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Background The aim of this study is to provide a comprehensive understanding of the current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its predictive accuracy in identifying eligible patients for inclusion in such trials. Methods Databases of PubMed, Embase, and Cochrane CENTRAL were searched until June 2022. Articles were included if they reported on AI actively being used in the clinical trial enrollment process. Narrative synthesis was conducted among all extracted data: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. For studies where the 2x2 contingency table could be calculated or supplied by authors, a meta-analysis to calculate summary statistics was conducted using the hierarchical summary receiver operating characteristics curve model. Results Ten articles reporting on more than 50 000 patients in 19 datasets were included. Accuracy, sensitivity, and specificity exceeded 80% in all but 1 dataset. Positive predictive value exceeded 80% in 5 of 17 datasets. Negative predictive value exceeded 80% in all datasets. Summary sensitivity was 90.5% (95% confidence interval [CI] = 70.9% to 97.4%); summary specificity was 99.3% (95% CI = 81.8% to 99.9%). Conclusions AI demonstrated comparable, if not superior, performance to manual screening for patient enrollment into cancer clinical trials. As well, AI is highly efficient, requiring less time and human resources to screen patients. AI should be further investigated and implemented for patient recruitment into cancer clinical trials. Future research should validate the use of AI for clinical trials enrollment in less resource-rich regions and ensure broad inclusion for generalizability to all sexes, ages, and ethnicities.

Esophageal cancer and its treatment can cause serious morbidity/toxicity. These effects on health-related quality of life (HRQOL) can be measured using disease-specific scales such as FACT-E, generic scales such as EQ-5D-3L, or through symptoms. In a two-year cross-sectional study, we compared HRQOL across esophageal cancer patients treated in an ambulatory clinic and across multiple disease states, among patients with all stages of esophageal cancer. Consenting patients completed FACT-E, EQ-5D, a visual analog scale, and patient reported (PR)-ECOG. Symptom complexes were constructed from FACT-E domains. Responses were categorized by disease state: pre-, during, and post-treatment, surveillance, progression, and palliative chemotherapy. Spearman correlation and multivariable linear regression characterized these associations. In total, 199 patients completed 317 questionnaires. Mean FACT-E and subscale scores dropped from baseline through treatment and recovered during post-treatment surveillance (P < 0.001); EQ-5D health utility scores (HUS) displayed a similar pattern but with smaller differences (P = 0.07), and with evidence of ceiling effect. Among patients with stage II/III esophageal cancer, mean EQ-5D HUS varied across disease states (P < 0.001), along with FACT-E and subscales (P < 0.001). Among patients with advanced disease, there was no significant difference between baseline and on-treatment total scores, but improved esophageal cancer-specific scales were noted (P = 0.003). Strong correlation was observed between EQ-5D and FACT-E (R = 0.73), along with physical and functional subscales. In addition, the association between FACT-E and EQ-5D HUS was maintained in a multivariable model (P < 0.001). We interpret these results to suggest that in a real-world clinic setting, FACT-E, EQ-5D HUS, and symptoms were strongly correlated. Most HRQOL and symptom parameters suggested that patients had worse HRQOL and symptoms during curative therapy, but recovered well afterwards. In contrast, palliative chemotherapy had a neutral to positive impact on HRQOL/symptoms when compared to their baseline pre-treatment state.

Polymorphisms in the vascular endothelial growth factor (VEGF)/angiogenesis pathway have been implicated previously in cancer risk, prognosis and response to therapy including in esophageal adenocarcinoma. Prior esophageal adenocarcinoma studies focused on using candidate polymorphisms, limiting the discovery of novel polymorphisms. Here, we applied the tagSNP (single nucleotide polymorphism) approach to identify new VEGF pathway polymorphisms associated with esophageal adenocarcinoma prognosis and validated them in an independent cohort of esophageal adenocarcinoma patients. In 231 esophageal adenocarcinoma patients of all stages/treatment plans, 58 genetic polymorphisms (18 KDR, 7 VEGFA and 33 FLT1) selected through tagging and assessment of predicted function were genotyped. Cox-proportional hazard models adjusted for important socio-demographic and clinico-pathological factors were applied to assess the association of genetic polymorphisms with overall survival (OS) and progression-free survival (PFS). Significantly associated polymorphisms were then validated in an independent cohort of 137 esophageal adenocarcinoma patients. Among the 231 discovery cohort patients, 86% were male, median diagnosis age was 64 years, 34% were metastatic at diagnosis and median OS and PFS were 20 and 12 months, respectively. KDR rs17709898 was found significantly associated with PFS (adjusted hazard ratio, aHR = 0.69, 95% confidence interval (CI): 0.53-0.90; P = 5.9E-3). FLT1 rs3794405 and rs678714 were significantly associated with OS (aHR = 1.44, 95% CI: 1.04-1.99; P = 0.03 and aHR = 1.50, 95% CI: 1.01-2.24; P = 0.045, respectively). No VEGFA polymorphisms were found significantly associated with either outcome. Upon validation, FLT1 rs3794405 remained strongly associated with OS (aHR = 1.59, 95% CI: 1.04-2.44; P = 0.03). FLT1 rs3794405 is significantly associated with OS in esophageal adenocarcinoma, whereby each variant allele confers a 45-60% increased risk of mortality. Validation and evaluation of this association in other cancer sites are warranted.