Esophageal Cancer: Surgical Treatment of Esophageal Cancer – early outcomes and complications
Postoperative complications after esophagectomy remain a major source of morbidity, yet current surveillance relies predominantly on clinician-initiated assessments and structured database fields that systematically underdetect complications. We aimed to develop and internally validate a multimodal early warning system (MEWS-Eso) integrating patient-reported outcomes (PROs), laboratory trajectories, and large language model (LLM)-extracted clinical text features to enable real-time, automated complication surveillance.
This prospective cohort study enrolled 323 consecutive patients undergoing esophagectomy for esophageal cancer at a single tertiary center (2019–2024). PROs were collected using the MD Anderson Symptom Inventory (MDASI) at 10 timepoints from preoperative baseline through 6 months postoperatively (completion rate 91.3%). Laboratory values (complete blood count, C-reactive protein, procalcitonin, albumin) were extracted at 7 perioperative timepoints. Free-text clinical notes (nursing records, physician progress notes, operative reports; median 3,247 words/patient for POD0–POD7) were processed using GPT-4o for structured information extraction and semantic embedding generation. NLP was independently applied to identify complications missed by structured fields. Six models of incrementally increasing modality were compared using 5-fold cross-validation with 100 bootstrap iterations: M1 (PRO-only), M2 (PRO + clinical baseline), M3 (PRO + laboratory), M4 (PRO + text embeddings), M5 (PRO + laboratory + text), and M6 (full multimodal). The primary endpoint was postoperative complications within 90 days under both standard and NLP-augmented outcome definitions.
NLP text mining identified 72 complications missed by structured fields, most notably anastomotic stricture (43 NLP-detected vs. 3 structured-field recorded; p < 0.001). Under the NLP-augmented definition, complication prevalence increased from 52.3% to 61.0%. AUROC improved progressively with modality addition: M1 (PRO-only) 0.658 [95% CI 0.601–0.715], M3 (PRO + laboratory) 0.761 [0.712–0.810], M5 (PRO + laboratory + text) 0.824 [0.779–0.869], and M6 (full multimodal) 0.847 [0.805–0.889]. The CRP trajectory (POD1–POD5 slope) was the single strongest laboratory predictor (OR 2.41, 95% CI 1.78–3.26). LLM-extracted text features contributed an incremental AUROC gain of +0.063 beyond PRO + laboratory. At the RED-alert threshold (probability ≥0.70), MEWS-Eso achieved sensitivity 72.8%, specificity 83.5%, and positive predictive value 78.3%, with a median early warning lead time of 4.2 days. Removing the PRO module caused the largest performance drop (ΔAUROC = −0.089), followed by laboratory (−0.074) and text (−0.063).
A multimodal early warning system integrating PROs, laboratory trajectories, and LLM-processed clinical text substantially outperforms single-modality approaches for detecting postoperative complications after esophagectomy. PROs provide the most irreplaceable modality, while NLP-based text mining both corrects systematic outcome misclassification and contributes independent predictive signals. This framework demonstrates the feasibility of automated, patient-centered multimodal surveillance in surgical oncology.
Simiao Lu, Yi Zhu, Yongtao Han et al.· Diseases of the esophagus· 0 citations
Esophageal Cancer: Surgical Treatment of Esophageal Cancer – early outcomes and complications
Postoperative complications following esophagectomy are frequent and clinically heterogeneous. Serum C-reactive protein (CRP) is a widely available inflammatory marker, yet its longitudinal trajectory after esophagectomy and its relationship with complication severity have not been systematically characterized. This study aimed to identify distinct postoperative CRP trajectory patterns and evaluate their association with Clavien-Dindo graded complication outcomes.
Among 809 consecutive esophagectomy patients at a single center, 131 patients (16.2%) had complete serial CRP measurements on postoperative days (POD) 1, 3, 5, and 7. K-means clustering was applied to identify distinct CRP trajectory groups. Associations between trajectory groups and complications (Clavien-Dindo grading), anastomotic leak, and ICU length of stay were evaluated using chi-square tests, Kruskal-Wallis tests, and multivariable logistic regression. Receiver operating characteristic (ROC) analysis assessed the predictive utility of individual CRP timepoints and derived indicators.
Three distinct CRP trajectories were identified: Low-Stable (n=86, 65.6%), Moderate-Declining (n=20, 15.3%), and High-Persistent (n=25, 19.1%). Complication rates increased significantly across trajectories: severe complications (CD≥III) occurred in 15.1%, 25.0%, and 56.0% of patients in each group, respectively (p<0.001). The High-Persistent group showed a 5.71-fold increased odds of severe complications on multivariable analysis (OR 5.71, 95%CI 1.99–16.40, p=0.001). CRP on day 7 demonstrated the highest individual discriminative value (AUC=0.671), while the CRP D3/D1 ratio (AUC=0.632) offered the earliest actionable prediction at POD 3.
Postoperative CRP trajectory patterns are significantly associated with complication severity after esophagectomy. The High-Persistent trajectory identifies a high-risk subgroup, and the D3/D1 ratio enables early risk stratification by POD 3. Prospective studies with larger cohorts are warranted to validate these findings.
Simiao Lu, Yi Zhu, Yongtao Han et al.· Diseases of the esophagus· 0 citations
Esophageal Cancer: Molecular Biology/Pathology
With the advancement of personalized medicine, multi-target drug development has garnered significant attention, particularly for complex diseases such as cancer. This study aims to identify potential dual-target inhibitors against Epidermal Growth Factor Receptor (EGFR) and Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), two proteins whose aberrant activation is closely associated with tumorigenesis and progression in various cancers.
We collected IC50 values of active compounds for EGFR and PIK3CA from the BindingDB database, which were then standardized to pIC50 values using RDKit. A total of 2048 Extended-Connectivity Fingerprints (ECFPs) were calculated to serve as molecular descriptors. Various machine learning models, including Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbors, and LightGBM, were developed. The optimal model parameters were determined using ten-fold cross-validation and grid search, and model performance was assessed by Mean Absolute Error (MAE), Mean Squared Error (MSE), and the R-squared (R2) value.
The SVM model demonstrated the best performance and was selected to predict activities for both EGFR and PIK3CA.
The natural product compounds CNP0456830 and CNP0467494 exhibited the lowest binding free energies for both EGFR and PIK3CA, identifying them as the most promising dual-target inhibitors. This study offers a new direction and a potential therapeutic strategy for personalized drug design in cancer treatment.
Simiao Lu, Yi Zhu, Yongtao Han et al.· Diseases of the esophagus· 0 citations