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De Robertis et al. Art Int Surg 2023;3:166-79 https://dx.doi.org/10.20517/ais.2023.18 Page 174

predictors of their histological classification: a study by Permuth et al. suggested that the combination of
radiomics features with the miRNA genomic classifier (MGC) had an AUC = 0.92 and sensitivity (83%),
[64]
specificity (89%), PPV (88%), and NPV (85%) for prediction of IPMN malignancy .

Differential diagnosis
Radiomics is a promising tool to improve the characterization of focal pancreatic lesions. Zhang et al.
developed CT and radiomics nomograms for the differentiation between mass-forming pancreatitis (MFP)
and PDAC in patients with chronic pancreatitis (CP) . 138 patients with histopathologically diagnosed
[65]
MFCP or PDAC were retrospectively analyzed. Both models had good performance in differentiating
between the two entities in the training (AUC = 0.87/0.91) and validation (AUC = 0.94) cohorts. A
radiomics-based computer-aided diagnosis scheme proposed by Wei et al. had AUC = 0.767, sensitivity =
0.686, and specificity = 0.709 for preoperative diagnosis of cystic neoplasms .
[66]

Prediction of prognosis
Tumor grade is a major prognostic factor for pancreatic neoplasms. Chang et al. and Tikhonova et al.
[67]
[68]
reported significant differences in radiomics signatures between PDAC of different grades, with AUC
ranging from 0.66 to 0.77. Tumor grade is even more important from a prognostic point of view for pNENs.
Gu et al. included 138 patients with pathologically confirmed pNENs (training cohort, 104 patients;
validation cohort, 34 patients) . A nomogram integrating tumor margin status and a radiomic signature
[69]
derived from CT images showed strong discrimination with AUCs of 0.974 and 0.902 in the training and
validation cohort, respectively.

Several studies conducted a radiomic analysis to predict features with a negative prognostic role of pNENs.
In particular, a study retrospectively analyzed the MR histogram features of 42 patients with pNEN:
[70]
ADCentropy was higher in G2-3 tumors with AUC = 0.757, sensitivity and specificity of 83.3% and 61.1%,
while kurtosis was higher in pNENs with vascular infiltration, lymph node and liver metastases (P = 0.008,
0.021 and 0.008; AUC = 0.820, 0.709 and 0.820). Mori et al. evaluated radiomics features extracted from
unenhanced CT images and reported AUCs of 0.81/0.81 for the radiomic and clinic-radiological model for
[71]
metastases and 0.67/0.72 and 0.68/0.70 for tumor grade . Transcriptional classifiers are key prognostic
factors of PDAC. Salinas-Miranda et al. developed a radiomics score that was significantly associated
[72]
(coefficient = 0.31) with the four PDAC subtypes (squamous, pancreatic progenitor, immunogenic, and
aberrantly differentiated endocrine exocrine) classified by Bailey et al. through gene expression analysis.
[73]
Preoperative staging of pancreatic neoplasms includes the evaluation of vascular involvement, node and
distant metastases. Rigiroli et al. developed a 5-feature model with AUC = 0.71 for preoperative assessment
of superior mesenteric artery involvement by PDAC . Bian et al. developed a radiomic score that was
[74]
[76]
linked to the possibility of nodal metastasis . Finally, a study that comprised 220 patients developed a
[75]
logistic regression model composed of eight variables that had sensitivity, specificity, PPV, NPV and AUC
of 58.6%, 91.3%, 75.9%, 82.5%, and 0.850 for prediction of simultaneous liver metastases in PDAC patients.
Early recurrence after surgery is not uncommon in PDAC patients. Although the reasons for this have not
been defined with certainty, the availability of a non-invasive biomarker would be clinically useful in
patients with resectable PDAC to avoid unbeneficial surgery. Tang et al. analyzed 303 PDAC patients; the
AUC values for prediction of early recurrence of radiomics signatures were 0.80, 0.81, and 0.78 in the
training, inner validation, and outer validation cohorts, respectively; the AUC values for early recurrence
were 0.87, 0.88, and 0.85 in the training, internal validation, and external validation cohorts . A study
[77]
aimed to correlate conventional and radiomics MR features with the risk and the time to metastases after
surgical resection in patients with resected PDAC ; 120 patients were included. ADC skewness had a
[78]
significant impact on the risk of metastases, with HR = 5.22 (P < 0.001): the time to metastases was
significantly shorter (11.7 vs. 30.8 months, P < 0.001) in patients with an ADC skewness value greater than