Background/Objectives: Even with today's advancements, cancer still represents a major cause of mortality worldwide. One important aspect of cancer progression that has a big impact on diagnosis, prognosis, and treatment plans is accurate lymph node metastasis evaluation. However, regardless of the imaging method used, this process is challenging and time-consuming. This research aimed to develop and validate an automatic detection and segmentation system for superficial lymph node evaluation based on multimodal ultrasound images, such as traditional B-mode, Doppler, and elastography, using deep learning techniques. Methods: The suggested approach incorporated a Mask R-CNN architecture designed specifically for the detection and segmentation of lymph nodes. The pipeline first involved noise reduction preprocessing, after which morphological and textural feature segmentation and analysis were performed. Vascularity and stiffness parameters were further examined in Doppler and elastography pictures. Metrics, including accuracy, mean average precision (mAP), and dice coefficient, were used to assess the system's performance during training and validation on a carefully selected dataset of annotated ultrasound pictures. Results: During testing, the Mask R-CNN model showed an accuracy of 92.56%, a COCO AP score of 60.7 and a validation score of 64. Furter on, to improve diagnostic capabilities, Doppler and elastography data were added. This allowed for improved performance across several types of ultrasound images and provided thorough insights into the morphology, vascularity, and stiffness of lymph nodes. Conclusions: This paper offers a novel use of deep learning for automated lymph node assessment in ultrasound imaging. This system offers a dependable tool for doctors to evaluate lymph node metastases efficiently by fusing sophisticated segmentation techniques with multimodal image processing. It has the potential to greatly enhance patient outcomes and diagnostic accuracy.

To assess the performance of novel qualitative diagnostic criteria using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to identify the pathologic complete response (pCR) of primary tumors in esophageal cancer after neoadjuvant chemoradiation (nCRT).

Patients who underwent nCRT, subsequent MRI, positron emission tomography/computed tomography (PET/CT), endoscopy, or esophagectomy for esophageal cancer between October 2021 and October 2023 were retrospectively analyzed. The DCE-MRI response of primary tumors was interpreted using five grades by thoracic radiologists as follows: G1 (compatible with CR), G2 (probable CR), G3 (probable partial response [PR]), G4 (compatible with PR), and G5 (stable or progressive disease). The performances of MRI, PET/CT, endoscopy, and their combinations in diagnosing pCR in primary tumors were calculated.

A total of 52 patients (male:female, 46:6; age, 61.2 ± 8.0 years) were included. Surgical specimens revealed pCR (ypT0) in 34 patients. G1 as the MRI criterion for pCR of primary tumors yielded a positive predictive value (PPV), specificity of 100% (18/18), and low sensitivity (23.5% [8/34]). Combining G1 and G2 as the MRI criteria increased the sensitivity to 73.5% (25/34), with a specificity of 88.9% (16/18), accuracy of 78.8% (41/52), and PPV of 92.6% (25/27). Adding the DCE-MRI results (G1-2) significantly improved accuracy for both PET/CT (from 65.4% [34/52] to 80.8% [42/52], P = 0.03) and endoscopy (from 55.8% [29/52] to 76.9% [40/52], P = 0.005), with increase in sensitivity (from 55.9% [19/34] to 82.4% [28/34] for PET/CT-based evaluation [P = 0.008] and from 47.1% [16/34] to 82.4% [28/34] for endoscopy-based evaluation [P = 0.001]).

DCE-MRI-based grading shows high diagnostic performance for identifying pCR in primary tumors, particularly in terms of PPV and specificity, and enhances response evaluation when combined with PET/CT and endoscopy.

Solute carrier family 25 member 1 (SLC25A1) affects lipid metabolism and energy regulation in multiple types of tumor cell, affecting their proliferation and survival. To the best of our knowledge, however, the impact of SLC25A1 on the proliferation and survival of esophageal squamous cell carcinoma (ESCC) cells has yet to be explored. Here, SLC25A1 expression was detected in ESCC tissues and cell lines. SLC25A1 was silenced or blocked by lentivirus transfection or 2‑[(4‑chloro‑3‑nitrophenyl)sulfonylamino]benzoic acid in ESCC cells. To evaluate the impact of SLC25A1 on in vivo and in vitro proliferation, invasion and migration of ESCC cells, Cell Counting‑Kit, wound healing, colony formation, Transwell, EdU, flow cytometry, tumor xenograft in nude mice, lipid metabolism and energy metabolism detection assays were performed. Reverse transcription‑quantitative PCR and western blot analysis were performed to determine expression of downstream molecules and pathway proteins following the silencing and blockade of SLC25A1. SLC25A1 was significantly overexpressed in ESCC tissue and cell lines. The targeted silencing of SLC25A1 or inhibition of its protein led to a significant decrease in proliferative, invasive and migratory capabilities of ESCC cells, accompanied by increased apoptosis. Additionally, silencing of the SLC25A1 gene significantly inhibited xenograft tumor growth in vivo. The present results indicate that knockdown or blockade of SLC25A1 can significantly impede the malignant biological behavior of ESCC.

