Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature

Current Role of Artificial Intelligence in the Management of Esophageal Cancer

Background/Objectives: Esophageal cancer (EC) represents a major global contributor to cancer-related mortality. The advent of artificial intelligence (AI), including machine learning, deep learning, and radiomics, holds promise for enhancing treatment decisions and predicting outcomes. The aim of this review is to present an overview of the current landscape and future perspectives of AI in the management of EC. Methods: A literature search was performed on MEDLINE using the following keywords: "Artificial Intelligence", "Esophageal cancer", "Barrett's esophagus", "Esophageal Adenocarcinoma", and "Esophageal Squamous cell carcinoma". All titles and abstracts were screened; the results included 41 studies. Results: Over the past five years, the number of studies focusing on the application of AI to the treatment and prognosis of EC has surged, leveraging increasingly larger datasets with external validation. The simultaneous incorporation in AI models of clinical factors and features from several imaging modalities displays improved predictive performance, which may enhance patient outcomes, based on direct personalized therapeutic options. However, clinicians and researchers must address existing limitations, conduct randomized controlled trials, and consider the ethical and legal aspects that arise to establish AI as a standard decision-support tool. Conclusions: AI applications may result in substantial advances in EC management, heralding a new era. Considering the complexity of EC as a clinical entity, the evolving potential of AI is anticipated to ameliorate patients' quality of life and survival rates.

Comparative analysis of U-Mamba and no new U-Net for the detection and segmentation of esophageal cancer in contrast-enhanced computed tomography images

Background

Radiomics research in esophageal cancer (EC) has made considerable advancements. However, manual segmentation, which is relied upon in clinical and scientific workflows, remains time-consuming and inconsistent. This study aimed to develop and validate a deep learning (DL) model for the automatic detection and segmentation of EC lesions in contrast-enhanced computed tomography (CT) images.

Methods

We retrospectively collected the CT data of patients with EC confirmed by pathology from January 2017 to September 2021 at three hospitals and from individuals with a healthy esophagus. Manual labeling of EC lesions was conducted, and DL networks [no new U-Net (nnU-Net) and U-Mamba] were trained for automatic segmentation. An optimal threshold volume for EC lesion detection was determined and integrated into the postprocessing module. The performance of DL models was evaluated in internal, external, and thin-slice image test cohorts and compared with diagnoses by radiologists. The sensitivity, specificity, accuracy, Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated.

Results

A total of 871 patients (564 males) were included, with a median age of 67 years. DL models exhibited no significant difference from radiologists' diagnoses (P>0.05). Median DSC values for the internal, external, and thin-slice cohorts were 0.795, 0.811, and 0.797, respectively, with a corresponding HD of 9.733 mm, 7.860 mm, and 8.168 mm. An intraclass correlation coefficient greater than 0.7 was observed for 97.2% of the radiomic features extracted from thin-slice images.

Conclusions

The DL methods demonstrated exceptional sensitivity and robustness in EC detection and segmentation on contrast-enhanced CT images, not only reducing missed EC diagnoses but also providing radiologists with consistent lesion annotations.

Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers

One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endoscopic images, tissue samples, and medical histories of patients with gastrointestinal malignancies provide an enormous amount of medical data. There are encouraging signs that the advent of artificial intelligence could enhance the treatment of gastrointestinal issues with this data. Deep learning algorithms can swiftly and effectively analyze unstructured, high-dimensional data, including texts, images, and waveforms, while advanced machine learning approaches could reveal new insights into disease risk factors and phenotypes. In summary, artificial intelligence has the potential to revolutionize various features of gastrointestinal cancer care, such as early detection, diagnosis, therapy, and prognosis. This paper highlights some of the many potential applications of artificial intelligence in this domain. Additionally, we discuss the present state of the discipline and its potential future developments.

A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study

BACKGROUND

To develop and validate an online individualized model for predicting local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) treated by definitive chemoradiotherapy (dCRT).

