Share：
Share this content in WeChat
X
[Chinese][PDF]4053
• Clinical Articles •
The value of radiomics based on MRI T2WI in predicting the post-acute pancreatitis diabetes mellitus
DU Qinglin  HUANG Xiaohua  LIU Nian  CHEN Yuwei  ZHONG Shuting  LIU Zhuoyu 

Cite this article as: DU Q L, HUANG X H, LIU N, et al. The value of radiomics based on MRI T2WI in predicting the post-acute pancreatitis diabetes mellitus[J]. Chin J Magn Reson Imaging, 2023, 14(7): 67-72. DOI:10.12015/issn.1674-8034.2023.07.012.


[Abstract] Objective To evaluate the value of radiomics analysis based on T2WI sequence MRI in predicting post-acute pancreatitis diabetes mellitus (PPDM-A). Methods and Materials: Retrospective collection of patients diagnosed with acute pancreatitis (AP) in our hospital from January 2016 to December 2019, through clinical follow-up, they were divided into PPDM-A group (n=57) and normal blood glucose after AP group (n=83). The enrolled patients were randomly divided into the training group (n=98, 40 cases in the PPDM-A group, 58 cases in the normal blood glucose group after AP) and the validation group (n=42, 17 cases in the PPDM-A group, 25 cases in the normal blood glucose group after AP) at a ratio of 7∶3, and the relevant clinical characteristics of the two groups were collected at the same time. 3D Slicer software was used to delineate the edge of pancreatic parenchyma and extract features, variance threshold method, select K Best method and least absolute shrinkage and selection operator (LASSO) were used to extract the final radiomics features. Finally, random forest method was used to establish the clinical model, radiomics model and combined model for predicting PPDM-A. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive performance of the model, and DeLong test was used to compare the predictive efficiency of these models. The calibration curve of the best model was drawn and the Hosmer-Lemesow test was used to verify the goodness of fit of the model. The decision curve analysis (DCA) was used to evaluate the net clinical benefits of each model.Results The AUCs of the clinical model, radiomics model, and combined model in the training group were 0.773 [95% confidence interval (CI): 0.679-0.866], 0.831 (95% CI: 0.750-0.912) and 0.881 (95% CI: 0.816-0.946), respectively. In the validation group, they were 0.664 (95% CI: 0.488-0.839), 0.821 (95% CI: 0.684-0.958) and 0.899 (95% CI: 0.806-0.992), respectively. DeLong test results showed that the prediction performance of the combined model was statistically different from that of the clinical model in both the training and validation groups (training group: P=0.024, validation group: P=0.013). In the training group, there was a significant difference between the combined model and the radiomics model (P=0.047), but no significant difference was observed in the validation group (P=0.090). The Hosmer-Lemeshow test showed that the combined model was well calibrated (P=0.250). The DCA results indicated that both the radiomics model and the combined model had better net clinical benefits in predicting PPDM-A compared to the clinical model. Moreover, when the threshold probability was greater than 12%, the net clinical benefit of the combined model was superior to that of the radiomics model.Conclusions The combined model based on clinical features and T2WI radiomics features has a good predictive performance and can be used as an early method to predict the occurrence of PPDM-A.
[Keywords] radiomics；magnetic resonance imaging；acute pancreatitis；pancreatic diabetes mellitus；prediction

DU Qinglin   HUANG Xiaohua*   LIU Nian   CHEN Yuwei   ZHONG Shuting   LIU Zhuoyu  

Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China

Corresponding author: Huang XH, E-mail: 15082797553@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Nanchong City-School Science and Technology Strategic Cooperation Project (No. 20SXQT0303).
Received  2022-12-28
Accepted  2023-06-25
DOI: 10.12015/issn.1674-8034.2023.07.012
Cite this article as: DU Q L, HUANG X H, LIU N, et al. The value of radiomics based on MRI T2WI in predicting the post-acute pancreatitis diabetes mellitus[J]. Chin J Magn Reson Imaging, 2023, 14(7): 67-72. DOI:10.12015/issn.1674-8034.2023.07.012.