Neoadjuvant chemoimmunotherapy (NCIT) has improved pathological complete response and conferred survival benefits in patients with locally advanced esophageal cancer. However, surgical complications unrelated to the tumor continue to detract from patient outcomes. While the "watch-and-wait" strategy has been implemented in clinical complete responders following neoadjuvant therapy for rectal cancer, there is a lack of evidence supporting its practicability in esophageal cancer after NCIT. This pilot case series involves six clinical complete responders who deferred surgery under close surveillance after three or four cycles of neoadjuvant camrelizumab plus chemotherapy and who subsequently received camrelizumab as maintenance treatment. The primary observation measure of the series is event-free survival (EFS). Routine follow-up examinations included endoscopy, biopsy, contrast-enhanced computed tomography, and ultrasonography every 3-6 months. For patients who experienced local recurrence without metastasis, the salvage operation was the priority recommendation. As of September 5, 2024, the average follow-up duration was 124.4 weeks, with the average EFS reaching 134.7 weeks. No deaths or distant metastases were observed. Our findings suggest that responders to NCIT may be spared from esophagectomy. On the prerequisite of sufficient tumor regression during neoadjuvant cycles, immunotherapy may facilitate the continued eradication of residual disease in this series.

Disulfidptosis is a newly discovered type of cell death that differs from apoptosis, necrosis, ferroptosis and other death modes and is closely related to the occurrence and progression of tumors. However, the predictive potential and biological characteristics of disulfidptosis-related lncRNAs (DRGs-lncRNAs) in esophageal squamous cell carcinoma (ESCC) are unclear.

RNA transcriptome data, clinical information and mutation data for ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation and Cox regression analyses were used to identify the DRGs-lncRNAs associated with overall survival (OS). LASSO regression analysis was used to construct the prognostic model. A nomogram was created to predict the prognosis of patients with ESCC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the signaling pathways associated with the model. TIMER, CIBERSORT, ESTIMATE and other methods were used to analyze immune infiltration, immune function, immune checkpoints and drug sensitivity. The tumor mutation burden (TMB) were assessed between different risk groups. Real-time polymerase chain reaction (RT‒PCR) was used to detect the expression of DRGs-lncRNAs in ESCC cell lines.

A total of 155 lncRNAs significantly associated with disulfidptosis were identified. Through univariate Cox regression analysis, LASSO regression analysis and multivariate Cox regression analysis, 9 lncRNAs with independent prognostic significance were selected, and a prognosis model was established. Survival analysis with the prognostic model revealed that there were obvious differences in survival between the high- and low-risk groups. Further analysis revealed that the immune microenvironment, immune infiltration, immune function, immune checkpoints, and drug sensitivity significantly differed between the high-risk and low-risk groups. Patients who exhibited both high risk and high tumor mutation burden (TMB) survived shorter, while those who fell into the low risk and low TMB categories survived longer. In addition, RT‒PCR analysis revealed differential expression of DRG lncRNAs between ESCC cell lines and esophageal epithelial cell lines.

We established a DRG-lncRNA prognostic model that can be used to predict the prognosis, tumor mutation burden, immune cell infiltration, and drug sensitivity of ECSS patients. The results of this study provide valuable insights into the understanding of ESCC and provide valuable assistance for the individualized treatment of ESCC patients.

Oesophageal cancer is the seventh leading cause of cancer mortality worldwide. Two major pathological subtypes exist: oesophageal squamous cell carcinoma and oesophageal adenocarcinoma. Epidemiological studies in the last decade have shown a gradual increase in the incidence of oesophageal adenocarcinoma worldwide. The prognosis of oesophageal cancer has greatly improved due to breakthroughs in screening, surgical procedures, and novel treatment modalities. The success achieved with combined modality therapies, including surgery, chemotherapy, and radiotherapy, to treat locally advanced oesophageal cancer is particularly notable. Immunotherapy has become a crucial treatment for oesophageal cancer, with immune checkpoint inhibitor-based therapies now established as the standard of care in adjuvant and metastatic first-line settings. This Seminar provides an overview of advances in the screening, diagnosis, and treatment of oesophageal squamous cell carcinoma and oesophageal adenocarcinoma, with a particular focus on neoadjuvant therapies for locally advanced oesophageal cancer and immune checkpoint inhibitor-based therapies.

This study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).

Patients with ESCC undergoing nCRT followed by surgery were retrospectively enrolled from three institutions and divided into training and testing cohorts. Both traditional and deep-learning radiomics features were extracted from pre-treatment CT, T2, and DWI. Multiple radiomics models were developed, both single modality and integrated, using machine learning algorithms. The models' performance was assessed using receiver operating characteristic curve analysis, with the area under the curve (AUC) as a primary metric, alongside sensitivity and specificity from the cut-off analysis.

The study involved 151 patients, among whom 63 achieved pCR. The training cohort consisted of 89 patients from Institution 1 (median age 62, 73 males) and the testing cohort included 52 patients from Institution 2 (median age 62, 41 males), and 10 in a clinical trial from Institution 3 (median age 69, 9 males). The integrated model, combining traditional and deep learning radiomics features from CT, T2, and DWI, demonstrated the best performance with an AUC of 0.868 (95% CI: 0.766-0.959), sensitivity of 88% (95% CI: 73.9-100), and specificity of 78.4% (95% CI: 63.6-90.2) in the testing cohort. This model outperformed single-modality models and the clinical model.

A multimodality deep learning radiomics model, utilizing CT, T2, and DWI images, was developed and validated for accurately predicting pCR of ESCC following nCRT.

Our research demonstrates the satisfactory predictive value of multimodality deep learning radiomics for the response of nCRT in ESCC and provides a potentially helpful tool for personalized treatment including organ preservation strategy.

After neoadjuvant chemoradiotherapy, patients with ESCC have pCR rates of about 40%. The multimodality deep learning radiomics model, could predict pCR after nCRT with high accuracy. The multimodality radiomics can be helpful in personalized treatment of esophageal cancer.

