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Poulos et al. Mini-invasive Surg. 2025;9:6 https://dx.doi.org/10.20517/2574-1225.2024.42 Page 3 of 6

evaluation, interval cancers have been seen with what would typically be considered “low risk” lesions. As a
result of this alarming issue, a tissue systems pathology test (TissueCypher Barrett’s Esophagus Test, TSP-9)
was developed and incorporates AI with biomarkers in an effort to predict the individual’s true risk of
[15]
Barrett’s progression . As patient response to therapy is variable, AI tools can guide multidisciplinary
treatment plans and optimize patient outcomes. Rice et al., in their study using ML to analyze optimal
treatment for esophageal cancer, found that 61% of patients who underwent esophagectomy alone received
optimal treatment. In contrast, only 36% of patients who received neoadjuvant therapy were deemed to have
received optimal therapy. These estimates were based on random forest technology in a sequential analysis.
Optimal therapy was classified as the treatment modality that, after survival analysis using patient and
[16]
cancer characteristics, maximized lifetime as measured by restricted mean survival time . Another large
study on 418 patients with esophageal adenocarcinoma undergoing surgery with curative intent utilized 65
variables from individualized patient data and used ANNs to predict one- and five-year survival. These
variables spanned the full spectrum of care from symptoms at presentation to postoperative data such as
tumor gene expression. Two ANNs were developed and compared against a linear discriminant analysis
(LDA) to assess accuracy. Their results showed that the ANNs were more accurate than the LDA models
and were superior to a model based solely on the tumor-node-metastasis (TNM) staging criteria when
predicting survival .
[17]

Other groups have used AI to predict a patient’s responsiveness to chemotherapy. Using ANNs, real time
polymerase chain reaction assays of pre- and post-treatment esophageal cancer specimens were analyzed for
17 genes in an attempt to predict histopathologic tumor response to chemoradiation. Not only did the
analysis identify specific independent risk factors in the study population, it also outperformed univariate
and multivariate analysis in terms of predicting response to treatment. The analysis of these gene expression
arrays could predict tumor response to traditional neoadjuvant therapy with 85% accuracy . Alternatively,
[18]
ANNs trained on 18-fluorodeoxyglucose positron emission tomography (PET) scans from 107 patients with
esophageal cancer were able to predict chemotherapy non-responders with > 80% sensitivity/specificity.
Ypsilantis et al. used an “radiomics” approach where large amounts of quantitative data were extracted from
pretreatment PET images to compile a tumor phenotype and employed an ANN to learn from intra-tumor
slices seen on PET scans. Their model was able to extract PET image representations that could predict
[19]
non-responders to treatment with 80.7% sensitivity and 81.6% specificity . Oftentimes, non-responders to
neoadjuvant chemotherapy have a worse prognosis compared with those individuals treated with upfront
[19]
surgery . This study highlights the potential use of AI in non-invasively predicting cancer treatment
response which would allow for a more personalized approach to esophageal cancer patients.

INTRAOPERATIVE AND POSTOPERATIVE SURGICAL CARE
From a surgeon’s perspective, there are a number of hypothesized uses for AI applications to current
surgical techniques including intraoperative support, surgical training, and postoperative care . AI systems
[20]
have potential for intraoperative utility by way of anatomic structure identification. As outlined in a meta-
analysis by Anteby et al., ANNs have shown the ability to harness unlabeled laparoscopic footage to achieve
precise tasks such as anatomy detection, instrument identification, action recognition, and surgical phase
[21]
categorization . Analysis of ANN performance in quantifying data from laparoscopic videos showed
sensitivity as high as 95%, with a limitation being the heterogeneous nature of their pooled data from
multiple different procedure types .
[22]

Similar techniques have been applied to RAMIEs where ANNs were trained to identify key anatomic
structures including the azygos vein, superior vena cava, aorta, lung parenchyma, or the recurrent laryngeal
nerve [22,23] . Sato et al. created an AI model to identify the recurrent laryngeal nerve after training on 2,000