[1]
PETROV M S, YADAV D. Global epidemiology and holistic prevention of pancreatitis[J]. Nat Rev Gastroenterol Hepatol, 2019, 16(3): 175-184. DOI: 10.1038/s41575-018-0087-5.
[2]
QIAN S W, TONG Z H. Focus on the development and progression of post-acute pancreatitis diabetes mellitus[J]. Chin J Pancreatol, 2022, 22(2): 87-92. DOI: 10.3760/cma.j.cn115667-20220126-00015.
[3]
BHARMAL S H, CHO J, ALARCON RAMOS G C, et al. Trajectories of glycaemia following acute pancreatitis: a prospective longitudinal cohort study with 24 months follow-up[J].J Gastroenterol, 2020, 55(8): 775-788. DOI: 10.1007/s00535-020-01682-y.
[4]
WOODMANSEY C, MCGOVERN A P, MCCULLOUGH K A, et al. Incidence, demographics, and clinical characteristics of diabetes of the exocrine pancreas (type 3c): a retrospective cohort study[J]. Diabetes Care, 2017, 40(11): 1486-1493. DOI: 10.2337/dc17-0542.
[5]
CHO J, SCRAGG R, PETROV M S. Risk of mortality and hospitalization after post-pancreatitis diabetes mellitus vs type 2 diabetes mellitus: a population-based matched cohort study[J]. Am J Gastroenterol, 2019, 114(5): 804-812. DOI: 10.14309/ajg.0000000000000225.
[6]
PETROV M S. Post-pancreatitis diabetes mellitus and excess intra-pancreatic fat deposition as harbingers of pancreatic cancer[J]. World J Gastroenterol, 2021, 27(17): 1936-1942. DOI: 10.3748/wjg.v27.i17.1936.
[7]
CHO J, WALIA M, SCRAGG R, et al. Frequency and risk factors for mental disorders following pancreatitis: a nationwide cohort study[J]. Curr Med Res Opin, 2019, 35(7): 1157-1164. DOI: 10.1080/03007995.2018.1560748.
[8]
PETROV M S. Post-pancreatitis diabetes mellitus: investigational drugs in preclinical and clinical development and therapeutic implications[J]. Expert Opin Investig Drugs, 2021, 30(7): 737-747. DOI: 10.1080/13543784.2021.1931118.
[9]
LIU H, LI X Q, LUO G, et al. Risk factors for new-onset diabetes after incipient acute pancreatitis[J]. J Clin Hepatol, 2020, 36(12): 2771-2776. DOI: 10.3969/j.issn.1001-5256.2020.12.026.
[10]
CHO J, SCRAGG R, PANDOL S J, et al. Exocrine pancreatic dysfunction increases the risk of new-onset diabetes mellitus: results of a nationwide cohort study[J]. Clin Transl Sci, 2021, 14(1): 170-178. DOI: 10.1111/cts.12837.
[11]
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[12]
LAFATA K J, WANG Y Q, KONKEL B, et al. Radiomics: a primer on high-throughput image phenotyping[J].Abdom Radiol, 2022, 47(9): 2986-3002. DOI: 10.1007/s00261-021-03254-x.
[13]
Pancreatic Surgery Group of Surgery Branch of Chinese Medical Association. Guidelines for diagnosis and treatment of acute pancreatitis in China (2021)[J]. Chin J Surg, 2021, 59(7): 578-587. DOI: 10.3760/cma.j.cn112139-20210416-00172.
[14]
YU J, ZHANG H B, LI Y X. Pancreatogenic diabetes mellitus[J]. Chin J Diabetes Mellit, 2021, 13(1): 116-118. DOI: 10.3760/cma.j.cn115791-20201105-00656.
[15]
PETROV M S, BASINA M. DIAGNOSIS OF ENDOCRINE DISEASE: Diagnosing and classifying diabetes in diseases of the exocrine pancreas[J]. Eur J Endocrinol, 2021, 184(4): R151-R163. DOI: 10.1530/eje-20-0974.
[16]
KO J, SKUDDER-HILL L, CHO J, et al. The relationship between abdominal fat phenotypes and insulin resistance in non-obese individuals after acute pancreatitis[J/OL]. Nutrients, 2020, 12(9): 2883 [2022-12-27]. https://www.mdpi.com/2072-6643/12/9/2883. DOI: 10.3390/nu12092883.
[17]
CHAND S K, SINGH R G, PENDHARKAR S A, et al. Iron: a strong element in the pathogenesis of chronic hyperglycaemia after acute pancreatitis[J]. Biol Trace Elem Res, 2018, 183(1): 71-79. DOI: 10.1007/s12011-017-1131-y.
