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References | Case numbers | Radiomic method | Results |
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[96] | 164 | CT | CT-based radiomic classifiers had the potential to differentiate serous cystadenoma from IPMN and MCN |
[97] | 38 | CT | Radiomic method may more accurately predict IPMNs pathology than radiologic features considered in consensus guidelines |
[98] | 53 | CT | Radiomics could predict the malignant potential of intraductal papillary mucinous neoplasms and had important application values in clinical decision making |
[99] | 260 | CT | The proposed radiomic-based computer-aided diagnosis scheme could increase preoperative diagnostic accuracy and assist clinicians in making accurate management decisions |
[100] | 78 | CT | Radiomics made a contribution to the differentiation of pancreatic serous cystadenomas and mucinous cystadenomas |
[101] | 225 | CT | Radiomic features were independently and positively associated with the risk of LN metastasis in PDAC |
[102] | 159 | CT | CT radiomic signature could be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC |
[105] | 20 | CT | CT radiomic features may be potentially used for early assessment of treatment response and stratification for therapeutic intensification |
[106] | 90 | CT | Radiomics may develop into a biomarker for early prediction of treatment response |
[107] | 74 | CT | Overall survival and recurrence could be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer |
[108] | 24 | CT | Combining radiomics with CA19-9 could improve the ability to predict posttreatment responses |
[112] | Not mentioned | MRI | Radiomics could be used as an imaging biomarker for early immunotherapy response assessment in a KPC transgenic mouse model of PDAC |
[114] | 301 | CT | CT radiomic signature showed moderate predictive accuracy for differentiating low-grade from high-grade PDAC and should become a new noninvasive method for the preoperative prediction of histological grades of PDAC |
[115] | 86 | CT | Radiomics was rewarding for the aided diagnosis of R0 and R1. Texture features could potentially enhance physicians’ diagnostic ability |
[116] | 88 | CT | CT radiomics could be used for predicting the prognosis in pancreas head cancer patients who underwent curative resection |
[117] | 63 | MRI | MRI-based radiomic features were associated with overall survival in patients with pancreatic cancer |
[118] | 132 | MRI | Radiomic models had the potential to predict tumor subtypes and overall survival in PDAC |
[119] | 100 | CT | A CT-based radiomic signature was correlated with overall survival and local control after stereotactic body radiation therapy and allowed to identify low and high-risk groups of patients |
[120] | 98 | CT | The proposed survival model outperforms Cox proportional hazard model-based radiomic pipeline in PDAC prognosis |
[121] | 106 | CT | Radiomics was assisted in selecting an appropriate candidate for irradiation stents in patients with unresectable pancreatic cancer |
[122] | 117 | CT | Radiomics had the potential to predict pancreatic fistula operatively in patients who would receive pancreaticoduodenectomy |
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