METHODS

ESCC patients from three hospitals were randomly stratified into the training set (715) and the internal testing set (179), and patients from the other hospital as the external testing set (120). The important radiomic features extracted from contrast-enhanced computed tomography (CECT)-based subregions clustered from the whole volume of tumor and peritumor were selected and used to construct the subregion-based radiomic signature by using COX proportional hazards model, which was compared with the tumor-based radiomic signature. The clinical model and the radiomics model combing the clinical factors and the radiomic signature were further constructed and compared, which were validated in two testing sets.

RESULTS

The subresion-based radiomic signature showed better prognostic performance than the tumor-based radiomic signature (training: 0.642 vs. 0.621, internal testing: 0.657 vs. 0.638, external testing: 0.636 vs. 0.612). Although the tumor-based radiomic signature, the subregion-based radiomic signature, the tumor-based radiomics model, and the subregion-based radiomics model had better performance compared to the clinical model, only the subregion-based radiomics model showed a significant advantage (p < 0.05; training: 0.666 vs. 0.616, internal testing: 0.689 vs. 0.649, external testing: 0.642 vs. 0.604). The clinical model and the subregion-based radiomics model were visualized as the nomograms, which are available online and could interactively calculate LRFS probability.

CONCLUSIONS

We established and validated a CECT-based online radiomics nomogram for predicting LRFS in ESCC received dCRT, which outperformed the clinical model and might serve as a powerful tool to facilitate individualized treatment.

TRIAL REGISTRATION

This retrospective study was approved by the ethics committee (KY20222145-C-1).

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.

Contrast-enhanced CT radiomics combined with multiple machine learning algorithms for preoperative identification of lymph node metastasis in pancreatic ductal adenocarcinoma

Objectives

This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in the diagnosis of pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected to provide clinical treatment strategies.

Methods

A total of 128 patients with pathologically confirmed PDAC and who underwent surgical resection were randomized into training (n=93) and validation (n=35) groups. This study incorporated a total of 13 distinct machine learning algorithms and explored 85 unique combinations of these algorithms. The area under the curve (AUC) of each model was computed. The model with the highest mean AUC was selected as the best model which was selected to determine the radiomics score (Radscore). The clinical factors were examined by the univariate and multivariate analysis, which allowed for the identification of factors suitable for clinical modeling. The multivariate logistic regression was used to create a combined model using Radscore and clinical variables. The diagnostic performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

Results

Among the 233 models constructed using arterial phase (AP), venous phase (VP), and AP+VP radiomics features, the model built by applying AP+VP radiomics features and a combination of Lasso+Logistic algorithm had the highest mean AUC. A clinical model was eventually constructed using CA199 and tumor size. The combined model consisted of AP+VP-Radscore and two clinical factors that showed the best diagnostic efficiency in the training (AUC = 0.920) and validation (AUC = 0.866) cohorts. Regarding preoperative diagnosis of LN metastasis, the calibration curve and DCA demonstrated that the combined model had a good consistency and greatest net benefit.

Conclusions

Combining radiomics and machine learning algorithms demonstrated the potential for identifying the LN metastasis of PDAC. As a non-invasive and efficient preoperative prediction tool, it can be beneficial for decision-making in clinical practice.

Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer

BACKGROUND

Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning.

METHODS

A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS).

RESULTS

Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %-89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927).

CONCLUSION

Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.

Radiomics in esophagogastric junction cancer: A scoping review of current status and advances

PURPOSE

This scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer.

METHODS

We conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively.

RESULTS

A total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%).

CONCLUSION

Although there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.

Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Objective

Invasive lung adenocarcinoma（ILA） with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution（SR） enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning.

Methods

Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC).

Result

A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility.

Conclusion

The study successfully constructed and evaluated a deep learning（DL）-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.

