The clinical outcomes of elderly esophageal cancer patients who received definitive chemoradiotherapy

Risk factors of poor long-term outcomes in elderly patients with esophageal squamous cell carcinoma after minimally invasive esophagectomy

PURPOSE

Elderly patients with esophageal squamous cell carcinoma (ESCC) have more comorbidities than young patients do. Elderly smokers have a high mortality rate owing to physical dysfunction. This study aimed to identify risk factors for long-term outcomes after minimally invasive esophagectomy (MIE) in elderly patients with ESCC.

METHODS

This study included 110 elderly patients (aged ≥ 75 years) with ESCC who underwent MIE at Kobe University Hospital. Multivariate Cox proportional hazards regression analyses were performed to identify risk factors, including the geriatric nutritional risk index (GNRI), Charlson comorbidity index, and elderly smoker status, defined as smoking at the age of 70 years.

RESULTS

Multivariate analysis identified that elderly smokers, cT or cN status (≥ cT2 or cN-positive), and GNRI (≤ 92) were independent prognostic factors for overall survival (p = 0.026, 0.019, and 0.038, respectively). For patients with ≥ cT2 or cN-positive ESCC, elderly smokers or patients with GNRI (≤ 92) have significantly worse survival (p = 0.038). Elderly smokers were at risk of death from other diseases five years postoperatively (p = 0.016).

CONCLUSIONS

Elderly smokers, cT2 or cN positivity, and low GNRI were risk factors for poor survival. Furthermore, for patients with ≥ cT2 or cN-positive ESCC, these risk factors were associated with poor survival.

Induction Radiochemotherapy for Esophageal Cancer: Long-Term Outcomes from a Single-Center Study

Background/Objectives: The management of esophageal cancer (EC) remains a significant clinical challenge, particularly in optimizing therapeutic strategies for different stages and subgroups. This study assessed the impact of preoperative radiochemotherapy (CRT) on clinical staging and identified subgroups for whom definitive CRT (dCRT) may provide a favorable alternative to surgery. Methods: Sixty-one patients with esophageal adenocarcinoma or squamous cell carcinoma were enrolled. Pre-treatment staging included computed tomography, gastroscopy with biopsy, and comprehensive laboratory evaluations. Patients received preoperative CRT following the CROSS or dCRT protocols based on tumor stage. Surgical approaches included staged esophagectomy or single-stage Ivor Lewis procedures. Four patients declined surgery and were treated with dCRT. Postoperative outcomes were evaluated using pTNM classification. Follow-up included imaging and endoscopic surveillance. Statistical analyses assessed changes in staging and factors influencing treatment outcomes. Results: CRT significantly reduced T stage across the entire cohort (p = 0.0002), with complete pathological response (pT0N0M0) observed in 54.5% of patients following induction CRT (p = 0.0001). Male patients demonstrated a significant reduction in T stage (p = 0.0008), while a similar trend in females was not significant (p = 0.068). Among patients declining surgery, dCRT demonstrated acceptable oncologic control over a mean follow-up of 4 ± 0.79 years. Conclusions: Preoperative CRT effectively downstages EC and achieves high rates of response, especially in male patients. Therefore, dCRT may be a viable alternative in selected patients, emphasizing the need for individualized treatment strategies to optimize outcomes. These findings underscore the importance of refining multimodal approaches in EC care.

Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress

BACKGROUND

Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC).

METHODS

This study included elderly patients with ESCC who underwent curative ESCC resection surgery continuously from January 2013 to December 2020 and were stratified into the training and external validation cohorts. Using Cox stepwise regression analysis based on Akaike information criterion, the relationship between oxidative stress biomarkers and prognosis was explored, and a geriatric ESCC-related oxidative stress score (OSS) was constructed. To construct a predictive model for 3-year overall survival (OS), machine-learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed. These machine-learning strategies play a key role in data mining and pattern recognition tasks. Each model was tested in the external validation cohort through 1000 resampling iterations. Validation was conducted using receiver operating characteristic area under the curve (AUC) and calibration plots.

RESULTS

The training cohort and validation cohort consisted of 340 and 145 patients, respectively. In the training cohort, the 3-year OS rate for patients was 59.2%. We constructed the OSS based on systemic oxidative stress biomarkers using the training cohort. The study found that pathological N stage, pathological T stage, tumor histological type, lymphovascular invasion, CEA, OSS, CA 19 - 9, and the amount of bleeding were the most important factors influencing the 3-year OS. These eight important features were included in training the RF, DT, and SVM and trained on the training cohort and validated cohort, respectively. In the training cohort, the RF model demonstrated the highest predictive performance with an AUC of 0.975 (0.962-0.987), while the DT model is 0.784 (0.739-0.830) and the SVM is 0.879 (0.843-0.916). In the external validation cohort, the RF model again exhibited the highest performance with an AUC of 0.791 (0.717-0.864), compared to the DT model with an AUC of 0.717 (0.640-0.794) and 0.779 (0.702-0.856) in SVM.

CONCLUSIONS

The random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly patients with ESCC after curative surgery.

Machine learning-based prediction of 5-year survival in elderly NSCLC patients using oxidative stress markers

Background

Oxidative stress plays a significant role in aging and cancer, yet there is currently a lack of research utilizing machine learning models to examine the relationship between oxidative stress and prognosis in elderly non-small cell lung cancer (NSCLC) patients.

