sun R
        • Limkin EJ
        • Vakalopoulou M
        • et al.
        A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study.
        Lancet Oncol. 2018; 19: 1180-1191
        • Koçak B
        • Durmaz E
        • Ateş E
        • Kılıçkesmez Ö.
        Radiomics with artificial intelligence: a practical guide for beginners.
        Diagn Interv Radiol (Ankara, Turkey). 2019; 25: 485-495
        • Kim BR
        • Kim JH
        • Ahn SJ
        • et al.
        CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis.
        Eur Radiol. 2019; 29: 362-372
        • Yamashita R
        • Perrin T
        • Chakraborty J
        • et al.
        Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation.
        Eur Radiol. 2020; 30: 195-205
        • Heye T
        • Zausig N
        • Klauss M
        • et al.
        CT diagnosis of recurrence after pancreatic cancer: is there a pattern?.
        World J Gastroenterol. 2011; 17: 1126-1134
        • Lubner MG
        • Smith AD
        • Sandrasegaran K
        • Sahani DV
        • Pickhardt PJ.
        CT texture analysis: definitions, applications, biologic correlates, and challenges.
        Radiographics. 2017; 37: 1483-1503
        • De Robertis R
        • Maris B
        • Cardobi N
        • et al.
        Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?.
        Eur Radiol. 2018; 28: 2582-2591
        • Li J
        • Liu F
        • Fang X
        • et al.
        CT radiomics features in differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma: a propensity score analysis.
        Acad Radiol. 2022; 29: 358-366
        • Liu C
        • Bian Y
        • Meng Y
        • et al.
        Preoperative prediction of G1 and G2/3 grades in patients with nonfunctional pancreatic neuroendocrine tumors using multimodality imaging.
        Acad Radiol. 2021; 29: e49-e60
        • Ducreux M
        • Cuhna AS
        • Caramella C
        • et al.
        Cancer of the pancreas: ESMO clinical practice guidelines for diagnosis, treatment and follow-up.
        Ann Oncol. 2015; 26 (Suppl): v56-v68
        • Yamaguchi K
        • Okusaka T
        • Shimizu K
        • et al.
        Clinical practice guidelines for pancreatic cancer 2016 from the Japan Pancreas Society: a synopsis.
        Pancreas. 2017; 46: 595-604
        • O'Reilly D
        • Fou L
        • Hasler E
        • et al.
        Diagnosis and management of pancreatic cancer in adults: a summary of guidelines from the UK National Institute for Health and Care Excellence.
        Pancreatology. 2018; 18: 962-970
        • Daamen LA
        • Groot VP
        • Intven MPW
        • et al.
        Postoperative surveillance of pancreatic cancer patients.
        Eur J Surg Oncol. 2019; 45: 1770-1777