Machine learning to predict early recurrence after oesophageal cancer surgery

Design of risk prediction model for esophageal cancer based on machine learning approach

Background and aim

Esophageal cancer (EC) is a highly prevalent and progressive disease. Early prediction of EC risk in the population is crucial in preventing this disease and enhancing the overall health of individuals. So far, few studies have been conducted on predicting the EC risk based on the prediction models, and most of them focused on statistical methods. The ML approach obtained efficient predictive insights into the clinical domain. Therefore, this study aims to develop a risk prediction model for EC based on risk factors and by leveraging the ML approach to stratify the high-risk EC people and obtain efficient preventive purposes at the community level.

Material and methods

The current retrospective study was performed from 2018 to 2022 in Sari City based on 3256 EC and non-EC cases. The six selected algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XG-Boost), Bagging, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs), were used to develop the risk prediction model for EC and achieve the preventive purposes.

Results

Comparing the performance efficiency of algorithms revealed that the XG-Boost model gained the best predictability for EC risk with AU-ROC = 0.92 and AU-ROC-test = 0.889 for internal and validation states, respectively. Based on the XG-Boost, the factors, including sex, drinking hot liquids, fruit consumption, achalasia, and vegetable consumption, were considered the five top predictors of EC risk.

Conclusion

This study showed that the XG-Boost could provide insight into the early prediction of the EC risk for people and clinical providers to stratify the high-risk group of EC and achieve preventive measures based on modifying the risk factors associated with EC and other clinical solutions.

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI's vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.

A scoring model for predicting early recurrence of gastric cancer with normal preoperative tumor markers: A multicenter study

INTRODUCTION

Prognostic factors for postoperative early recurrence (ER) of gastric cancer (GC) in patients with normal or abnormal preoperative tumor markers (pre-TMs) remain unclear.

MATERIALS AND METHODS

2875 consecutive patients with GC who underwent radical gastrectomy (RG) between January 2010 and December 2016 were enrolled and randomly divided into training and internal validation groups. ER was defined as recurrence within two years of gastrectomy. Normal pre-TMs were defined as CEA≤5 ng/mL and CA199 ≤ 37 U/mL. Least absolute shrinkage selection operator (LASSO) Cox regression analysis was used to screen ER predictors. The scoring model was validated using 546 patients from another hospital.

RESULTS

A total of 3421 patients were included. Multivariate Cox analysis showed that pre-TMs was an independent prognostic factor for ER. Survival after ER was equally poor in the normal and abnormal pre-TMs groups (P = 0.160). Based on LASSO Cox regression, the ER of patients with abnormal pre-TMs was only associated with the pT and pN stages; however, in patients with normal pre-TMs, it was also associated with tumor size, perineural invasion, and prognostic nutritional index. Scoring model constructed for patients with normal pre-TMs had better predictive performance than TNM staging (concordance-index:0.826 vs. 0.807, P < 0.001) and good reproducibility in both validation sets. Moreover, through risk stratification, the scoring model could not only identify the risk of ER but also distinguish ER patterns and adjuvant chemotherapy benefit subgroups.

CONCLUSION

pre-TMs is an independent prognostic factor for ER in GC after RG. The established scoring model demonstrates excellent predictive performance and clinical utility.

Clinical predictors of early postoperative recurrence after radical esophagectomy for thoracic esophageal cancer

PURPOSE

Esophagectomy for esophageal cancer has a high incidence rate of early postoperative recurrence and death. This study aimed to identify the clinical and pathological features in early recurrence cases and to confirm the usefulness of prediction using these factors for effective adjuvant therapy and postoperative surveillance.

METHODS

One hundred and twenty five patients who developed postoperative recurrence after undergoing radical esophagectomy for thoracic esophageal cancer were classified into two groups as follows: those with early recurrence at ≤ 6 months and those with nonearly recurrence at > 6 months after surgery. After identifying related factors of early recurrence, usefulness of these factors for prediction were examined in all patients with and without recurrence.

RESULTS

The analysis cohort consisted of 43 and 82 patients in the early and nonearly recurrence groups, respectively. In multivariate analysis, factors associated with early recurrence were higher initial levels of tumor markers (squamous cell carcinoma [SCC] ≥ 1.5 ng/ml in tumors, except for adenocarcinoma, and carcinoembryonic antigen [CEA] ≥ 5.0 ng/ml in adenocarcinoma) and higher venous invasion (v), i.e., ≥ 2 (p = 0.040 and p = 0.004, respectively). The usefulness of these two factors for recurrence prediction was confirmed in 378 patients, including 253 patients without recurrence. Patients with at least one of the two factors had significantly higher early recurrence rates than those without any factors in pStages II and III (odds ratio [OR], 6.333; p = 0016 and OR, 4.346; p = 0.008, respectively).