[18]
FIRKINS S A, HART P A, PAPACHRISTOU G I, et al. Identification of a risk profile for new-onset diabetes after acute pancreatitis[J]. Pancreas, 2021, 50(5): 696-703. DOI: 10.1097/MPA.0000000000001818.
[19]
MAN T, SEICEAN R, LUCACIU L, et al. Risk factors for new-onset diabetes mellitus following acute pancreatitis: a prospective study[J]. Eur Rev Med Pharmacol Sci, 2022, 26(16): 5745-5754. DOI: 10.26355/eurrev_202208_29511.
[20]
GUO S Y, YANG H Y, NING X Y, et al. Combination of body mass index and fasting blood glucose improved predictive value of new-onset prediabetes or diabetes after acute pancreatitis[J]. Pancreas, 2022, 51(4): 388-393. DOI: 10.1097/mpa.0000000000002025.
[21]
BHARMAL S H, KIMITA W, KO J, et al. Cytokine signature for predicting new-onset prediabetes after acute pancreatitis: a prospective longitudinal cohort study[J/OL]. Cytokine, 2022, 150: 155768 [2022-12-27]. https://www.sciencedirect.com/science/article/abs/pii/S1043466621003574?via%3Dihub. DOI: 10.1016/j.cyto.2021.155768.
[22]
LV Y Q, ZHANG J, YANG T, et al. Non-alcoholic fatty liver disease (NAFLD) is an independent risk factor for developing new-onset diabetes after acute pancreatitis: a multicenter retrospective cohort study in Chinese population[J/OL]. Front Endocrinol (Lausanne), 2022, 13: 903731 [2022-12-27]. https://www.frontiersin.org/articles/10.3389/fendo.2022.903731/full. DOI: 10.3389/fendo.2022.903731.
[23]
MA J H, YUAN Y J, LIN S H, et al. Nomogram for predicting diabetes mellitus after the first attack of acute pancreatitis[J]. Eur J Gastroenterol Hepatol, 2019, 31(3): 323-328. DOI: 10.1097/meg.0000000000001307.
[24]
HART P A, BRADLEY D, CONWELL D L, et al. Diabetes following acute pancreatitis[J]. Lancet Gastroenterol Hepatol, 2021, 6(8): 668-675. DOI: 10.1016/S2468-1253(21)00019-4.
[25]
RICHARDSON A, PARK W G. Acute pancreatitis and diabetes mellitus: a review[J]. Korean J Intern Med, 2021, 36(1): 15-24. DOI: 10.3904/kjim.2020.505.
[26]
HU Y T, HUANG X H, LIU N, et al. The value of T2WI sequence-based radiomics in predicting recurrence of acute pancreatitis[J]. Chin J Magn Reson Imaging, 2021, 12(10): 12-15. DOI: 10.12015/issn.1674-8034.2021.10.003.
[27]
FAN H Y, CHEN J M, CHEN L L, et al. The value of CT based radiomics in predicting progression of early acute pancreatitis[J]. Chin J Radiol, 2022, 56(7): 778-784. DOI: 10.3760/cma.j.cn112149-20210829-00615.
[28]
TANG L L, MA L, CHEN Y Y, et al. Radiomics analysis of contrast-enhanced T1W MRI: predicting the recurrence of acute pancreatitis[J/OL]. Sci Rep, 2023, 13: 2762 [2022-12-27]. https://www.nature.com/articles/s41598-022-13650-y. DOI: 10.1038/s41598-022-13650-y.
[29]
ZWANENBURG A, VALLIÈRES M, ABDALAH M A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2): 328-338. DOI: 10.1148/radiol.2020191145.
[30]
ABBASIAN ARDAKANI A, BUREAU N J, CIACCIO E J, et al. Interpretation of radiomics features-a pictorial review[J/OL]. Comput Methods Programs Biomed, 2022, 215: 106609 [2022-12-27]. https://www.sciencedirect.com/science/article/abs/pii/S0169260721006830?via%3Dihub. DOI: 10.1016/j.cmpb.2021.106609.

PREV Effect of BOLD-fMRI on early renal function in patients with type 2 diabetes mellitu
NEXT Study on the application value of DWI combined with conventional MRI in making surgical plan of PDS for advanced ovarian cancer
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn