Management of clinical T2N0 esophageal cancer: a review

Insights from explainable AI in oesophageal cancer team decisions

BACKGROUND

Clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this decision-making. We applied global XAI techniques to examine the impact of key clinical decision-drivers when mapped by a machine learning (ML) model, on the likelihood of receiving different oesophageal cancer (OC) treatment modalities by the multidisciplinary team (MDT).

METHODS

Retrospective analysis of 893 OC patients managed between 2010 and 2022 at our tertiary unit, used a random forests (RF) classifier to predict four possible treatment pathways as determined by the MDT: neoadjuvant chemotherapy followed by surgery (NACT + S), neoadjuvant chemoradiotherapy followed by surgery (NACRT + S), surgery-alone, and palliative management. Variable importance and partial dependence (PD) analyses then examined the influence of targeted high-ranking clinical variables within the ML model on treatment decisions as a surrogate model of the MDT decision-making dynamic.

RESULTS

Amongst guideline-variables known to determine treatments, such as Tumour-Node-Metastasis (TNM) staging, age also proved highly important to the RF model (16.1 % of total importance) on variable importance analysis. PD subsequently revealed that predicted probabilities for all treatment modalities change significantly after 75 years (p < 0.001). Likelihood of surgery-alone and palliative therapies increased for patients aged 75-85yrs but lowered for NACT/NACRT. Performance status divided patients into two clusters which influenced all predicted outcomes in conjunction with age.

CONCLUSION

XAI techniques delineate the relationship between clinical factors and OC treatment decisions. These techniques identify advanced age as heavily influencing decisions based on our model with a greater role in patients with specific tumour characteristics. This study methodology provides the means for exploring conscious/subconscious bias and interrogating inconsistencies in team-based decision-making within the era of AI-driven decision support.

Artificial Intelligence-Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads

Early detection of esophageal neoplasia via evaluation of endoscopic surveillance biopsies is the key to maximizing survival for patients with Barrett's esophagus, but it is hampered by the sampling limitations of conventional slide-based histopathology. Comprehensive evaluation of whole biopsies with 3-dimensional (3D) pathology may improve early detection of malignancies, but large 3D pathology data sets are tedious for pathologists to analyze. Here, we present a deep learning-based method to automatically identify the most critical 2-dimensional (2D) image sections within 3D pathology data sets for pathologists to review. Our method first generates a 3D heatmap of neoplastic risk for each biopsy, then classifies all 2D image sections within the 3D data set in order of neoplastic risk. In a clinical validation study, we diagnose esophageal biopsies with artificial intelligence-triaged 3D pathology (3 images per biopsy) vs standard slide-based histopathology (16 images per biopsy) and show that our method improves detection sensitivity while reducing pathologist workloads.

Metachronous Squamous Cell Carcinoma of the Esophagus After Resolution of Previous Adenocarcinoma

Esophageal cancer is the sixth leading cause of cancer-related death worldwide. Metachronous malignancies refer to multiple independent primary cancers diagnosed at least 6 months apart. The incidence of metachronous esophageal cancers with different histologic subtypes is extremely rare. This case presents an unprecedented occurrence of esophageal adenocarcinoma, followed by metachronous squamous cell carcinoma.

Histological prognosticators in neoadjuvant naive oesophageal cancer patients

PURPOSE

Prognosis of oesophageal cancer is primarily based upon the TNM stage of the disease. However, even in those with similar TNM staging, survival can be varied. Additional histopathological factors including venous invasion (VI), lymphatic invasion (LI) and perineural invasion (PNI) have been identified as prognostic markers yet are not part of TNM classification. The aim of this study is to determine the prognostic importance of these factors and overall survival in patients with oesophageal or junctional cancer who underwent transthoracic oesophagectomy as the unimodality treatment.

METHODS

Data from patients who underwent transthoracic oesophagectomy for adenocarcinoma without neoadjuvant treatment were reviewed. Patients were treated with radical resection, with a curative intent using a transthoracic Ivor Lewis or three staged McKeown approach.

RESULTS

A total of 172 patients were included. Survival was poorer when VI, LI and PNI were present (p<0.001), with the estimated survival being significantly worse (p<0.001) when patients were stratified according to the number of factors present. Univariable analysis of factors revealed VI, LI and PNI were all associated with survival. Presence of LI was independently predictive of incorrect staging/upstaging in multivariable logistic regression analysis (OR 12.9 95% CI 3.6-46.6, p<0.001).

CONCLUSION

Histological factors of VI, LI and PNI are markers of aggressive disease and may have a role in prognostication and decision-making prior to treatment. The presence of LI as an independent marker of upstaging could be a potential indication for the use of neoadjuvant treatment in patients with early clinical disease.