CONCLUSIONS

Early recurrence of thoracic esophageal cancer (i.e., during ≤ 6 months after esophagectomy) was associated with higher initial tumor marker levels and pathological findings of v ≥ 2. The combination of these two factors is useful as a simple and critical predictor of early postoperative recurrence.

Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study

Obstructive

To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics.

Methods

A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models.

Results

1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722-0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models.

Conclusion

Machine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.

Detection of Cancer Recurrence Using Systemic Inflammatory Markers and Machine Learning after Concurrent Chemoradiotherapy for Head and Neck Cancers

Pretreatment values of the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are well-established prognosticators in various cancers, including head and neck cancers. However, there are no studies on whether temporal changes in the NLR and PLR values after treatment are related to the development of recurrence. Therefore, in this study, we aimed to develop a deep neural network (DNN) model to discern cancer recurrence from temporal NLR and PLR values during follow-up after concurrent chemoradiotherapy (CCRT) and to evaluate the model's performance compared with conventional machine learning (ML) models. Along with conventional ML models such as logistic regression (LR), random forest (RF), and gradient boosting (GB), the DNN model to discern recurrences was trained using a dataset of 778 consecutive patients with primary head and neck cancers who received CCRT. There were 16 input features used, including 12 laboratory values related to the NLR and the PLR. Along with the original training dataset (N = 778), data were augmented to split the training dataset (N = 900). The model performance was measured using ROC-AUC and PR-AUC values. External validation was performed using a dataset of 173 patients from an unrelated external institution. The ROC-AUC and PR-AUC values of the DNN model were 0.828 ± 0.032 and 0.663 ± 0.069, respectively, in the original training dataset, which were higher than the ROC-AUC and PR-AUC values of the LR, RF, and GB models in the original training dataset. With the recursive feature elimination (RFE) algorithm, five input features were selected. The ROC-AUC and PR-AUC values of the DNN-RFE model were higher than those of the original DNN model (0.883 ± 0.027 and 0.778 ± 0.042, respectively). The ROC-AUC and PR-AUC values of the DNN-RFE model trained with a split dataset were 0.889 ± 0.032 and 0.771 ± 0.044, respectively. In the external validation, the ROC-AUC values of the DNN-RFE model trained with the original dataset and the same model trained with the split dataset were 0.710 and 0.784, respectively. The DNN model with feature selection using the RFE algorithm showed the best performance among the ML models to discern a recurrence after CCRT in patients with head and neck cancers. Data augmentation by splitting training data was helpful for model performance. The performance of the DNN-RFE model was also validated with an external dataset.

Machine learning applications in upper gastrointestinal cancer surgery: a systematic review

BACKGROUND

Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies.

METHODS

A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models.

RESULTS

From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy.

CONCLUSIONS

Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML.

Submucosal Tunneling Techniques for Tumor Resection

The concept of third space endoscopy is based on the principle that the deeper layers of the gastrointestinal tract can be accessed by tunneling in the submucosal space and maintaining the integrity of the overlying mucosa. The mucosal flap safety valve enabled endoscopists to use submucosal space securely. The era of third space endoscopy started with peroral endoscopic myotomy for treatment of achalasia and has expanded to treat various other gastrointestinal disorders, such as mucosal lesions, submucosal tumors, extraluminal tumors, and refractory gastroparesis, Zenker diverticulum, and restoration of the completely obstructed esophageal lumen. Third space endoscopy rapidly emerged as a minimally invasive alternative to conventional surgery. Many studies discovered that this technique is safe and effective with excellent outcomes. Our review focused on the indications, techniques, clinical management, and adverse events of submucosal tunneling techniques for tumor resection.

Implementation of Machine Learning to Predict Cost of Care Associated with Ambulatory Single-Level Lumbar Decompression

BACKGROUND

With the emergence of the concept of value-based care, efficient resource allocation has become an increasingly prominent factor in surgical decision-making. Validated machine learning (ML) models for cost prediction in outpatient spine surgery are limited. As such, we developed and internally validated a supervised ML algorithm to reliably identify cost drivers associated with ambulatory single-level lumbar decompression surgery.

