A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.

BACKGROUND: Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear. PURPOSE: To develop a DL method based on T2-weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer. STUDY TYPE: Retrospective. SUBJECTS: A total of 376 patients (108 women [28.7%]) with histopathology-confirmed rectal adenocarcinoma and KRAS mutation status. FIELD STRENGTH/SEQUENCE: A 3 T, turbo spin echo T2WI and single-shot echo-planar diffusion-weighted imaging (b = 0, 1000 sec/mm2 ). ASSESSMENT: A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA). STATISTICAL TESTS: Mann-Whitney U test, Chi-squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer-Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients (P < 0.05 was considered statistically significant). RESULTS: All the nine clinical-MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85. DATA CONCLUSION: The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

The policies on the use of large language models in radiological journals are lacking: a meta-research study.

OBJECTIVE: To evaluate whether and how the radiological journals present their policies on the use of large language models (LLMs), and identify the journal characteristic variables that are associated with the presence. METHODS: In this meta-research study, we screened Journals from the Radiology, Nuclear Medicine and Medical Imaging Category, 2022 Journal Citation Reports, excluding journals in non-English languages and relevant documents unavailable. We assessed their LLM use policies: (1) whether the policy is present; (2) whether the policy for the authors, the reviewers, and the editors is present; and (3) whether the policy asks the author to report the usage of LLMs, the name of LLMs, the section that used LLMs, the role of LLMs, the verification of LLMs, and the potential influence of LLMs. The association between the presence of policies and journal characteristic variables was evaluated. RESULTS: The LLM use policies were presented in 43.9% (83/189) of journals, and those for the authors, the reviewers, and the editor were presented in 43.4% (82/189), 29.6% (56/189) and 25.9% (49/189) of journals, respectively. Many journals mentioned the aspects of the usage (43.4%, 82/189), the name (34.9%, 66/189), the verification (33.3%, 63/189), and the role (31.7%, 60/189) of LLMs, while the potential influence of LLMs (4.2%, 8/189), and the section that used LLMs (1.6%, 3/189) were seldomly touched. The publisher is related to the presence of LLM use policies (p < 0.001). CONCLUSION: The presence of LLM use policies is suboptimal in radiological journals. A reporting guideline is encouraged to facilitate reporting quality and transparency. CRITICAL RELEVANCE STATEMENT: It may facilitate the quality and transparency of the use of LLMs in scientific writing if a shared complete reporting guideline is developed by stakeholders and then endorsed by journals. KEY POINTS: The policies on LLM use in radiological journals are unexplored. Some of the radiological journals presented policies on LLM use. A shared complete reporting guideline for LLM use is desired.