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IntroductionLung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes.MethodsHere, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer.ConclusionThe major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

Key Points Radiomics is becoming increasingly more important in medical imaging The explosion of medical imaging data creates an environment ideal for machine-learning and data-based science Radiomics-based decision-support systems for precision diagnosis and treatment can be a powerful tool in modern medicine Large-scale data sharing is necessary for the validation and full potential that radiomics represents Standardized data collection, evaluation criteria, and reporting guidelines are required for radiomics to mature as a discipline Radiomics is the high-throughput mining of quantitative image features from standard-of-care medical imaging to enable data to be extracted and applied within clinical-decision support systems. The process of radiomics is described and its pitfalls, challenges, opportunities, and capacity to improve clinical decision making. The radiomics field requires standardized evaluation of scientific findings and their clinical relevance. This review provides guidance for investigations to meet this urgent need in the field of radiomics. Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours. Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

ObjectivesDevelop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM).MethodsThis multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested.ResultsThe radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume.ConclusionsRadiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients.Key Points• A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules.• Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

Introduction: Acute appendicitis (AA) is a common cause of abdominal pain that can lead to complications like perforation and intra-abdominal abscesses, increasing morbidity and mortality, often requiring emergency surgery. Nevertheless, appendectomy is performed in up to 95% of uncomplicated cases, while complications like perforation and intra-abdominal abscesses increase morbidity and mortality. The current study compares the accuracy of GPT-4.5, DeepSeek R1, and machine learning in assisting with surgical decision-making for patients presenting with lower abdominal pain at the Emergency Department. Methods: In this multicenter retrospective study, 63 histopathologically confirmed appendicitis patients and 50 control patients with right abdominal pain presenting at the Emergency Department at two German hospitals between October 2022 and October 2023 were included. Using each patient’s clinical, laboratory, and radiological findings, DeepSeek (with and without Retrieval-Augmented Generation using 2020 Jerusalem guidelines) was compared in terms of accuracy with GPT-4.5 and a random forest-based machine-learning model, with a board-certified surgeon (reference standard) to determine the optimal treatment approach (laparoscopic exploration/appendectomy versus conservative antibiotic therapy). Results: Accuracy of agreement with board-certified surgeons in the decision-making of appendectomy versus conservative therapy increased non-significantly from 80.5% to 83.2% with DeepSeek and from 70.8 to 76.1% when GPT-4.5 was provided with the World Journal of Emergency Surgery 2020 Jerusalem guidelines on the diagnosis and treatment of acute appendicitis. The estimated machine-learning model training accuracy was 84.3%, while the validation accuracy for the model was 85.0%. Discussion: GPT-4.5 and DeepSeek R1, as well as the machine-learning model, demonstrate promise in aiding surgical decision-making for appendicitis, particularly in resource-constrained settings. Ongoing training and validation are required to optimize the performance of such models.

In this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastasis. Two comprehensive data sources were used: the Dutch Cancer Society Database (Alp 7072, DESIGN) and \"Big Data To Decide\" (BD2Decide). The gross tumor volumes (GTV) were delineated on contrast-enhanced CT. Radiomic features were extracted using the RadiomiX Discovery Toolbox (OncoRadiomics, Liege, Belgium). Clinical patient features such as age, gender, performance status etc. were collected. Two machine learning methods were chosen for their ability to handle censored data: Cox proportional hazards regression and random survival forest (RSF). Multivariable clinical and radiomic Cox/ RSF models were generated based on significance in univariable cox regression/ RSF analyses on the held out data in the training dataset. Features were selected according to a decreasing hazard ratio for Cox and relative importance for RSF. A total of 444 patients with radiotherapy planning CT-scans were included in this study: 301 head and neck squamous cell carcinoma (HNSCC) patients in the training cohort (DESIGN) and 143 patients in the validation cohort (BD2DECIDE). We found that the highest performing model was a clinical model that was able to predict distant metastasis in oropharyngeal cancer cases with an external validation C-index of 0.74 and 0.65 with the RSF and Cox models respectively. Peritumoral radiomics based prediction models performed poorly in the external validation, with C-index values ranging from 0.32 to 0.61 utilizing both feature selection and model generation methods. Our results suggest that radiomic features from the peritumoral regions are not useful for the prediction of time to OS, LR and DM.