Few predictive biomarkers exist for identifying patients who may benefit from neoadjuvant therapy (NAT). The intratumoral microbial composition is comprehensively profiled to predict the efficacy and prognosis of patients with esophageal squamous cell carcinoma (ESCC) who underwent NAT and curative esophagectomy. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis is conducted to screen for the most closely related microbiota and develop a microbiota-based risk prediction (MRP) model on the genera of TM7x, Sphingobacterium, and Prevotella. The predictive accuracy and prognostic value of the MRP model across multiple centers are validated. The MRP model demonstrates good predictive accuracy for therapeutic responses in the training, validation, and independent validation sets. The MRP model also predicts disease-free survival (p = 0.00074 in the internal validation set and p = 0.0017 in the independent validation set) and overall survival (p = 0.00023 in the internal validation set and p = 0.11 in the independent validation set) of patients. The MRP-plus model basing on MRP, tumor stage, and tumor size can also predict the patients who can benefit from NAT. In conclusion, the developed MRP and MRP-plus models may function as promising biomarkers and prognostic indicators accessible at the time of diagnosis.

Predicting the efficacy of neoadjuvant immunochemotherapy (NICT) for esophageal squamous cell carcinoma (ESSC) prior to surgery can minimize unnecessary surgical interventions and facilitate personalized treatment strategies. Our goal is to develop and validate an image-based radiomic model using preoperative computed tomography (CT) scans and clinical data to predict pathological complete response (pCR) in resectable ESSC following neoadjuvant immunotherapy.

We retrospectively collected data from patients diagnosed with ESCC at the First Affiliated Hospital of Soochow University between January 2018 and May 2023, who received preoperative neoadjuvant immunochemotherapy. Eligible patients were randomly divided into training and validation sets. Radiomic features extracted from preprocessed CT images were used to develop a radiomic model, incorporating Radiomic score (Rad-score) and clinical factors through multivariate logistic regression analysis. The model's performance was assessed for calibration, discrimination, and clinical utility in an independent validation cohort.

We enrolled a total of 105 eligible participants who were randomly divided into two groups: a training set (N=74) and a validation set (N=31). After data dimension reduction and feature selection, we identified 11 radiomic features, which collectively formed the Rad-score. Rad-score had an area under the curve (AUC) of 0.83 (95% CI 0.72-0.93) in the training set and 0.78 (95% CI 0.60-0.95) in the validation set. Multivariate analysis revealed that radiological response and Neutrophil-Lymphocyte Ratio (NLR) were independent predictors of pCR, with p-values of 0.0026 and 0.0414, respectively. We developed and validated a nomogram combining Rad-score and clinical features, achieving AUCs of 0.90 (95% CI 0.82-0.98) in the training set and 0.85 (95% CI 0.70-0.99) in the validation set. The Delong test confirmed the nomogram's superiority over pure radiomic and clinical models. Decision curve analysis (DCA) and integrated discrimination improvement (IDI) assessment supported the clinical value and superiority of the combined model.

The nomogram, which integrates Rad-score and clinical features, offers a precise and reliable method for predicting pCR status in ESCC patients who have undergone neoadjuvant immunochemotherapy. This tool aids in tailoring treatment strategies to individual patients.

The 25th Annual Western Canadian Gastrointestinal Cancer Consensus Conference (WCGCCC) was held in Winnipeg, Manitoba, on 26-27 October 2023. The WCGCCC is an interactive multidisciplinary conference that was attended by healthcare professionals from across Western Canada (British Columbia, Alberta, and Manitoba) who are involved in the care of patients with gastrointestinal cancer. Surgical, medical, and radiation oncologists; pathologists; oncology nurses; pharmacists; and a family physician in oncology (FPO) participated in presentation and discussion sessions for the purpose of developing the recommendations presented here. This consensus statement addresses current issues in the management of gastroesophageal cancers.

Neoadjuvant therapy in patients with locally advanced rectal cancer (LARC) has achieved good pathologic complete response (pCR) rates, potentially eliminating the need for surgical intervention. This study investigated preoperative methods for predicting pCR after neoadjuvant short-course radiotherapy (SCRT) combined with immunochemotherapy. Methods: Treatment-naïve patients with histologically confirmed LARC were enrolled from February 2023 to July 2023. Before surgery, the patients received neoadjuvant SCRT followed by 2 cycles of capecitabine and oxaliplatin plus camrelizumab. 68Ga-labeled fibroblast activation protein inhibitor ([68Ga]Ga-FAPI-04) PET/MRI, [18F]FDG PET/CT, and contrast-enhanced MRI were performed before treatment initiation and before surgery in each patient. PET and MRI features and the size and number of lesions were also collected from each scan. Each parameter's sensitivity, specificity, and diagnostic cutoff were derived via receiver-operating-characteristic curve analysis. Results: Twenty eligible patients (13 men, 7 women; mean age, 60.2 y) were enrolled and completed the entire trial, and all patients had proficient mismatch repair or microsatellite-stable LARC. A postoperative pCR was achieved in 9 patients (45.0%). In the visual evaluation, both [68Ga]Ga-FAPI-04 PET/MRI and [18F]FDG PET/CT were limited to forecasting pCR. Contrast-enhanced MRI had a low sensitivity of 55.56% to predict pCR. In the quantitative evaluation, [68Ga]Ga-FAPI-04 change in SULpeak percentage, where SULpeak is SUVpeak standardized by lean body mass, had the largest area under the curve (0.929) with high specificity (sensitivity, 77.78%; specificity, 100.0%; cutoff, 63.92%). Conclusion: [68Ga]Ga-FAPI-04 PET/MRI is a promising imaging modality for predicting pCR after SCRT combined with immunochemotherapy. The SULpeak decrease exceeding 63.92% may provide valuable guidance in selecting patients who can forgo surgery after neoadjuvant therapy.

Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.

This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps.

The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively.

By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value.

Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts.

Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion.

A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

To identify the most sensitive imaging examination method to evaluate the prognosis of esophageal squamous cell carcinoma (ESCC).

Thirty patients with esophageal squamous cell carcinoma (ESCC) participated in the study and underwent chemoradiotherapy (CRT). They were divided into two groups based on their survival status: the survival group and non-survival group. The diagnostic tests were utilized to determine the most effective imaging examination method for assessing the prognosis.

1. There were no significant differences in tumor length shown on esophagography or computed tomography (CT) or the maximal esophageal wall thickness shown on CT at the specified time points between the two groups. 2. The tumor length on diffusion-weighted imaging (DWI) in the survival group was significantly lower than in the non-survival group at the end of the sixth week of treatment (P=0.001). The area under the ROC curve was 0.840 (P=0.002), and the diagnostic efficiency was moderately accurate. 3. The apparent diffusion coefficient (ADC) values of the survival group were significantly higher than those in the non-survival group at the end of the fourth week and sixth week of treatment (both P<0.001). Areas under the curve were 0.866 and 0.970, with P values of 0.001 and <0.001 and good diagnostic accuracy. Cox regression analyses indicated the ADC at the end of the sixth week of treatment was an independent risk factor.

Compared with esophagography and CT, DW-MRI has certain advantages in predicting the prognosis of ESCC.

We aimed to determine whether monitoring tumor-derived exosomal microRNAs (miRNAs) could be used to assess radiotherapeutic sensitivity in patients with locally advanced esophageal squamous cell carcinoma (ESCC). RNA sequencing was employed to conduct a comparative analysis of miRNA expression levels during radiotherapy, focusing on identifying miRNAs associated with progression. Electron microscopy confirmed the existence of exosomes, and co-cultivation assays and immunofluorescence validated their capacity to infiltrate macrophages. To determine the mechanism by which exosomal miR-143-3p regulates the interplay between ESCC cells and M2 macrophages, ESCC cell-derived exosomes were co-cultured with macrophages. Serum miR-143-3p and miR-223-3p were elevated during radiotherapy, suggesting resistance to radiation and an unfavorable prognosis for ESCC. Increased levels of both miRNAs independently predicted shorter progression-free survival (p = 0.015). We developed a diagnostic model for ESCC using serum microRNAs, resulting in an area under the curve of 0.751. Radiotherapy enhanced the release of miR-143-3p from ESCC cell-derived exosomes. Immune cell infiltration analysis at the Cancer Genome Atlas (TCGA) database revealed that ESCC cell-derived miR-143-3p triggered M2 macrophage polarization. Mechanistically, miR-143-3p upregulation affected chemokine activity and cytokine signaling pathways. Furthermore, ESCC cell exosomal miR-143-3p could be transferred to macrophages, thereby promoting their polarization. Serum miR-143-3p and miR-223-3p could represent diagnostic and prognostic markers for patients with ESCC undergoing radiotherapy. Unfavorable prognosis could be linked to the increased levels of ESCC cell-derived exosomal miR-143-3p, which might promote tumor progression by interacting with macrophages.

Programmed cell death receptor 1 (PD-1) and programmed cell death ligand 1 (PD-L1) are important immune checkpoint molecules that contribute to tumor immune evasion. However, the main treatment modalities for patients with early and intermediate stage colorectal cancer (CRC) are surgery, and the role of PD-1/PD-L1 inhibitors in these patients is not yet clear. Therefore, this study aims to review the treatment progress of PD-1/PD-L1 inhibitors for early- and intermediate-stage microsatellite high-instability (MSI-H) and stable (MSS) colorectal cancer, in order to provide more options for patients with early- and intermediate-stage colorectal cancer.

A scoping review of clinical trial registries ( Clinicaltrials.gov and EU clinical trial registers) and PubMed/Medline database of trials on PD-1/PD-L1 Inhibitors for early and middle-stage MSI-H and MSS CRC was done up to March 2024.

A total of 19 trials related to early to mid-stage MSH-I or MSS CRC were included. Among them, 6 trials are in recruiting status, 3 trials are in active, not recruiting status, 3 trials are completed, 1 trial is terminated, and 1 trial is unknown. Of these, 9 trials involve MSI-H type CRC, and 10 trials involve MSS type CRC. Preclinical phase I/II trials are predominant, with only 3 clinical phase III trials. In trials related to MSI-H type CRC, 4 studies involve PD-1/PD-L1 inhibitors combined with neoadjuvant therapy, and 5 studies involve combination therapy. In trials related to MSS type CRC, 3 studies involve PD-1/PD-L1 inhibitors combined with targeted therapy, 2 studies involve PD-1/PD-L1 inhibitors combined with chemotherapy, 1 study involves PD-1/PD-L1 inhibitor combined immunotherapy, 1 study involves PD-1/PD-L1 inhibitors combined with bacterial therapy, and 3 studies involve PD-1/PD-L1 inhibitors combined with comprehensive therapy. As for primary outcome measures, 4 trials select pathological complete response rates, 3 trials select progression-free survival rate, 3 trials select objective response rate, 3 trials select overall survival rate, 4 trials select disease-free survival rate, 1 trial selects clinical complete response rate, and 1 trial selects percentage of participants with a dose-limiting toxicity.