Multi-disciplinary management of esophageal carcinoma: Current practices and future directions

Esophageal cancer in one of the most malignant and hard-to-treat cancers. Esophageal squamous carcinoma (ESCC) is most common in Asian countries, whereas adenocarcinoma at the esophago-gastric junction (EGJ AC) is more prevalent in the Western countries. Due to differences in both genetic background and response to chemotherapy and radiotherapy, both histologic subtypes need different paradigms of management. Since the landmark CROSS study has demonstrated the superior survival benefit of tri-modality including neoadjuvant chemoradiotherapy prior to esophagectomy, the tri-modality becomes the standard of care; however, it is suitable for a highly-selected patient. Tri-modality should be offered for every ESCC patient, if a patient is fit for surgery with adequate cardiopulmonary reserve, regardless of ages. Definitive chemoradiotherapy remains the best option for a patient who is not a surgical candidate or declines surgery. On the contrary, owing to doubtful benefits of radiotherapy with potentially more toxicities related to radiotherapy in EGJ AC, either neoadjuvant chemotherapy or peri-operative chemotherapy would be more preferable in an EGJ AC patient. In case of very locally advanced disease (cT4b), the proper management is more challenging. Even though, palliative care is the safe option, multi-modality therapy with curative intent like neoadjuvant chemotherapy with conversion surgery may be worthwhile; however, it should be suggested on case-by-case basis.

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

Objective

We aimed to evaluate the diagnostic effectiveness of computed tomography (CT)-based radiomics for predicting lymph node metastasis (LNM) in patients diagnosed with esophageal cancer (EC).

Methods

The present study conducted a comprehensive search by accessing the following databases: PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying relevant studies published until July 10th, 2023. The diagnostic accuracy was summarized using the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). The researchers utilized Spearman's correlation coefficient for assessing the threshold effect, besides performing meta-regression and subgroup analysis for the exploration of possible heterogeneity sources. The quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score (RQS).

Results

The meta-analysis included six studies conducted from 2018 to 2022, with 483 patients enrolled and LNM rates ranging from 27.2% to 59.4%. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC, along with their corresponding 95% CI, were 0.73 (0.67, 0.79), 0.76 (0.69, 0.83), 3.1 (2.3, 4.2), 0.35 (0.28, 0.44), 9 (6, 14), and 0.78 (0.74, 0.81), respectively. The results demonstrated the absence of significant heterogeneity in sensitivity, while significant heterogeneity was observed in specificity; no threshold effect was detected. The observed heterogeneity in the specificity was attributed to the sample size and CT-scan phases (P < 0.05). The included studies exhibited suboptimal quality, with RQS ranging from 14 to 16 out of 36. However, most of the enrolled studies exhibited a low-risk bias and minimal concerns relating to applicability.

Conclusion

The present meta-analysis indicated that CT-based radiomics demonstrated a favorable diagnostic performance in predicting LNM in EC. Nevertheless, additional high-quality, large-scale, and multicenter trials are warranted to corroborate these findings.

Systematic Review Registration

Open Science Framework platform at https://osf.io/5zcnd.

Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model

OBJECTIVES

To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features.

METHODS

The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers.

RESULTS

A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers.

CONCLUSION

The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients.

CLINICAL RELEVANCE STATEMENT

The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed.

KEY POINTS

• Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.

Artificial intelligence in oral health surveillance among under-served communities

A sizable percentage of the population in India still does not have easy access to dental facilities. Therefore, it is of interest to document the role of artificial intelligence (AI) in oral surveillance of underserved communities. Available data shows that AI makes it possible to screen, diagnose, track, prioritize, and monitor dental patients remotely via smart devices. As a result, dentists won't have to deal with simple situations that only require standard treatments; freeing them up to focus on more complicated cases. Additionally, this would allow dentists to reach a broader, more underprivileged population in difficult-to-reach places. AI fracture recognition and categorization performance has shown promise in preliminary testing. Methods for detecting aberrations are frequently employed in public health practise and research continues to be focused on them.

Machine learning applications for early detection of esophageal cancer: a systematic review

INTRODUCTION

Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC.

METHODS

We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review.

RESULTS

The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods.

CONCLUSION

Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study

BACKGROUND

Oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction are among the most common malignant epithelial tumours. Most patients receive neoadjuvant therapy before complete tumour resection. Histological assessment after resection includes identification of residual tumour tissue and areas of regressive tumour, data which are used to calculate a clinically relevant regression score. We developed an artificial intelligence (AI) algorithm for tumour tissue detection and tumour regression grading in surgical specimens from patients with oesophageal adenocarcinoma or adenocarcinoma of the oesophagogastric junction.

