Top

Published in:

Open Access 27-12-2023 | Computed Tomography | Pancreatic Tumors

Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients

Authors: Kai Wang, PhD, John D. Karalis, MD, Ahmed Elamir, MD, Alessandro Bifolco, MD, Megan Wachsmann, MD, Giovanni Capretti, MD, Paola Spaggiari, MD, Sebastian Enrico, BSA, Kishore Balasubramanian, MS, Nafeesah Fatimah, MD, Giada Pontecorvi, PhD, Martina Nebbia, MD, Adam Yopp, MD, Ravi Kaza, MD, Ivan Pedrosa, MD, PhD, Herbert Zeh III, MD, Patricio Polanco, MD, Alessandro Zerbi, MD, Jing Wang, PhD, Todd Aguilera, MD, PhD, Matteo Ligorio, MD, PhD

Published in: Annals of Surgical Oncology | Issue 4/2024

Abstract

Background

Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions.

Methods

We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS.

Results

The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69).

Conclusions

Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.

Available only for authorised users

Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.CrossRefPubMed

Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913–21. https://doi.org/10.1158/0008-5472.CAN-14-0155.CrossRefPubMed

Demir IE, Jäger C, Schlitter AM, et al. R0 versus R1 resection matters after pancreaticoduodenectomy, and less after distal or total pancreatectomy for pancreatic cancer. Ann Surg. 2018;268(6):1058–68.CrossRefPubMed

Kaltenmeier C, Nassour I, Hoehn RS, et al. Impact of Resection margin status in patients with pancreatic cancer: a national cohort study. J Gastrointest Surg. 2021;25(9):2307–16. https://doi.org/10.1007/s11605-020-04870-6.CrossRefPubMed

Ferrone CR, Marchegiani G, Hong TS, et al. Radiological and surgical implications of neoadjuvant treatment with FOLFIRINOX for locally advanced and borderline resectable pancreatic cancer. Ann Surg. 2015;261(1):12–7. https://doi.org/10.1097/sla.0000000000000867.CrossRefPubMed

Perri G, Prakash L, Wang H, et al. Radiographic and serologic predictors of pathologic major response to preoperative therapy for pancreatic cancer. Ann Surg. 2021;273(4):806–13.CrossRefPubMed

Khristenko E, Shrainer I, Setdikova G, Palkina O, Sinitsyn V, Lyadov V. Preoperative CT-based detection of extrapancreatic perineural invasion in pancreatic cancer. Sci Rep. 2021;11(1):1–11.CrossRef

Zhang Y, Huang Z-X, Song B. Role of imaging in evaluating the response after neoadjuvant treatment for pancreatic ductal adenocarcinoma. World J Gastroenterol. 2021;27(22):3037.CrossRefPubMedPubMedCentral

Janssen BV, Verhoef S, Wesdorp NJ, et al. Imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in pancreatic cancer: a scoping review. Ann Surg. 2022;275(3):560–7.CrossRefPubMed

10.

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77. https://doi.org/10.1148/radiol.2015151169.CrossRefPubMed

11.

Healy GM, Salinas-Miranda E, Jain R, et al. Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation. Eur Radiol. 2022;32(4):2492–505.CrossRefPubMed

12.

Parr E, Du Q, Zhang C, et al. Radiomics-based outcome prediction for pancreatic cancer following stereotactic body radiotherapy. Cancers. 2020;12(4):1051.CrossRefPubMedPubMedCentral

13.

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(1):362–72.CrossRefPubMed

14.

Jeon SH, Song C, Chie EK, et al. Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer. Radiat Oncol. 2019;14(1):1–10.CrossRef

15.

Nasief H, Hall W, Zheng C, et al. Improving treatment response prediction for chemoradiation therapy of pancreatic cancer using a combination of delta-radiomics and the clinical biomarker CA19-9. Front Oncol. 2020;9:1464.CrossRefPubMedPubMedCentral

16.

Tomaszewski M, Latifi K, Boyer E, et al. Delta radiomics analysis of magnetic resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer. Radiat Oncol. 2021;16(1):1–11.CrossRef

17.

Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–38.CrossRefPubMed

18.

Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging. 2019;46(13):2638–55.CrossRefPubMed

19.

Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62. https://doi.org/10.1038/nrclinonc.2017.141.CrossRefPubMed

20.