METHODS

A retrospective review of the New York State Ambulatory Surgical Database was performed to identify patients who underwent single-level lumbar decompression from 2014 to 2015. Patients with a length of stay of >0 were excluded. Using pre- and intraoperative parameters (features) derived from the New York State Ambulatory Surgical Database, an optimal supervised ML model was ultimately developed and internally validated after 5 candidate models were rigorously tested, trained, and compared for predictive performance related to total charges. The best performing model was then evaluated by testing its performance on identifying relationships between features of interest and cost prediction. Finally, the best performing algorithm was entered into an open-access web application.

RESULTS

A total of 8402 patients were included. The gradient-boosted ensemble model demonstrated the best performance assessed via internal validation. Major cost drivers included anesthesia type, operating room time, race, patient income and insurance status, community type, worker's compensation status, and comorbidity index.

CONCLUSIONS

The gradient-boosted ensemble model predicted total charges and associated cost drivers associated with ambulatory single-level lumbar decompression using a large, statewide database with excellent performance. External validation of this algorithm in future studies may guide practical application of this clinical tool.

Prediction of persistent acute kidney injury in postoperative intensive care unit patients using integrated machine learning: a retrospective cohort study

Acute kidney injury (AKI) often occurs in patients in the intensive care unit (ICU). AKI duration is closely related to the prognosis of critically ill patients. Identifying the disease course length in AKI is critical for developing effective individualised treatment. To predict persistent AKI at an early stage based on a machine learning algorithm and integrated models. Overall, 955 patients admitted to the ICU after surgery complicated by AKI were retrospectively evaluated. The occurrence of persistent AKI was predicted using three machine learning methods: a support vector machine (SVM), decision tree, and extreme gradient boosting and with an integrated model. External validation was also performed. The incidence of persistent AKI was 39.4-45.1%. In the internal validation, SVM exhibited the highest area under the receiver operating characteristic curve (AUC) value, followed by the integrated model. In the external validation, the AUC values of the SVM and integrated models were 0.69 and 0.68, respectively, and the model calibration chart revealed that all models had good performance. Critically ill patients with AKI after surgery had high incidence of persistent AKI. Our machine learning model could effectively predict the occurrence of persistent AKI at an early stage.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Objective

Using visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.

Methods

On April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.

Results

A total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General &amp; Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.

Conclusions

An increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.

Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo

PURPOSE

Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN).

METHODS

Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out.

RESULTS

A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively.

CONCLUSION

Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.

Development and validation of metabolic scoring to individually predict prognosis and monitor recurrence early in gastric cancer: A large-sample analysis

PURPOSE

To develop and validate a simple metabolic score (Metabolic score, MS) for use in evaluating the prognosis of gastric cancer (GC) patients and dynamically monitor for early recurrence.

METHODS

We retrospectively collected general clinicopathological data of patients who underwent radical gastrectomy for GC between September 2012 and December 2017 in the Department of Gastric Surgery of the Fujian Medical University Union Hospital. Using a random forest algorithm to screen preoperative blood indicators into the Least absolute shrinkage and selection operator (LASSO) model, we developed a novel MS to predict prognosis.

RESULTS

Data of 1974 patients were used to develop and validate the model. Total cholesterol (TCHO), bilirubin (TBIL), direct bilirubin (DBIL), and 15 other metabolic indicators had significant predictive value for the prognosis using the random forest algorithm. In the overall population, 533 patients (27.0%) had high and 1441 (73%) had low MS status. High MS status was related to tumor progression. The KM curves of 3-year OS and RFS for training set patients showed low MS had a better prognosis than high MS (OS: 79.4% vs 59.7%, P < 0.001; RFS: 76.0% vs 56.2%, P < 0.001).

CONCLUSIONS

We have developed and validated MS to predict the long-term survival of GC patients and allow early monitoring of recurrence. This will provide physicians with simple, economical, and dynamic tumor monitoring information.

Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy

PURPOSE

Recovery following elective knee arthroscopy can be compromised by prolonged postoperative opioid utilization, yet an effective and validated risk calculator for this outcome remains elusive. The purpose of this study is to develop and validate a machine-learning algorithm that can reliably and effectively predict prolonged opioid consumption in patients following elective knee arthroscopy.

METHODS

A retrospective review of an institutional outcome database was performed at a tertiary academic medical centre to identify adult patients who underwent knee arthroscopy between 2016 and 2018. Extended postoperative opioid consumption was defined as opioid consumption at least 150 days following surgery. Five machine-learning algorithms were assessed for the ability to predict this outcome. Performances of the algorithms were assessed through discrimination, calibration, and decision curve analysis.

RESULTS

Overall, of the 381 patients included, 60 (20.3%) demonstrated sustained postoperative opioid consumption. The factors determined for prediction of prolonged postoperative opioid prescriptions were reduced preoperative scores on the following patient-reported outcomes: the IKDC, KOOS ADL, VR12 MCS, KOOS pain, and KOOS Sport and Activities. The ensemble model achieved the best performance based on discrimination (AUC = 0.74), calibration, and decision curve analysis. This model was integrated into a web-based open-access application able to provide both predictions and explanations.

CONCLUSION

Following appropriate external validation, the algorithm developed presently could augment timely identification of patients who are at risk of extended opioid use. Reduced scores on preoperative patient-reported outcomes, symptom duration and perioperative oral morphine equivalents were identified as novel predictors of prolonged postoperative opioid use. The predictive model can be easily deployed in the clinical setting to identify at risk patients thus allowing providers to optimize modifiable risk factors and appropriately counsel patients preoperatively.

LEVEL OF EVIDENCE

III.

The Use of miRNAs in Predicting Response to Neoadjuvant Therapy in Oesophageal Cancer

Oesophageal cancer (OC) is the ninth most common cancer worldwide. Patients receive neoadjuvant therapy (NAT) as standard of care, but less than 20% of patients with oesophageal adenocarcinoma (OAC) or a third of oesophageal squamous cell carcinoma (OSCC) patients, obtain a clinically meaningful response. Developing a method of determining a patient's response to NAT before treatment will allow rational treatment decisions to be made, thus improving patient outcome and quality of life. (1) Background: To determine the use and accuracy of microRNAs as biomarkers of response to NAT in patients with OAC or OSCC. (2) Methods: MEDLINE, EMBASE, Web of Science and the Cochrane library were searched to identify studies investigating microRNAs in treatment naïve biopsies to predict response to NAT in OC patients. (3) Results: A panel of 20 microRNAs were identified as predictors of good or poor response to NAT, from 15 studies. Specifically, miR-99b, miR-451 and miR-505 showed the strongest ability to predict response in OAC patients along with miR-193b in OSCC patients. (4) Conclusions: MicroRNAs are valuable biomarkers of response to NAT in OC. Research is needed to understand the effects different types of chemotherapy and chemoradiotherapy have on the predictive value of microRNAs; studies also require greater standardization in how response is defined.

Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma

The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular data from consecutive trabeculectomy surgeries from a single academic institution between January 2014 and December 2018 were incorporated into models using random forest, support vector machine, artificial neural networks and multivariable logistic regression. Mean area under the receiver operating characteristic curve (AUC) and accuracy were used to evaluate the discrimination of each model to predict complete success of trabeculectomy surgery at 1 year. The top performing model was optimized using recursive feature selection and hyperparameter tuning. Calibration and net benefit of the final models were assessed. Among the 230 trabeculectomy surgeries performed on 184 patients, 104 (45.2%) were classified as complete success. Random forest was found to be the top performing model with an accuracy of 0.68 and AUC of 0.74 using 5-fold cross-validation to evaluate the final optimized model. These results provide evidence that machine learning models offer value in predicting trabeculectomy outcomes in patients with refractory glaucoma.

Impact of postoperative chemotherapy on survival for oesophagogastric adenocarcinoma after preoperative chemotherapy and surgery

BACKGROUND

Perioperative chemotherapy is widely used in the treatment of oesophagogastric adenocarcinoma (OGAC) with a substantial survival benefit over surgery alone. However, the postoperative part of these regimens is given in less than half of patients, reflecting uncertainty among clinicians about its benefit and poor postoperative patient fitness. This study estimated the effect of postoperative chemotherapy after surgery for OGAC using a large population-based data set.

METHODS

Patients with adenocarcinoma of the oesophagus, gastro-oesophageal junction or stomach diagnosed between 2012 and 2018, who underwent preoperative chemotherapy followed by surgery, were identified from a national-level audit in England and Wales. Postoperative therapy was defined as the receipt of systemic chemotherapy within 90 days of surgery. The effectiveness of postoperative chemotherapy compared with observation was estimated using inverse propensity treatment weighting.

RESULTS

Postoperative chemotherapy was given to 1593 of 4139 patients (38.5 per cent) included in the study. Almost all patients received platinum-based triplet regimens (4004 patients, 96.7 per cent), with FLOT used in 3.3 per cent. Patients who received postoperative chemotherapy were younger, with a lower ASA grade, and were less likely to have surgical complications, with similar tumour characteristics. After weighting, the median survival time after postoperative chemotherapy was 62.7 months compared with 50.4 months without chemotherapy (hazard ratio 0.84, 95 per cent c.i. 0.77 to 0.94; P = 0.001).