METHODS

We used one training cohort and four independent test cohorts to develop, train, and validate a deep learning tool. The material consisted of histological slides from surgically resected specimens from patients with oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction from three pathology institutes (two in Germany, one in Austria) and oesophageal cancer cohort of The Cancer Genome Atlas (TCGA). All slides were from neoadjuvantly treated patients except for those from the TCGA cohort, who were neoadjuvant-therapy naive. Data from training cohort and test cohort cases were extensively manually annotated for 11 tissue classes. A convolutional neural network was trained on the data using a supervised principle. First, the tool was formally validated using manually annotated test datasets. Next, tumour regression grading was assessed in a retrospective cohort of post-neoadjuvant therapy surgical specimens. The grading of the algorithm was compared with that of a group of 12 board-certified pathologists from one department. To further validate the tool, three pathologists processed whole resection cases with and without AI assistance.

FINDINGS

Of the four test cohorts, one included 22 manually annotated histological slides (n=20 patients), one included 62 sides (n=15), one included 214 slides (n=69), and the final one included 22 manually annotated histological slides (n=22). In the independent test cohorts the AI tool had high patch-level accuracy for identifying both tumour and regression tissue. When we validated the concordance of the AI tool against analyses by a group of pathologists (n=12), agreement was 63·6% (quadratic kappa 0·749; p<0·0001) at case level. The AI-based regression grading triggered true reclassification of resected tumour slides in seven cases (including six cases who had small tumour regions that were initially missed by pathologists). Use of the AI tool by three pathologists increased interobserver agreement and substantially reduced diagnostic time per case compared with working without AI assistance.

INTERPRETATION

Use of our AI tool in the diagnostics of oesophageal adenocarcinoma resection specimens by pathologists increased diagnostic accuracy, interobserver concordance, and significantly reduced assessment time. Prospective validation of the tool is required.

FUNDING

North Rhine-Westphalia state, Federal Ministry of Education and Research of Germany, and the Wilhelm Sander Foundation.

The Oesophageal Cancer Multidisciplinary Team: Can Machine Learning Assist Decision-Making?

BACKGROUND

The complexity of the upper gastrointestinal (UGI) multidisciplinary team (MDT) is continually growing, leading to rising clinician workload, time pressures, and demands. This increases heterogeneity or 'noise' within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In recent decades, the application of artificial intelligence (AI) and more specifically the branch of machine learning (ML) has led to a paradigm shift in the perceived utility of statistical modelling within healthcare. Within oesophageal cancer (OC) care, ML techniques have already been applied with early success to the analyses of histological samples and radiology imaging; however, it has not yet been applied to the MDT itself where such models are likely to benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy.

METHODS

This review discusses the current role the MDT plays in modern UGI cancer care as well as the utilisation of ML techniques to date using histological and radiological data to predict treatment response, prognostication, nodal disease evaluation, and even resectability within OC.

RESULTS

The review finds that an emerging body of evidence is growing in support of ML tools within multiple domains relevant to decision-making within OC including automated histological analysis and radiomics. However, to date, no specific application has been directed to the MDT itself which routinely assimilates this information.

CONCLUSIONS

The authors feel the UGI MDT offers an information-rich, diverse array of data from which ML offers the potential to standardise, automate, and produce more consistent, data-driven MDT decisions.

Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network

Objectives

Recurrence risk evaluation is clinically significant for patients with locally advanced cervical cancer (LACC). We investigated the ability of transformer network in recurrence risk stratification of LACC based on computed tomography (CT) and magnetic resonance (MR) images.

Methods

A total of 104 patients with pathologically diagnosed LACC between July 2017 and December 2021 were enrolled in this study. All patients underwent CT and MR scanning, and their recurrence status was identified by the biopsy. We randomly divided patients into training cohort (48 cases, non-recurrence: recurrence = 37: 11), validation cohort (21 cases, non-recurrence: recurrence = 16: 5), and testing cohort (35 cases, non-recurrence: recurrence = 27: 8), upon which we extracted 1989, 882 and 315 patches for model's development, validation and evaluation, respectively. The transformer network consisted of three modality fusion modules to extract multi-modality and multi-scale information, and a fully-connected module to perform recurrence risk prediction. The model's prediction performance was assessed by six metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, f1-score, sensitivity, specificity and precision. Univariate analysis with F-test and T-test were conducted for statistical analysis.