Kocak B, Baessler B, Bakas S, et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging. 2023;14(1):75. https://doi.org/10.1186/s13244-023-01415-8.CrossRefPubMedPubMedCentral

21.

Hall WA, Heerkens HD, Paulson ES, et al. Pancreatic gross tumor volume contouring on computed tomography (CT) compared with magnetic resonance imaging (MRI): results of an international contouring conference. Pract Radiat Oncol. 2018;8(2):107–15. https://doi.org/10.1016/j.prro.2017.11.005.CrossRefPubMed

22.

Oar A, Lee M, Le H, et al. Australasian Gastrointestinal Trials Group (AGITG) and Trans-Tasman Radiation Oncology Group (TROG) guidelines for pancreatic stereotactic body radiation therapy (SBRT). Pract Radiat Oncol. 2020;10(3):e136–46. https://doi.org/10.1016/j.prro.2019.07.018.CrossRefPubMed

23.

Cellini F, Arcelli A, Simoni N, et al. Basics and frontiers on pancreatic cancer for radiation oncology: target delineation, SBRT, SIB technique, MRgRT, particle therapy, immunotherapy and clinical guidelines. Cancers (Basel). 2020. https://doi.org/10.3390/cancers12071729.CrossRefPubMed

24.

Vallieres M, Kay-Rivest E, Perrin LJ, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep. 2017;7(1):10117. https://doi.org/10.1038/s41598-017-10371-5.ADSCrossRefPubMedPubMedCentral

25.

D’Onofrio M, Ciaravino V, Cardobi N, et al. CT enhancement and 3D texture analysis of pancreatic neuroendocrine neoplasms. Sci Rep. 2019;9(1):2176. https://doi.org/10.1038/s41598-018-38459-6.ADSCrossRefPubMedPubMedCentral

26.

Jeon SH, Song C, Chie EK, et al. Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer. Radiat Oncol. 2019;14(1):43. https://doi.org/10.1186/s13014-019-1246-8.CrossRefPubMedPubMedCentral

27.

Fave X, Zhang LF, Yang JZ, et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017;7(1):588. https://doi.org/10.1038/s41598-017-00665-z.ADSCrossRefPubMedPubMedCentral

28.

Chiesa S, Bartoli FB, Longo S, et al. Delta radiomics features analysis for the prediction of patients outcomes in glioblastoma multiforme: the generating hypothesis phase of GLIFA project. Int J Radiat Oncol. 2018;102(3):S213–S213. https://doi.org/10.1016/j.ijrobp.2018.07.128.CrossRef

29.

Palumbo D, Mori M, Prato F, et al. Prediction of early distant recurrence in upfront resectable pancreatic adenocarcinoma: a multidisciplinary, machine learning-based approach. Cancers (Basel). 2021;13:19. https://doi.org/10.3390/cancers13194938.CrossRef

30.

Nasief H, Zheng C, Schott D, et al. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precis Oncol. 2019;3:25. https://doi.org/10.1038/s41698-019-0096-z.CrossRefPubMedPubMedCentral

31.

Cozzi L, Comito T, Fogliata A, et al. Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. PLoS ONE. 2019;14(1):e0210758. https://doi.org/10.1371/journal.pone.0210758.CrossRefPubMedPubMedCentral

32.

Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36. https://doi.org/10.1186/s41747-018-0068-z.CrossRefPubMedPubMedCentral

33.

Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L. A review of original articles published in the emerging field of radiomics. Eur J Radiol. 2020;127:108991. https://doi.org/10.1016/j.ejrad.2020.108991.CrossRefPubMed

34.

Chen Y, Chen TW, Wu CQ, et al. Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol. 2019;29(8):4408–17. https://doi.org/10.1007/s00330-018-5824-1.CrossRefPubMed

35.

Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK. Radiomics in stratification of pancreatic cystic lesions: Machine learning in action. Cancer Lett. 2020;469:228–37. https://doi.org/10.1016/j.canlet.2019.10.023.CrossRefPubMed

36.

Zhou ZG, Folkert M, Iyengar P, et al. Multi-objective radiomics model for predicting distant failure in lung SBRT. Phys Med Biol. 2017;62(11):4460–78. https://doi.org/10.1088/1361-6560/aa6ae5.CrossRefPubMedPubMedCentral

37.

Wang K, Zhou Z, Wang R, et al. A multi-objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancers. Med Phys. 2020;47(10):5392–400.CrossRefPubMed

38.