CONCLUSION

This study has shown that postoperative chemotherapy improves overall survival in patients with OGAC treated with preoperative chemotherapy and surgery.

Artificial intelligence in thoracic surgery: a narrative review

Objective

The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction.

Background

Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway.

Methods

We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021.

Conclusion

Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decision-making process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice.

Keywords

Artificial intelligence (AI); thoracic surgery; machine learning; lung resection; perioperative medicine

Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review

PURPOSE

An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery.

METHODS

Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed.

RESULTS

Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM.

CONCLUSIONS

A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.

A Review on the Current Applications of Artificial Intelligence in the Operating Room

Background. Artificial intelligence (AI) is an era upcoming in medicine and, more recently, in the operating room (OR). Existing literature elaborates mainly on the future possibilities and expectations for AI in surgery. The aim of this study is to systematically provide an overview of the current actual AI applications used to support processes inside the OR. Methods. PubMed, Embase, Cochrane Library, and IEEE Xplore were searched using inclusion criteria for relevant articles up to August 25th, 2020. No study types were excluded beforehand. Articles describing current AI applications for surgical purposes inside the OR were reviewed. Results. Nine studies were included. An overview of the researched and described applications of AI in the OR is provided, including procedure duration prediction, gesture recognition, intraoperative cancer detection, intraoperative video analysis, workflow recognition, an endoscopic guidance system, knot-tying, and automatic registration and tracking of the bone in orthopedic surgery. These technologies are compared to their, often non-AI, baseline alternatives. Conclusions. Currently described applications of AI in the OR are limited to date. They may, however, have a promising future in improving surgical precision, reduce manpower, support intraoperative decision-making, and increase surgical safety. Nonetheless, the application and implementation of AI inside the OR still has several challenges to overcome. Clear regulatory, organizational, and clinical conditions are imperative for AI to redeem its promise. Future research on use of AI in the OR should therefore focus on clinical validation of AI applications, the legal and ethical considerations, and on evaluation of implementation trajectory.

Machine learning in gastrointestinal surgery

Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.

Machine learning-based approach for disease severity classification of carpal tunnel syndrome

Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.

Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.

The Prognostic Value of the Lymph Node in Oesophageal Adenocarcinoma; Incorporating Clinicopathological and Immunological Profiling

Simple Summary

Oesophageal cancer rates are increasing rapidly with patients often presenting at an advanced stage. The current approach to treatment involves radiotherapy, chemotherapy, or combination chemoradiotherapy with surgery; however, only a fraction of these patients will achieve meaningful responses. Therefore, there is a need to better understand the tumour and lymph node microenvironments to inform future treatment strategies. This study measured immune markers including immune checkpoint expression in tumour and lymph node tissue in oesophageal cancer patients and patient clinical outcomes, including survival time, response to treatment, and adverse events. We report herein that nodal metastases is of equal prognostic importance to clinical tumour stage and tumour regression grade in OAC and we observed a more immunosuppressive microenvironment in the tumour compared with the lymph node.

Abstract

Response rates to the current gold standards of care for treating oesophageal adenocarcinoma (OAC) remain modest with 15–25% of patients achieving meaningful pathological responses, highlighting the need for novel therapeutic strategies. This study consists of immune, angiogenic, and inflammatory profiling of the tumour microenvironment (TME) and lymph node microenvironment (LNME) in OAC. The prognostic value of nodal involvement and clinicopathological features was compared using a retrospective cohort of OAC patients (n = 702). The expression of inhibitory immune checkpoints by T cells infiltrating tumour-draining lymph nodes (TDLNs) and tumour tissue post-chemo(radio)therapy at surgical resection was assessed by flow cytometry. Nodal metastases is of equal prognostic importance to clinical tumour stage and tumour regression grade (TRG) in OAC. The TME exhibited a greater immuno-suppressive phenotype than the LNME. Our data suggests that blockade of these checkpoints may have a therapeutic rationale for boosting response rates in OAC.

Variation in histopathological assessment and association with surgical quality indicators following oesophagectomy

BACKGROUND

Histopathological outcomes, such as lymph node yield and margin positivity, are used to benchmark and assess surgical centre quality, and are reported annually by the National Oesophago-Gastric Cancer Audit (NOGCA) in England and Wales. The variation in pathological specimen assessment and how this affects these outcomes is not known.