Results

The proposed transformer network is superior to conventional radiomics methods and other deep learning networks in both training, validation and testing cohorts. Particularly, in testing cohort, the transformer network achieved the highest AUC of 0.819 ± 0.038, while four conventional radiomics methods and two deep learning networks got the AUCs of 0.680 ± 0.050, 0.720 ± 0.068, 0.777 ± 0.048, 0.691 ± 0.103, 0.743 ± 0.022 and 0.733 ± 0.027, respectively.

Conclusions

The multi-modality transformer network showed promising performance in recurrence risk stratification of LACC and may be used as an effective tool to help clinicians make clinical decisions.

A review of radiomics and genomics applications in cancers: the way towards precision medicine

The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field.

A combined predicting model for benign esophageal stenosis after simultaneous integrated boost in esophageal squamous cell carcinoma patients (GASTO1072)

Purpose

We aimed to develop a combined predicting model for benign esophageal stenosis (BES) after simultaneous integrated boost (SIB) with concurrent chemotherapy in patients with esophageal squamous cell carcinoma (ESCC).

Methods

This study included 65 patients with EC who underwent SIB with chemotherapy. Esophageal stenosis was evaluated using esophagograms and the severity of eating disorders. Risk factors were investigated using univariate and multivariate analyses. Radiomics features were extracted based on contrast-enhanced CT (CE-CT) before treatment. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for feature selection and radiomics signature construction. The model’s performance was evaluated using Harrell’s concordance index and receiver operating characteristic curves.

Results

The patients were stratified into low- and high-risk groups according to BES after SIB. The area under the curves of the clinical model, Rad-score, and the combined model were 0.751, 0.820 and 0.864, respectively. In the validation cohort, the AUCs of these three models were 0.854, 0.883 and 0.917, respectively. The Hosmer-Lemeshow test showed that there was no deviation from model fitting for the training cohort (p=0.451) and validation cohort (p=0.481). The C-indexes of the nomogram were 0.864 and 0.958 for the training and validation cohort, respectively. The model combined with Rad-score and clinical factors achieved favorable prediction ability.

Conclusion

Definitive chemoradiotherapy could alleviate tumor-inducing esophageal stenosis but result in benign stenosis. We constructed and tested a combined predicting model for benign esophageal stenosis after SIB. The nomogram incorporating both radiomics signature and clinical prognostic factors showed favorable predictive accuracy for BES in ESCC patients who received SIB with chemotherapy.

Trial registration number and date of registration

Registered in www.Clinicaltrial.gov, ID: NCT01670409, August 12, 2012

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

BACKGROUND

Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality.

METHODS

A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist.

RESULTS

The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews.

CONCLUSION

Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.

Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate. Automated recognition of EC can aid physicians in improving diagnostic performance and accuracy. However, the classification of EC is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness. The recent developments of deep learning (DL) and computer-aided diagnosis (CAD) models have been useful for designing accurate EC classification models. In this aspect, this study develops an atom search optimization with a deep transfer learning-driven EC classification (ASODTL-ECC) model. The presented ASODTL-ECC model mainly examines the medical images for the existence of EC in a timely and accurate manner. To do so, the presented ASODTL-ECC model employs Gaussian filtering (GF) as a preprocessing stage to enhance image quality. In addition, the deep convolution neural network- (DCNN-) based residual network (ResNet) model is applied as a feature extraction approach. Besides, ASO with an extreme learning machine (ELM) model is utilized for identifying the presence of EC, showing the novelty of the work. The performance of the ASODTL-ECC model is assessed and compared with existing models under several medical images. The experimental results pointed out the improved performance of the ASODTL-ECC model over recent approaches.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

PURPOSE

Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines.

METHODS

A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality.

RESULTS

Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings.

CONCLUSION

A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.