Poruk KE, Gay DZ, Brown K, et al. The clinical utility of CA 19–9 in pancreatic adenocarcinoma: diagnostic and prognostic updates. Curr Mol Med. 2013;13(3):340–51.PubMedPubMedCentral

39.

Ahmad SA, Duong M, Sohal DP, et al. Surgical outcome results from SWOG S1505: a randomized clinical trial of mFOLFIRINOX vs. gemcitabine/nab-paclitaxel for perioperative treatment of resectable pancreatic ductal adenocarcinoma. Ann Surg. 2020;272(3):481.CrossRefPubMed

40.

Oettle H, Neuhaus P, Hochhaus A, et al. Adjuvant chemotherapy with gemcitabine and long-term outcomes among patients with resected pancreatic cancer: the CONKO-001 randomized trial. JAMA. 2013;310(14):1473–81.CrossRefPubMed

41.

Neoptolemos JP, Stocken DD, Bassi C, et al. Adjuvant chemotherapy with fluorouracil plus folinic acid vs gemcitabine following pancreatic cancer resection: a randomized controlled trial. JAMA. 2010;304(10):1073–81.CrossRefPubMed

42.

Rigiroli F, Hoye J, Lerebours R, et al. Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma. Eur Radiol. 2023;33(8):5779–91. https://doi.org/10.1007/s00330-023-09532-0.CrossRefPubMed

43.

Khalvati F, Zhang Y, Baig S, et al. Prognostic value of CT radiomic features in resectable pancreatic ductal adenocarcinoma. Sci Rep. 2019. https://doi.org/10.1038/s41598-019-41728-7.CrossRefPubMedPubMedCentral

44.

Oikonomou A, Khalvati F, Tyrrell PN, et al. Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep. 2018. https://doi.org/10.1038/s41598-018-22357-y.CrossRefPubMedPubMedCentral

45.

Lubner MG, Stabo N, Abel EJ, Del Rio AM, Pickhardt PJ. CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. Am J Roentgenol. 2016;207(1):96–105.CrossRef

46.

Lubner MG, Stabo N, Lubner SJ, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdominal Imaging. 2015;40:2331–7.CrossRefPubMed

47.

Toyama Y, Hotta M, Motoi F, Takanami K, Minamimoto R, Takase K. Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer. Sci Rep. 2020. https://doi.org/10.1038/s41598-020-73237-3.CrossRefPubMedPubMedCentral

Title: Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients
Authors: Kai Wang, PhD
John D. Karalis, MD
Ahmed Elamir, MD
Alessandro Bifolco, MD
Megan Wachsmann, MD
Giovanni Capretti, MD
Paola Spaggiari, MD
Sebastian Enrico, BSA
Kishore Balasubramanian, MS
Nafeesah Fatimah, MD
Giada Pontecorvi, PhD
Martina Nebbia, MD
Adam Yopp, MD
Ravi Kaza, MD
Ivan Pedrosa, MD, PhD
Herbert Zeh III, MD
Patricio Polanco, MD
Alessandro Zerbi, MD
Jing Wang, PhD
Todd Aguilera, MD, PhD
Matteo Ligorio, MD, PhD
Publication date: 27-12-2023
Publisher: Springer International Publishing
Keywords: Computed Tomography
Computed Tomography
Published in: Annals of Surgical Oncology / Issue 4/2024
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI: https://doi.org/10.1245/s10434-023-14805-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 4/2024

ASO Author Reflections: Opting for the Most Appropriate Treatment Approach for Elderly Patients with Resectable Esophageal Cancer

ASO Author Reflections: To Test or Not to Test: How Do We Decide the Optimal Site for Organoid Generation in Patients with Metastatic Disease?

ASO Author Reflections: Comparison Between Plastic and Metallic Biliary Stent Placement for Preoperative Patients with Pancreatic Head Cancer—A Systematic Review and Meta-analysis

ASO Author Reflections: Preoperative Frailty and Nutritional Assessment of Patients Undergoing Oncologic Resections

ASO Visual Abstract: Double Negativity of MRI-Detected and Pathologically Diagnosed Extramural Venous Invasion is a Favorable Prognostic Factor for Rectal Cancer

ASO Visual Abstract: Minimally Invasive Approach Provides Oncological Benefit in Patients with High Risk of Very Early Recurrence (VER) After Surgery for Intrahepatic Cholangiocarcinoma (iCCA)