METHODS

A survey of practice was circulated to all tertiary oesophagogastric cancer centres across England and Wales. Questions captured demographic data, and information on how specimens were prepared and analysed. National performance data were retrieved from the NOGCA. Survey results were compared for tertiles of lymph node yield, and circumferential and longitudinal margins.

RESULTS

Survey responses were received from 32 of 37 units (86 per cent response rate), accounting for 93.1 per cent of the total oesophagectomy volume in England and Wales. Only 5 of 32 units met or exceeded current guidelines on specimen preparation according to the Royal College of Pathologists guidelines. There was wide variation in how centres defined positive (R1) margins, and how margins and lymph nodes were assessed. Centres with the highest nodal yield were more likely to use systematic fat blocking, and to re-examine specimens when the initial load was low. Systematic blocking of lesser curve fat resulted in significantly higher rates of patients with at least 15 lymph nodes examined (91.4 versus 86.5 per cent; P = 0.027).

CONCLUSION

Preparation and histopathological assessment of specimens varies significantly across institutions. This challenges the validity of currently used surgical quality metrics for oesophageal and other tumours.

The challenge of offering potentially curative treatment to patients with esophageal cancer and a history of liver transplantation: A literature review and case report

BACKGROUND

With survival after liver transplantation continuing to improve, effective and evidence-based treatment of malignancies in this patient group is needed as a matter of urgency. Treatment outcomes for esophageal cancer, a challenging malignancy to treat in otherwise fit and well patients, after liver transplant are rarely reported.

METHODS

A systematic literature review was performed according to the PRISMA guidance to identify studies reporting outcomes of radical esophageal cancer treatment in patients with liver transplant. Management strategies and oncological outcomes were compared with a case managed at our institution.

RESULTS

Six studies were identified for review, and the outcomes of 13 patients were collated. The most common indication for liver transplant was alcohol-related liver disease (62%), and the most common tumor type was adenocarcinoma (54%). Neoadjuvant chemotherapy was delivered safely in 23% of cases in the literature and in the case managed at our institution. The median time from liver transplant to esophagectomy was 46 months, and the majority of patients underwent an Ivor-Lewis esophagectomy. The median follow-up from esophagectomy was 17 months, with a pooled 1-year survival of 77% and recurrence rate of 38%. This was comparable with corresponding rates reported for nontransplanted patients.

CONCLUSION

This case report and systematic review demonstrates that radical treatment of esophageal cancer after liver transplantation is not only technically feasible when managed by expert multidisciplinary teams but that it also improves survival. Routine surveillance of liver transplant patients with evidence of Barrett's preoperatively should be considered and close involvement of appropriate specialists in individual treatment planning is vital to acceptable outcomes.

Outcomes and survival following neoadjuvant chemotherapy versus neoadjuvant chemoradiotherapy for cancer of the esophagus: Inverse propensity score weighted analysis

BACKGROUND

Esophageal cancer is increasingly common and carries a poor prognosis. The optimal treatment modality for locally advanced cancer is unknown, with current guidance recommending either neoadjuvant chemotherapy (CT) or chemoradiotherapy (CRT) followed by surgery. There is a lack of adequately powered trials comparing CT against CRT. We retrospectively compared CT versus CRT using a propensity score weighting approach.

METHODS

Demographic, disease, treatment and outcome data were retrieved from a local database for patients who received neoadjuvant CT or CRT followed by surgery. Inverse probability of treatment weighting (IPTW) was used to balance groups using a propensity score-weighting approach. Groups were assessed for differences in postoperative outcomes and survival. Kaplan-Meier and non-parametric tests were used to compare survival and outcome data as appropriate.

RESULTS

Data for 284 patients were retrieved. Following IPTW groups were well matched. No significant differences were seen for postoperative complications (CT 64.9% vs. CRT 63.3%, p = 0.807), including major complications (24.0% vs. 23.6%, p = 0.943) and anastomotic leak (7.8% vs. 5.6%, p = 0.526). Significantly higher rates of clinical regression and complete pathological response were seen following CRT (p = 0.002 for both). Rates of R0 resection were higher with CRT, CT 79.1% vs. CRT 93.1%, p = 0.006. There was no difference between groups for overall or disease-free survival.

CONCLUSION

This study suggests that the significant improvements in local tumour response seen after neoadjuvant CRT compared to CT may not translate to different survival outcomes. However, it must be stressed that adequately powered prospective trials are still lacking.