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Page 173 De Robertis et al. Art Int Surg 2023;3:166-79 https://dx.doi.org/10.20517/ais.2023.18

Response to treatment
Early and accurate response evaluation in patients with LM would be of importance given the availability of
several different treatment modalities, and several studies [47-50] reported a significant association between
radiomics features and response to chemotherapy and targeted therapies.

PANCREATIC NEOPLASMS
The potential usefulness of radiomics in preoperative staging and prediction of histological findings and
clinical outcomes was reported by three systematic reviews and meta-analyses; these studies highlighted the
low quality of most radiomics studies conducted so far. Abunahel et al. included 72 studies encompassing
[51]
8,863 participants; 66 studies investigated focal pancreatic lesions . Overall, second-order features were the
most useful for lesion characterization, while filtered image features were most useful for classification and
prognosis predictions. The median RQS of studies included was 28%, and it was significantly correlated
both with the amount of features (r = 0.52, P < 0.001) and the size of the study population (r = 0.34,
P = 0.003). The meta-analysis by Gao et al. evaluated 23 studies . Two of them showed better prognosis
[52]
prediction performance of radiomics compared to TNM staging; 9 studies demonstrated a significant
correlation between entropy, a radiomic feature describing the uncertainty or randomness in the image
values, and OS (mean HR = 1.66). Staal et al. included 45 studies on pancreatic neuroendocrine neoplasms
[53]
(pNENs) . The mean RQS of the studies was 18%. In most studies, radiomics features could predict tumor
grade or differentiate pNENs from other lesions with AUCs = 0.74-0.96 and 0.80-0.99, respectively; one
study developed a predictive model for disease recurrence (AUC = 0.77).

Early detection of pancreatic cancer
Despite improvements in the multidisciplinary treatment approach to patients with pancreatic ductal
adenocarcinoma (PDAC), the prognosis of this disease remains very poor, as PDAC is frequently diagnosed
at an advanced stage. The goal of screening and surveillance programs is to detect and treat stage I PDAC
and cancer precursor lesions as intraductal papillary mucinous neoplasms (IPMNs) with high-grade
[54]
dysplasia . Even though encouraging results were described by two studies [55,56] , a meta-analysis by Chhoda
et al. reported a significant proportion of PDACs diagnosed at a late stage during follow-up, which limits
the survival benefit of surveillance . Although no studies have been conducted in this regard, AI and
[57]
radiomics could theoretically play a role, as they can automatically differentiate between cancer and normal
pancreas, as reported by Chu et al.: in this study, 427 features were extracted from CT images; overall, the
accuracy of the binary random forest classification was 99.2%, with an AUC of 99.9%, a sensitivity of 100%
and a specificity of 98.5%; a major limitation of this study is that the mean tumor size was 4.1 cm, which
usually corresponds to a non resectable disease . Two studies by Qureshi et al. and Javed et al.
[58]
[60]
[59]
identified several features that may be predictive of PDAC development extracted from pre-diagnostic CT
scans in PDAC patients: the predictive model had an accuracy of 86%-89.3%, a sensitivity of 86% and a
specificity of 93%; unfortunately, these results were based on a very limited study population and needed
further validation. The most robust study on early automated detection of PDAC was published by
Mukherjee et al.: they used a radiomics-based machine learning model to detect PDAC before the clinical
diagnosis based on volumetric segmentation of the pancreas performed on pre-diagnostic CT scans in 155
PDAC patients and 265 normal subjects . A supporting vector machine model had high sensitivity
[61]
(95.5%), specificity (90.3%), AUC (0.98), and accuracy (92.2%) for the classification of CT into pre-
diagnostic versus normal. The paradigm for early detection of cancer in IPMN is based on “worrisome
features” and “high-risk stigmata”. Accurate prediction of the malignant potential of IPMN is of great
importance. Nevertheless, studies on AI/radiomic applications in identifying “malignant” IPMNs comprised
[62]
very small study populations. The largest were proposed by Jeon et al. and Chakraborty et al. , who
[63]
reported that radiomics features improve the performance of MR and CT for predicting malignant IPMNs.
Circulating micro RNAs (miRNAs) may be diagnostic biomarkers of incidentally detected IPMNs and