Demographic, endoscopic and histological profile of esophageal cancer at the Gastroenterology Department of Maputo Central Hospital from January 2016 to December 2018

INTRODUCTION

esophageal cancer is a major public health problem in Mozambique. It is the nineth most common cancer worldwide in terms of incidence (604.000 new cases/year), and sixth in overall mortality (544.076 deaths/year). In Mozambique esophageal cancer was the seventh most common cancer in males and the fifth in females between 1991 and 2008.

METHODS

it was done a cross-sectional hospital-based epidemiological study, using secondary demographics endoscopic and pathologic features data. A retrospective analysis of the existing information of patients classified as esophageal cancer diagnosed with upper gastrointestinal endoscopy observed from January 1^st, 2016 to December 31^st, 2018 at the Gastroenterology Service of Maputo Central Hospital. A coding sheet was created a priori, and data analysed in SPSS version 20.

RESULTS

of the 205 cases with complete records where included in the analysis, there was a higher frequency of females with 56.6% (116/205). The average age was 59.5 years with standard deviation of ± 12.9 years. Most of the patients were native of southern Mozambique, with 92.7% (190/205), of which Maputo made up 53.2% (109/205). Regarding race, 99.5% (204/205) were black. The most affected endoscopic location was the middle third with 48.8% (100/205), followed by the lower third with 29.8% (61/205) and the upper third with 21.5% (44/205). Squamous cell carcinoma was the most frequent, with 92.7% (190/205), followed by adenocarcinoma with 4.9% (10/205).

CONCLUSION

due to the high number of observed cases of esophageal cancer, a high degree of clinical suspicion is needed for timely diagnosis and more effective treatment. Updated prevalent studies are needed throughout the country to understand the true impact of esophageal cancer on the Mozambican population.

Trimodality Approach for Esophageal Malignancies

Trimodality therapy, or the use of concurrent chemoradiation followed by surgery, is the cornerstone of contemporary management of esophageal cancer. This article discusses the landmark trials and most current data to understand the concepts, applications, and outcomes from trimodality therapy in locally advanced esophageal cancer.

Staging esophageal cancer: low EUS accuracy in t2n0 patients

Background and study aims  Esophageal cancer (EC) is one of the most lethal malignancies worldwide. Staging of EC is performed with computed tomography (CT), positron-emission tomography (PET), and endoscopic ultrasonography (EUS). Patient management mostly depends on lymph node status. Compared to histopathology, the accuracy of EUS for T and N parameters is about 85 % and 75 %, respectively. Errors in staging may change prognosis. The aim of this study was to assess the role of EUS in T2-N0 EC considering the experience of two high-volume digestive endoscopic centers. Methods  Two prospectively collected databases were queried to identify all patients with EC, staged as cT2N0 by EUS, with no distant metastases at CT/PET scan and who underwent transthoracic esophagectomy. Preoperative EUS staging (cTNM) was compared to histopathology of the surgical specimen (pTNM) to evaluate accuracy. Results  Of 729 consecutive patients with EC between January 2011 and September 2018, 72 (49 men) had cT2N0 disease. CT and PET scans confirmed the absence of distant metastasis. In 43 of 72 patients (60 %), the evaluation was correct, 23 of 72 (31,7 %) were understaged, and six of 72 patients (8,3 %) were overstaged. Among the understaged patients, eight were understaged by tumor depth (35 %), seven by nodal involvement (30 %), and eight by both (35 %). All six patients who were overstaged had T1b-N0 disease. EUS accuracy was 77 % in staging for tumor depth and 82 % in staging for nodal metastases. The positive predictive value (PPV) for cT2N0 EC was 60 % (43 pT2N0 /72 cT2N). Conclusions  The accuracy of EUS staging of T2N0 EC is low, with only 60 % of patients undergoing appropriate therapy based on histopathology.

Transcription/Expression of KLRB1 Gene as A Prognostic Indicator in Human Esophageal Squamous Cell Carcinoma

AIM AND OBJECTIVE

Esophageal squamous cell carcinoma (ESCC) is the most prevalent type of cancer with worldwide distribution and dismal prognosis despite ongoing efforts to improve treatment options. Therefore, it is essential to determine the prognostic factors for ESCC.

METHODS AND RESULTS

We determined KLRB1 to be a prognostic indicator of human ESCC. KLRB1 was expressed at low levels in ESCC patients. Based on the risk score, patients were divided into high and low-risk groups. High-risk patients showed a poor survival rate. The prediction model based on the N stage, sex, and KLRB1 was significantly better than that based on the N stage and sex. The modified prediction model showed a robust ROC curve with an AUC value of 0.973. The knockdown of KLRB1 inhibited the growth of human ESCC cells. KLRB1 regulated Akt, mTOR, p27, p38, NF-κB, Cyclin D1, and JNK signaling, which was consistent with the result of GSEA.

CONCLUSION

KLRB1 is a potential prognostic marker for human ESCC patients.