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PubMed2003

The inclusion of reports of randomised trials published in languages other than English in systematic reviews.

OBJECTIVE: To assemble a large dataset of language restricted and language inclusive systematic reviews, including both conventional medicinal (CM) and complementary and alternative medicine (CAM) interventions. To then assess the quality of these reports by considering and comparing different types of systematic reviews and their associated RCTs; CM and CAM interventions; the effect of language restrictions compared with language inclusions, and whether these results are influenced by other issues, including statistical heterogeneity and publication bias, in the systematic review process. DATA SOURCES: MEDLINE, EMBASE, the Cochrane Database of Systematic Reviews and the Centralised Information Service for Complementary Medicine. REVIEW METHODS: Three types of systematic reviews were included: language restricted; language inclusive/English language (EL) reviews that searched RCTs in languages other than English (LOE) but did not find any and, hence, could not include any, in the quantitative data synthesis; and systematic reviews that searched for RCTs in LOE and included them in the quantitative data synthesis. Fisher's exact test was applied to compare the three different types of systematic reviews with respect to their reporting characteristics and the systematic review quality assessment tool. The odds ratio of LOE trials versus EL trials was computed for each review and this information was pooled across the reviews to examine the influence that language of publication and type of intervention (CM, CAM) have on the estimates of intervention effect. Several sensitivity analyses were performed. RESULTS: The LOE RCTs were predominantly in French and German. Language inclusive/LOE systematic reviews were of the highest quality compared with the other types of reviews. The CAM reviews were of higher quality compared with the CM reviews. There were only minor differences in the quality of reports of EL RCTs compared with the eight other languages considered. However, there are inconsistent differences in the quality of LOE reports depending on the intervention type. The results, and those reported previously, suggest that excluding reports of RCTs in LOE from the analytical part of a systematic review is reasonable. Because the present research and previous efforts have not included every type of CM RCT and the resulting possibility of the uncertainty as to when bias will be present by excluding LOE, it is always prudent to perform a comprehensive search for all evidence. This result only applies to reviews investigating the benefits of CM interventions. This does not imply that systematic reviewers should neglect reports in LOE. We recommend that systematic reviewers search for reports regardless of the language. There may be merit in including them in some aspects of the review process although this decision is likely to depend on several factors, including fiscal and other resources being available. Language restrictions significantly shift the estimates of an intervention's effectiveness when the intervention is CAM. Here, excluding trials reported in LOE, compared with their inclusion, resulted in a reduced intervention effect. The present results do not appear to be influenced by statistical heterogeneity and publication bias. CONCLUSIONS: With the exception of CAM systematic reviews, the quality of recently published systematic reviews is less than optimal. Language inclusive/LOE systematic reviews appear to be a marker for a better quality systematic review. Language restrictions do not appear to bias the estimates of a conventional intervention's effectiveness. However, there is substantial bias in the results of a CAM systematic review if LOE reports are excluded from it.

Complementary Therapies↗

PubMed2026

Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are a robust prognostic marker in patients with triple-negative breast cancer. Artificial intelligence (AI)-derived computational tools assessing TILs could improve efficiency, but require independent validation against clinical outcomes. We aimed to compare the prognostic performance of AI-derived TIL scores with pathologist-scored TILs in a large, prospectively collected dataset pooled from randomised controlled trials. METHODS: CATALINA was an independent, external validation study using prospectively collected long-term clinical outcome data pooled from seven randomised clinical trials conducted at multiple sites. We independently evaluated two previously validated AI pipelines that generate five computationally assessed tumour-infiltrating lymphocyte (cTIL) scores by masked, independent deployment of locked models. cTIL scores were correlated with the mean of the pathologist-scored stromal TILs (sTILs) in 220 digitised haematoxylin and eosin whole slide images in a cohort of patients with early-stage triple-negative or HER-2 positive breast cancer, previously scored by trained pathologists in a TIL-reproducibility study. Prognostic performance was assessed in a separate cohort of patients with early triple-negative breast cancer pooled from seven prospective, randomised adjuvant trials. Multivariable Cox regression models adjusted for clinicopathological factors and study heterogeneity assessed associations of cTIL score and sTIL score with invasive disease-free survival, distant disease-free survival, and overall survival. 5-year discrimination was estimated using time-dependent area under the receiver operating characteristic curve (AUC). FINDINGS: Individual data were collated from 1759 patients, of whom 1356 had complete clinicopathological data, pathologist sTIL scores, and cTIL scores available. Modest correlation (r 0·375-0·473) was observed between cTIL scores and the mean pathologist sTIL score. Both sTIL and cTIL were independently associated with 5-year invasive disease-free survival, distant disease-free survival, and overall survival after adjustment for clinicopathological factors (hazard ratio for invasive disease-free survival was 0·73 [95% CI 0·66-0·82]; q<0·0001, distant disease-free survival was 0·70 [0·61-0·79]; q<0·0001, and overall survival was 0·72 [0·63-0·82]; q<0·0001 for sTIL scores and 0·80 [0·73-0·89]; q<0·0001, 0·77 [0·69-0·86]; q<0·0001, and 0·79 [0·70-0·88]; q=0·0002, respectively, for percentage_lymphocyte scores). In models adjusted for clinicopathological variables and sTIL score, cTIL score did not maintain a statistically significant prognostic association. Both sTIL and cTIL scores improved the 5-year AUC over clinicopathological variables alone, while cTIL score did not significantly further improve AUC when combined with clinicopathological variables and sTIL score. INTERPRETATION: Two cTIL models deployed entirely without retraining or modification provided statistically significant prognostic information and improved risk discrimination compared with clinicopathological variables alone in this large, platform-based, independent validation study. Although cTIL score did not incrementally improve prognostication compared with models combining clinicopathological variables with sTIL score, these findings support the application of cTILs as a reproducible prognostic biomarker, particularly in settings where routine or widespread pathologist assessment is unavailable. FUNDING: Breast Cancer Research Foundation (USA).

Humans↗