Open science practices need substantial improvement in prognostic model studies in oncology using machine learning.

OBJECTIVE: To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING: We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices. RESULTS: We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs. CONCLUSION: The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.

Overinterpretation and misreporting of prognostic factor studies in oncology: a systematic review.

BACKGROUND: Cancer prognostic biomarkers have shown disappointing clinical applicability. The objective of this study was to classify and estimate how study results are overinterpreted and misreported in prognostic factor studies in oncology. METHODS: This systematic review focused on 17 oncology journals with an impact factor above 7. PubMed was searched for primary clinical studies published in 2015, evaluating prognostic factors. We developed a classification system, focusing on three domains: misleading reporting (selective, incomplete reporting, misreporting), misleading interpretation (unreliable statistical analysis, spin) and misleading extrapolation of the results (claiming irrelevant clinical applicability, ignoring uncertainty). RESULTS: Our search identified 10,844 articles. The 98 studies included investigated a median of two prognostic factors (Q1-Q3, 1-7). The prognostic factors' effects were selectively and incompletely reported in 35/98 and 24/98 full texts, respectively. Twenty-nine articles used linguistic spin in the form of strong statements. Linguistic spin rejecting non-significant results was found in 34 full-text results and 15 abstract results sections. One in five articles had discussion and/or abstract conclusions that were inconsistent with the study findings. Sixteen reports had discrepancies between their full-text and abstract conclusions. CONCLUSIONS: Our study provides evidence of frequent overinterpretation of findings of prognostic factor assessment in high-impact medical oncology journals.

Reporting guidelines for oncology research: helping to maximise the impact of your research.

Many reports of health research omit important information needed to assess their methodological robustness and clinical relevance. Without clear and complete reporting, it is not possible to identify flaws or biases, reproduce successful interventions, or use the findings in systematic reviews or meta-analyses. The EQUATOR Network (http://www.equator-network.org/) promotes responsible reporting and the use of reporting guidelines to improve the accuracy, completeness, and transparency of health research. EQUATOR supports researchers by providing online resources and training. EQUATOR Oncology, a project funded by Cancer Research UK, aims to support cancer researchers reporting their research through the provision of online resources. In this article, our objective is to highlight reporting issues related to oncology research publications and to introduce reporting guidelines that are designed to aid high-quality reporting. We describe generic reporting guidelines for the main study types, and explain how these guidelines should and should not be used. We also describe 37 oncology-specific reporting guidelines, covering different clinical areas (e.g., haematology or urology) and sections of the report (e.g., methods or study characteristics); most of these are little-used. We also provide some background information on EQUATOR Oncology, which focuses on addressing the reporting needs of the oncology research community.

Effect of lapatinib on oral digoxin absorption in patients.

The potential for an interaction between lapatinib and absorption of the P-glycoprotein (ABCB1) substrate digoxin at a therapeutic dose in breast cancer patients was characterized. Seventeen women with HER2-positive metastatic breast cancer received a single oral 0.5-mg dose of digoxin on days 1 and 9 and oral lapatinib 1500 mg once daily on days 2 through 9. Digoxin pharmacokinetic parameters were determined on day 1 (digoxin administration alone) and on day 9 (coadministration of lapatinib and digoxin), and parameters were compared to determine the effects of lapatinib on digoxin absorption. Concomitant medications that could affect ABCB1 were accounted for. Lapatinib 1500 mg/day increased digoxin absorption approximately 80%, implicating lapatinib inhibition of intestinal ABCB1-mediated efflux. In summary, coadministration of lapatinib with narrow therapeutic index drugs that are substrates of ABCB1 should be undertaken with caution and dose adjustment should be considered.

Analysis of the regulation, expression, and localisation of the isocitrate lyase from Aspergillus fumigatus, a potential target for antifungal drug development.

Invasive aspergillosis, caused by Aspergillus fumigatus, is a severe systemic infection in immunocompromised patients. New drug targets are required, since therapeutic treatment often fails and is hampered by severe side effects of antifungals. Enzymes of the glyoxylate bypass are potential targets, since they are absent in humans, but required for growth of Aspergillus on C2-generating carbon sources. The key enzyme isocitrate lyase (ICL) can be inhibited by 3-nitropropionate, both as a purified enzyme and within intact cells, whereas the latter inhibition upregulates ICL promoter activity. ICL was found in distinct subcellular structures within growing hyphae, but only under conditions requiring ICL activity. In contrast, ICL was constitutively found in conidia, suggesting a specific role during germination. Lipids, as potential substrates, were detected in conidia and macrophages. Additionally, germinating conidia within macrophages contain ICL, suggesting that the glyoxylate shunt might be a relevant target for development of antifungals.