For early- and middle-stage MSI-H and MSS CRC, PD-1/PD-L1 inhibitors have shown some therapeutic efficacy, as evidenced by phase I/II studies. However, contemporary trial designs exhibit heterogeneity, with relatively few inclusion criteria, the use of various drug combinations and regimens, and significant variations in reported endpoints. Nevertheless, more double-arm, multicenter, randomized controlled trials are still needed to confirm the efficacy of immunotherapy.

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.

Herein, we aimed to evaluate the efficacy and safety of camrelizumab combined with docetaxel and carboplatin as a neoadjuvant treatment for locally advanced oesophageal squamous cell carcinoma (OSCC). Fifty-one patients with OSCC, treated from July 2020 to October 2022, were analyzed. Of them, 41 patients underwent surgery 4-8 weeks after undergoing two cycles of camrelizumab (200 mg IV Q3W) combined with docetaxel (75 mg/m2 IV Q3W) and carboplatin (area under the curve = 5-6 IV Q3W). The primary endpoint was the pathological complete response rate. All 51 patients (100%) experienced treatment-related grades 1-2 adverse events, and 2 patients (3.9%) experienced grade 4 events (including elevated alanine transaminase/aspartate transferase levels and Guillain-Barre syndrome). Fifty patients were evaluated for the treatment efficacy. Of them, 13 achieved complete response, and the objective response rate was 74%. Only 41 patients underwent surgical treatment. The pathological complete response rate was 17.1%, the major pathological response rate was 63.4%, and the R0 resection rate was 100%. Approximately 22% of the patients had tumor regression grades 0. Eight patients (19.5%) developed surgery-related complications. The median follow-up time was 18 months (range: 3-29 months). Four patients experienced disease progression, while four died. The median disease-free survival and overall survival were not reached. Camrelizumab combined with docetaxel and carboplatin is an effective and safe neoadjuvant treatment for locally advanced OSCC. This regimen may afford a potential strategy to treat patients with locally advanced OSCC.

Diagnosis and therapy of esophageal carcinoma is challenging and requires a multidisciplinary approach. The purpose of the updated German guideline "Diagnosis and Treatment of Squamous Cell Carcinoma and Adenocarcinoma of the Esophagus-version 3.1" is to provide practical and evidence-based advice for the management of patients with esophageal cancer. Recommendations were developed by a multidisciplinary expert panel based on an extensive and systematic evaluation of the published medical literature and the application of well-established methodologies (e.g. Oxford evidence grading scheme, grading of recommendations). Accurate diagnostic evaluation of the primary tumor as well as lymph node and distant metastases is required in order to guide patients to a stage-appropriate therapy after the initial diagnosis of esophageal cancer. In high-grade intraepithelial neoplasia or mucosal carcinoma endoscopic resection shall be performed. Whether endoscopic resection is the definitive therapeutic measure depends on the histopathological evaluation of the resection specimen. Esophagectomy should be performed minimally invasive or in combination with open procedures (hybrid technique). Because the prognosis in locally advanced esophageal carcinoma is poor with surgery alone, multimodality therapy is recommended. In locally advanced adenocarcinomas of the esophagus or esophagogastric junction, perioperative chemotherapy or preoperative radiochemotherapy should be administered. In locally advanced squamous cell carcinomas of the esophagus, preoperative radiochemotherapy followed by complete resection or definitive radiochemotherapy without surgery should be performed. In the case of residual tumor in the resection specimen after neoadjuvant radiochemotherapy and R0 resection of squamous cell carcinoma or adenocarcinoma, adjuvant immunotherapy with nivolumab should be given. Systemic palliative treatment options (chemotherapy, chemotherapy plus immunotherapy, immunotherapy alone) in unresectable or metastastic esophageal cancer depend on histology and are stratified according to PD-L1 and/or Her2 expression.

A pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) is seen in up to 40% of the patients with esophageal squamous cell carcinoma (ESCC). No nomogram has been constructed for the prediction of pCR for patients whose primary chemotherapy was a taxane-based regimen. The aim is to identify characteristics associated with a pCR through analyzing multiple pre- and post-nCRT variables and to develop a nomogram for the prediction of pCR for these patients by integrating clinicopathological characteristics and hematological biomarkers.

We analyzed 293 patients with ESCC who underwent nCRT followed by esophagectomy. Clinicopathological factors, hematological parameters before nCRT, and hematotoxicity during nCRT were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR. A nomogram model was built and evaluated for both discrimination and calibration.

After surgery, 37.88% of the study patients achieved pCR. Six variables were included in the nomogram: sex, cN stage, chemotherapy regimen, duration of nCRT, pre-nCRT neutrophil-to-lymphocyte ratio (NLR), and pre-nCRT platelet-to-lymphocyte ratio (PLR). The nomogram indicated good accuracy and consistency in predicting pCR, with a C-index of 0.743 (95% confidence interval: 0.686, 0.800) and a p value of 0.600 (>0.05) in the Hosmer-Lemeshow goodness-of-fit test.

Female, earlier cN stage, duration of nCRT (< 62 days), chemotherapy regimen of taxane plus platinum, pre-nCRT NLR (≥2.199), and pre-nCRT PLR (≥99.302) were significantly associated with a higher pCR in ESCC patients whose primary chemotherapy was a taxane-based regimen for nCRT. A nomogram was developed and internally validated, showing good accuracy and consistency.

Guidelines on the treatment of oesophageal squamous cell carcinoma (SCC) recommend neoadjuvant chemoradiotherapy plus surgery or definitive chemoradiotherapy. The aim of this study was to evaluate the outcome of patients with a cCR after chemoradiotherapy who underwent active surveillance.