Methods

This study included elderly NSCLC patients who underwent radical lung cancer resection from January 2012 to April 2018, exploring the relationship between Oxidative Stress Score (OSS) and prognosis. Machine learning techniques, including Decision Trees (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to develop predictive models for 5-year overall survival (OS).

Results

The datasets consisted of 1647 patients in the training set, 705 in the internal validation set, and 516 in the external validation set. An OSS was formulated from six systemic oxidative stress biomarkers, such as albumin, total bilirubin, and blood urea nitrogen, among others. Boruta variable importance analysis identified low OSS as a key indicator of poor prognosis. The OSS was subsequently integrated into the DT, RF, and SVM models for training. These models, optimized through hyperparameter tuning on the training set, were then evaluated on the internal and external validation sets. The RF model demonstrated the highest predictive performance, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.794 in the internal validation set, compared to AUCs of 0.711 and 0.760 for the DT and SVM models, respectively. Similarly, in the external validation set, the RF model achieved an AUC of 0.784, outperforming the DT and SVM models, which had AUCs of 0.699 and 0.730, respectively. Calibration plots confirmed the RF model's superior calibration, followed by the SVM model, with the DT model performing the poorest.

Conclusion

The OSS-based clinical prediction model, constructed using machine learning methodologies, effectively predicts the prognosis of elderly NSCLC patients post-radical surgery.

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma

No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson's correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86-1.00]) as well as in the validation cohort (0.92 [95%CI 0.71-0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness.

Safe and curative modified two-stage operation for T4 esophageal cancer after definitive chemoradiotherapy: a case report

BACKGROUND

The prognosis of esophageal cancer (EC) with organ invasion is extremely poor. In these cases, definitive chemoradiotherapy (CRT) followed by salvage surgery can be planned; however, the issue of high morbidity and mortality rates persists. Herein, we report the long-term survival of a patient with EC and T4 invasion who underwent a modified two-stage operation after definitive CRT.

CASE PRESENTATION

A 60-year-old male presented with type 2 upper thoracic EC with tracheal invasion. First, definitive CRT was performed, which resulted in tumor shrinkage and improvement in the tracheal invasion. However, an esophagotracheal fistula subsequently developed, and the patient was treated with fasting and antibiotics. Although the fistula recovered, severe esophageal stenoses made oral intake impossible. To improve quality of life and cure the EC, a modified two-stage operation was planned. In the first surgery, an esophageal bypass was performed using a gastric tube with cervical and abdominal lymph node dissections. After confirming improved nutritional status and absence of distant metastasis, the second surgery was performed with subtotal esophagectomy, mediastinal lymph node dissection, and tracheobronchial coverage of the fistula. The patient discharged without major complications after radical resection and has been recurrence-free for 5 years since the start of treatment.

CONCLUSION

A standard curative strategy could be difficult for EC with T4 invasion due to differences in the invaded organs, presence of complications, and patient condition. Therefore, patient-tailored treatment plans are needed, including a modified two-stage operation.

Chemoradiotherapy in geriatric patients with squamous cell carcinoma of the esophagus: Multi-center analysis on the value of standard treatment in the elderly

Background and purpose

To evaluate the tolerability and outcomes of chemoradiation in elderly patients with locally advanced esophageal squamous cell carcinoma (ESCC).

Materials and methods

This multi-center retrospective analysis included 161 patients with SCC of the esophagus with a median age of 73 years (range 65-89 years) treated with definitive or neoadjuvant (chemo)radiotherapy between 2010 and 2019 at 3 large comprehensive cancer centers in Germany. Locoregional control (LRC), progression-free survival (PFS), distant metastasis-free survival (DMFS), overall survival (OS), and treatment-associated toxicities were analyzed, and parameters determining patient outcomes and treatment tolerance were assessed.

Results

The delivery of radiotherapy without dose reduction was possible in 149 patients (93%). In 134 patients (83%), concomitant chemotherapy was initially prescribed; however, during the course of therapy, 41% of these patients (n = 55) required chemotherapy de-escalation due to treatment-related toxicities. Fifty-two patients (32%) experienced higher-grade acute toxicities, and 22 patients (14%) higher-grade late toxicities. The 2-year LRC, DMFS, PFS, and OS rates amounted to 67.5%, 33.8%, 31.4%, and 40.4%, respectively. Upon multivariate analysis, full-dose concomitant chemotherapy (vs. no or modified chemotherapy) was associated with significantly better DMFS (p=0.005), PFS (p=0.005) and OS (p=0.001). Furthermore, neoadjuvant chemoradiotherapy followed by tumor resection (vs. definitive chemoradiotherapy or definitive radiotherapy alone) significantly improved PFS (p=0.043) and OS (p=0.049). We could not identify any clinico-pathological factor that was significantly associated with LRC. Furthermore, definitive (chemo)radiotherapy, brachytherapy boost and stent implantation were significantly associated with higher-grade acute toxicities (p<0.001, p=0.002 and p=0.04, respectively). The incidence of higher-grade late toxicities was also significantly associated with the choice of therapy, with a higher risk for late toxicities when treatment was switched from neoadjuvant to definitive (chemo)radiotherapy compared to primary definitive (chemo)radiotherapy (p<0.001).

Conclusions

Chemoradiation with full-dose and unmodified concurrent chemotherapy has a favorable prognostic impact in elderly ESCC patients; however, about half of the analyzed patients required omission or adjustment of chemotherapy due to comorbidities or toxicities. Therefore, the identification of potential predictive factors for safe administration of concurrent chemotherapy in elderly ESCC patients requires further exploration to optimize treatment in this vulnerable patient cohort.