Anecdotal evidence from preliminary observations has noted multiple instances where osteoporosis is present in elderly patients before the clinical detection of bowel disease, even in the absence of overt gastrointestinal symptoms. However, any potential association between these conditions remains to be further investigated. This computed tomography (CT) study investigates whether patients with gastrointestinal (GI) perforation have lower bone mineral density (BMD) than age and sex matched controls. BMD was measured by drawing 3D regions of interest in the bone marrow of the L1–L3 vertebral bodies on CT scans of each of 37 GI perforations and matched controls. Spectrometric calibration of Hounsfield units to the mineral scale was performed with density measurements in the paravertebral muscles (erector spinae) and subcutaneous adipose tissue. The mean BMD of patients with GI perforation (135.9 ± 24.3 mg/ml) was significantly lower than that of controls (96.9 ± 27.5 mg/ml, p  < 0.05). The calculated T-and Z-scores of bone mineral density were also significantly different between the two groups ( p  < 0.05 for each) and were − 2.9 (± 0.90) and − 0.8 (± 0.91) in patients with GI perforation and − 1.6 (± 0.83) and 0 (± 0.96) in the control group, respectively. The results imply that patients with gastrointestinal (GI) perforation have lower bone mineral density (BMD) than age-and sex-matched controls, posing the question whether the screening and aggressive management of osteoporosis is high-risk populations for gastrointestinal perforation can prevent gastrointestinal complications in targeted populations.

Introduction Erosive Hand OsteoArthritis (EHOA) is a common rheumatological problem. We aim to determine characteristics of EHOA patients: comorbidities, radiographic erosivity and pain experienced after being diagnosed, in order to find which of these are potentially relevant in upcoming interventional trials. Method Retrospective analysis of EHOA patients within the electronic database in one centre, with a telephone interview on pain as experienced even exceeding 12 months after being diagnosed. Results Eighty-four non-academic EHOA patients were found: 89% females (median age 69 years), 11% males (similar age distribution). Kellgren-Lawrence (KL) erosivity scores in both sexes were comparable; DIPs scored higher than PIP’s. Comorbid conditions were crystal-induced arthritis, rheumatoid arthritis (RA) and psoriatic arthritis (PsA) in 8%, 5% and 1%, respectively; seropositivity for rheumatoid factor and anti-citrullinated protein antibodies in 8% and 1% respectively. Pain worst experienced often exceeded a visual analogue score of 5.0, but was unrelated to the total KL score. Some pain reduction was reached with non-steroidals (perorally/transcutaneously) as deduced from continued use in 1 in 3. Conclusions In many EHOA patients, there is an unmet need regarding the treatment of pain, which per se was not directly correlated with erosivity score. Future studies may be designed considering the aforementioned characteristics, acting on the inflammatory process resulting in PIP/DIP erosions, with the exclusion of RA and PsA in order to get a clean study on EHOA. Several studies with monoclonal antibodies have been performed but demonstrated ineffectivity on the outcome of pain. Hope glooms with the arrival of innovative small molecules that may reach EHOA target cells. Key Points • Erosive handOA is a common problem in non-academic rheumatology; it is often associated with significant pain in both sexes exceeding a VASpain of 5.0 even years after being diagnosed; 1 in 3 found some relief in non-steroidals perorally/transcutaneously. • Future studies will have to focus on (episodic) inflammatory hand OA resulting in proven erosivity (EHOA) located in PIP plus DIP joints and may have to exclude comorbid active crystal-induced arthritis as well as rheumatoid/psoriatic arthritis and possibly even RF/ACPA seropositivity in order to get a clean study on EHOA. • As several big monoclonals have failed in EHOA, we may have to search for promising new drugs within the group of small molecules. These will have to show a significant pain-reducing effect and preferably also a disease-modifying osteoarthritis drug (DMOAD) effect.

Background: Nonsurgical treatment of uncomplicated appendicitis is a reasonable option in many cases despite the sparsity of robust, easy access, externally validated, and multimodally informed clinical decision support systems (CDSSs). Developed by OpenAI, the Generative Pre-trained Transformer 3.5 model (GPT-3) may provide enhanced decision support for surgeons in less certain appendicitis cases or those posing a higher risk for (relative) operative contra-indications. Our objective was to determine whether GPT-3.5, when provided high-throughput clinical, laboratory, and radiological text-based information, will come to clinical decisions similar to those of a machine learning model and a board-certified surgeon (reference standard) in decision-making for appendectomy versus conservative treatment. Methods: In this cohort study, we randomly collected patients presenting at the emergency department (ED) of two German hospitals (GFO, Troisdorf, and University Hospital Cologne) with right abdominal pain between October 2022 and October 2023. Statistical analysis was performed using R, version 3.6.2, on RStudio, version 2023.03.0 + 386. Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. Statistical significance was defined as p < 0.05. Results: There was agreement between the surgeon’s decision and GPT-3.5 in 102 of 113 cases, and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT-3.5. The estimated model training accuracy was 83.3% (95% CI: 74.0, 90.4), while the validation accuracy for the model was 87.0% (95% CI: 66.4, 97.2). This is in comparison to the GPT-3.5 accuracy of 90.3% (95% CI: 83.2, 95.0), which did not perform significantly better in comparison to the machine learning model (p = 0.21). Conclusions: This study, the first study of the “intended use” of GPT-3.5 for surgical treatment to our knowledge, comparing surgical decision-making versus an algorithm found a high degree of agreement between board-certified surgeons and GPT-3.5 for surgical decision-making in patients presenting to the emergency department with lower abdominal pain.