Patients with oesophageal SCC who were treated with chemoradiotherapy between January 2016 and June 2022 were identified from an institutional database. Survival and recurrence of patients with a cCR who underwent active surveillance were compared with those of patients who underwent planned surgery. Survival was calculated according to the Kaplan-Meier method and compared between groups using the log rank test.

The 37 patients who underwent active surveillance were older and tumours were more often located in the middle/upper-third of the oesophagus than in the surgery group of 57 patients. Median follow-up was 28.1 (i.q.r. 17.2-47.1) months for the active surveillance group and 20 (12.9-39.1) months for the surgery group. Overall survival was comparable between the two groups, with 3-year survival rates of 50 (95% c.i. 31 to 67) and 59 (40 to 73)% for the active surveillance and surgery groups respectively (P = 0.55). Three-year progression-free survival for patients who underwent active surveillance was better than in the surgery group: 70 (43 to 85) versus 58 (40 to 72)% (P = 0.02). Overall and progression-free survival was comparable between patients in the active surveillance group and 23 patients in the surgery group who had a pCR (ypT0 N0). The overall recurrence rate was comparable between the groups: 7 of 37 (19.4%) in active surveillance group versus 16 of 49 (32.6%) in surgery group (P = 0.26). Locoregional recurrence was noted more often in the active surveillance group and systemic recurrence in the surgery group.

Active surveillance is feasible and safe for patients with oesophageal SCC who have a cCR after chemoradiotherapy.

More than 40% of patients with resectable esophageal squamous cell cancer (ESCC) achieve pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who have favorable prognosis and may benefit from an organ-preservation strategy. Our study aims to develop and validate a machine learning model based on MR radiomics to accurately predict the pCR of ESCC patients after nCRT.

In this retrospective multicenter study, eligible patients with ESCC who underwent baseline MR (T2-weighted imaging) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Models were constructed using machine learning algorithms based on clinical factors and MR radiomics to predict pCR after nCRT. The area under the curve (AUC) and cutoff analysis were used to evaluate model performance.

A total of 155 patients were enrolled in this study, 82 in the training set and 73 in the testing set. The radiomics model was constructed based on two radiomics features, achieving AUCs of 0.968 (95%CI 0.933-0.992) in the training set and 0.885 (95%CI 0.800-0.958) in the testing set. The cutoff analysis resulted in an accuracy of 82.2% (95%CI 72.6-90.4%), a sensitivity of 75.0% (95%CI 58.3-91.7%), and a specificity of 85.7% (95%CI 75.5-96.0%) in the testing set.

A machine learning model based on MR radiomics was developed and validated to accurately predict pCR after nCRT in patients with ESCC.

Esophagectomy is the main treatment for esophageal squamous cell carcinoma (ESCC), and patients with histopathologically negative margins still have a relatively higher recurrence rate. Contrast-enhanced CT (CECT) radiomics might noninvasively obtain potential information about the internal heterogeneity of ESCC and its adjacent tissues. This study aimed to develop CECT radiomics models to preoperatively identify the differences between tumor and proximal tumor-adjacent and tumor-distant tissues in ESCC to potentially reduce tumor recurrence.

A total of 529 consecutive patients with ESCC from Centers A (n = 447) and B (n = 82) undergoing preoperative CECT were retrospectively enrolled in this study. Radiomics features of the tumor, proximal tumor-adjacent (PTA) and proximal tumor-distant (PTD) tissues were individually extracted by delineating the corresponding region of interest (ROI) on CECT and applying the 3D-Slicer radiomics module. Patients with pairwise tissues (ESCC vs. PTA, ESCC vs. PTD, and PTA vs. PTD) from Center A were randomly assigned to the training cohort (TC, n = 313) and internal validation cohort (IVC, n = 134). Univariate analysis and the least absolute shrinkage and selection operator were used to select the core radiomics features, and logistic regression was performed to develop radiomics models to differentiate individual pairwise tissues in TC, validated in IVC and the external validation cohort (EVC) from Center B. Diagnostic performance was assessed using area under the receiver operating characteristics curve (AUC) and accuracy.

With the chosen 20, 19 and 5 core radiomics features in TC, 3 individual radiomics models were developed, which exhibited excellent ability to differentiate the tumor from PTA tissue (AUC: 0.965; accuracy: 0.965), the tumor from PTD tissue (AUC: 0.991; accuracy: 0.958), and PTA from PTD tissue (AUC: 0.870; accuracy: 0.848), respectively. In IVC and EVC, the models also showed good performance in differentiating the tumor from PTA tissue (AUCs: 0.956 and 0.962; accuracy: 0.956 and 0.937), the tumor from PTD tissue (AUCs: 0.990 and 0.974; accuracy: 0.952 and 0.970), and PTA from PTD tissue (AUCs: 0.806 and 0.786; accuracy: 0.760 and 0.786), respectively.

CECT radiomics models could differentiate the tumor from PTA tissue, the tumor from PTD tissue, and PTA from PTD tissue in ESCC.

We aimed to develop and validate a radiomics nomogram and determine the value of radiomic features from lymph nodes (LNs) for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced esophageal squamous cell carcinoma (ESCC).

In this multicenter retrospective study, eligible participants who had undergone NCRT followed by radical esophagectomy were consecutively recruited. Three radiomics models (modelT, modelLN, and modelTLN) based on tumor and LN features, alone and combined, were developed in the training cohort. The radiomics nomogram was developed by incorporating the prediction value of the radiomics model and clinicoradiological risk factors using multivariate logistic regression, and was evaluated using the receiver operating characteristic curve, validated in two external validation cohorts.

Between October 2011 and December 2018, 116 patients were included in the training cohort. Between June 2015 and October 2020, 51 and 27 patients from two independent hospitals were included in validation cohorts 1 and 2, respectively. The radiomics modelTLN performed better than the radiomics modelT for predicting pCR. The radiomics nomogram incorporating the predictive value of the radiomics modelTLN and heterogeneous after NCRT outperformed the clinicoradiological model, with an area under the curve (95% confidence interval) of 0.833 (0.765-0.894) versus 0.764 (0.686-0.833) [p = 0.088, DeLong test], 0.824 (0.718-0.909) versus 0.692 (0.554-0.809) [p = 0.012], and 0.902 (0.794-0.984) versus 0.696 (0.526-0.857) [p = 0.024] in all three cohorts.

Radiomic features from LNs could provide additional value for predicting pCR in ESCC patients, and the radiomics nomogram provided an accurate prediction of pCR, which might aid treatment decision.

Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy dose variations to enhance prediction accuracy. It involved patients with node-positive esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy and surgery, with data collected from 2014 to 2017, randomly split into five subsets for 5-fold cross-validation. The algorithm DCRNet, an advanced version of OCRNet, integrates RT dose distribution into dose contextual representations (DCR), combining dose and pixel representation with ten soft regions. Among the 80 enrolled patients (mean age 55.68 years, primarily male, with stage III disease and middle-part lesions), the ypCR rate was 28.75%, showing no significant demographic or disease differences between the ypCR and non-ypCR groups. Among the three summarization methods, the maximum value across the CTV method produced the best results with an AUC of 0.928. The HRNetV2p model with DCR performed the best among the four backbone models tested, with an AUC of 0.928 (95% CI, 0.884-0.972) based on 5-fold cross-validation, showing significant improvement compared to other models. This underscores DCR-equipped models' superior AUC outcomes. The study highlights the potential of dose-guided deep learning in ypCR prediction, necessitating larger, multicenter studies to validate the results.

Esophageal cancer is the sixth most common cancer worldwide. Approximately 50% of patients have locally advanced disease. The CROSS and NEOCRTEC5010 trials have demonstrated that neoadjuvant chemoradiotherapy followed by surgery is the standard treatment for patients with resectable disease. However, a pathological complete response is frequently not achieved, and most patients have a poor prognosis. The CheckMate 577 trial demonstrates that nivolumab adjuvant therapy improves disease-free survival in patents without a pathological complete response. However, there are still numerous clinical questions of concern that remain controversial based on the results of the subgroup analysis. In this review, we aim to offer constructive suggestions addressing the clinical concerns raised in the CheckMate 577 trial.

Diffusion weighted magnetic resonance imaging (DW-MRI) can be prognostic for response to neoadjuvant chemotherapy (nCRT) in patients with esophageal cancer. However, manual tumor delineation is labor intensive and subjective. Furthermore, noise in DW-MRI images will propagate into the corresponding apparent diffusion coefficient (ADC) signal. In this study a workflow is investigated that combines a denoising algorithm with semi-automatic segmentation for quantifying ADC changes.

Twenty patients with esophageal cancer who underwent nCRT before esophagectomy were included. One baseline and five weekly DW-MRI scans were acquired for every patient during nCRT. A self-supervised learning denoising algorithm, Patch2Self, was used to denoise the DWI-MRI images. A semi-automatic delineation workflow (SADW) was next developed and compared with a manually adjusted workflow (MAW). The agreement between workflows was determined using the Dice coefficients and Brand Altman plots. The prognostic value of ADCmean increases (%/week) for pathologic complete response (pCR) was assessed using c-statistics.

The median Dice coefficient between the SADW and MAW was 0.64 (interquartile range 0.20). For the MAW, the c-statistic for predicting pCR was 0.80 (95% confidence interval (CI):0.56-1.00). The SADW showed a c-statistic of 0.84 (95%CI:0.63-1.00) after denoising. No statistically significant differences in c-statistics were observed between the workflows or after applying denoising.

The SADW resulted in non-inferior prognostic value for pCR compared to the more laborious MAW, allowing broad scale applications. The effect of denoising on the prognostic value for pCR needs to be investigated in larger cohorts.

Neoadjuvant therapy (NAT) + surgical resection for pancreatic cancer (PC) has gained consensus in recent years. Pathological response (PR) is generally assessed according to the College of American Pathologists grading system, ranging from 0 (complete response) to 3 (no response). The aim of our study is to evaluate the PR in a series of resections for PC after NAT and its prognostic implication. 112 patients undergone NAT and resection for PC between 2011 and 2020 were retrospectively evaluated. PR was 0/1, 2 and 3 in 18 (15%), 79 (61%) and 29 (24%) cases, respectively. Chemotherapy regimens different from FOLFIRINOX and gemcitabine + nab-paclitaxel (OR 11.61 (2.53-53.36), p = 0.002) and lymphovascular invasion (OR 11.28 (1.89-67.23), p = 0.008) were associated to PR-3. Median follow-up was 25.8 (3.6-130.5) months. For PR-0/1, PR-2 and PR-3, median DFS was 45.8, 11.5, 4.6 months (p < 0.0001), respectively, while median OS was not reached, 27.1 and 17.5 months (p = 0.0006), respectively. At univariate analysis, PR-0/1 was significantly associated to better DFS and OS (HR 0.33 (0.17-0.67), p = 0.002; HR 0.20 (0.07-0.54), p = 0.002, respectively). At multivariate analysis, pancreaticoduodenectomy (HR 0.50 (0.30-0.84), p = 0.009), LNR (HR 27.14 (1.21-608.9), p = 0.038) and lymphovascular invasion (HR 1.99 (1.06-3.76), p = 0.033) were independently associated to DFS; pre-treatment CA 19.9 value (HR 1.00 (1.00-1.00), p = 0.025), post-treatment resectability status (HR 0.51 (0.28-0.95), p = 0.035), pancreaticoduodenectomy (HR 0.56 (0.32-0.99), p = 0.050), severe morbidity (2.99 (1.22-7.55), p = 0.017), LNR (HR 56.8 (2.08-1548.3), p = 0.017), lymphovascular invasion (HR 2.18 (1.08-4.37), p = 0.029) were independently associated to OS. PR did not reach statistical significance at multivariate analysis. A favorable PR is observed only in a limited number of cases. The prognostic role of PR, despite being promising, remains unclear and further multicentric studies are needed.

To develop accurate and accessible prediction methods for assessing pathologic response following NICT prior to surgery, we conducted a retrospective study including 137 patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery after two cycles of NICT between January 2019 and March 2022 at our center. We collected clinical parameters to evaluate the dynamic changes in the primary tumor. Univariate and multivariate analyses were performed to determine the correlations between these parameters and the pathologic response of the primary tumor. Subsequently, we constructed prediction models for pCR and MPR using multivariate logistic regression. The MPR prediction Model 2 was internally validated using bootstrapping and externally validated using an independent cohort from our center. The univariate logistic analysis revealed significant differences in clinical parameters reflecting tumor regression among patients with varying pathologic responses. The clinical models based on these assessments demonstrated excellent predictive performance, with the training cohort achieving a C-index of 0.879 for pCR and 0.912 for MPR, while the testing cohort also achieved a C-index of 0.912 for MPR. Notably, the MPR prediction Model 2, with a threshold cut-off of 0.74, exhibited 92.7% specificity and greater than 70% sensitivity, indicating a low rate of underestimating residual tumors. In conclusion, our study demonstrated the high accuracy of clinical assessment-based models in pathologic response prediction, aiding in decision-making regarding organ preservation and radiotherapy adjustments after induction immunochemotherapy.

This study aimed to investigate the predictive value of 18 F-FDG PET/CT for pathological response after neoadjuvant immunochemotherapy (NICT) in patients with esophageal squamous cell carcinoma (ESCC).

The clinical data of 54 patients with ESCC who underwent two cycles of NICT followed by surgery were retrospectively analyzed. NICT consisted of PD-1 blockade therapy combined with chemotherapy. 18 F-FDG PET/CT scans were performed before and after NICT. The pathological results after surgery were used to assess the degree of pathological response. The scan parameters of 18 F-FDG PET/CT and their changes before and after NICT were compared with the pathological response.

Among the 54 patients, 10 (18.5%) achieved complete pathological response (pCR) and 21 (38.9%) achieved major pathological response (MPR). The post-NICT scan parameters and their changes were significantly associated with the pathological response. In addition, the values of the changes in the scanned parameters before and after treatment can further predict the pathological response of the patient.

18 F-FDG PET/CT is a useful tool to evaluate the efficacy of NICT and predict pathological response in patients with ESCC. The post-NICT scan parameters and their changes can help identify patients who are likely to achieve pCR or MPR.

Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy.

PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented.

Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature.

Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.

Locally advanced oesophageal cancer can be treated with definitive chemoradiation (dCRT) or with neoadjuvant chemoradiation followed by surgery (nCRT + S), but treatment modality choice is not always clear. The aim of this study was to investigate the factors associated with the choice of treatment modality in locally advanced oesophageal cancer.

This was a retrospective cohort study of 149 patients treated with dCRT(n = 85) or nCRT + S (n = 64) for oesophageal cancer in Helsinki University Hospital in 2008-2018. Logistic regression was used to analyse factors associated with choice of treatment modality and to compare dosimetric factors with postoperative complications. Multivariate analyses identified factors associated with survival.

Surgery was performed after chemoradiation as planned on 64/91 patients (70%). 28/64 had pathological complete response (44%). Probability of nCRT + S was higher in stages I-III versus IV (OR 3.62, 95% CI 1.53-8.53; P = .003), ECOG 0-1 versus 2 (OR 6.99, 95% CI 1.81-26.96; P = .005) or in the middle/lower vs upper oesophageal tumours (OR 5.61, 95% CI 1.83-17.16, P = .003). Probability for surgery was lower, if patient had lost > 10% of body weight (OR 0.46, 95% CI 0.21-0.98, P = 0.043). Patients in the nCRT + S group had significantly better median overall survival (mOS) and local control than the dCRT group (60 vs. 10 months, P < .001 and 53 vs. 6 months, P < 0.0001, respectively). 10/85 (12%) patients died within three months after dCRT. In multivariate analysis, nCRT + S was associated with improved mOS (HR 0.28, 95% CI 0.17-0.44, P < .001). Current smokers had worse mOS (HR 2.02, 95% CI 1.04-3.92, P = .037) compared to never-smokers. No significant dosimetric factor associated with postoperative complications was found.

The overall clinical status of the patients and the stage of the cancer guide the choice of treatment modalities, leading to overtreatment. Patients with better prognoses were more likely operated after chemoradiation, although there is no evidence of OS benefit in previous randomized trials. On the other hand, the prognosis was poor for patients with poor general health and advanced cancers, despite the chemoradiation. Thus, there are signs of overtreatment. MDT practice should be recommended to optimise the choice of treatment modalities. Smoking status is an independent factor associated with survival.