Structured reporting of computed tomography in the polytrauma patient assessment: a Delphi consensus proposal

The Potential of Gemini and GPTs for Structured Report Generation based on Free-Text 18F-FDG PET/CT Breast Cancer Reports

RATIONALE AND OBJECTIVE

To compare the performance of large language model (LLM) based Gemini and Generative Pre-trained Transformers (GPTs) in data mining and generating structured reports based on free-text PET/CT reports for breast cancer after user-defined tasks.

MATERIALS AND METHODS

Breast cancer patients (mean age, 50 years ± 11 [SD]; all female) who underwent consecutive 18F-FDG PET/CT for follow-up between July 2005 and October 2023 were retrospectively included in the study. A total of twenty reports from 10 patients were used to train user-defined text prompts for Gemini and GPTs, by which structured PET/CT reports were generated. The natural language processing (NLP) generated structured reports and the structured reports annotated by nuclear medicine physicians were compared in terms of data extraction accuracy and capacity of progress decision-making. Statistical methods, including chi-square test, McNemar test and paired samples t-test, were employed in the study.

RESULTS

The structured PET/CT reports for 131 patients were generated by using the two NLP techniques, including Gemini and GPTs. In general, GPTs exhibited superiority over Gemini in data mining in terms of primary lesion size (89.6% vs. 53.8%, p < 0.001) and metastatic lesions (96.3% vs 89.6%, p < 0.001). Moreover, GPTs outperformed Gemini in making decision for progress (p < 0.001) and semantic similarity (F1 score 0.930 vs 0.907, p < 0.001) for reports.

CONCLUSION

GPTs outperformed Gemini in generating structured reports based on free-text PET/CT reports, which is potentially applied in clinical practice.

DATA AVAILABILITY

The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Body composition parameters in initial CT imaging of mechanically ventilated trauma patients: Single-centre observational study

BACKGROUND

Body composition parameters provide relevant prognostic significance in critical care cohorts and cancer populations. Published results regarding polytrauma patients are inconclusive to date. The goal of this study was to analyse the role of body composition parameters in severely injured trauma patients.

METHODS

All consecutive patients requiring emergency tracheal intubation and mechanical ventilation before initial computed tomography (CT) at a level-1 trauma centre over a 12-year period (2008-2019) were reanalysed. The analysis included CT-derived body composition parameters based upon whole-body trauma CT as prognostic variables for 30-day mortality, intensive care unit length of stay (ICU LOS) and mechanical ventilation duration.

RESULTS

Four hundred seventy-two patients (75% male) with a median age of 49 years, median injury severity score of 26 and 30-day mortality rate of 22% (104 patients) met the inclusion criteria and were analysed. Regarding body composition parameters, 231 patients (49%) had visceral obesity, 75 patients had sarcopenia (16%) and 35 patients had sarcopenic obesity (7.4%). After adjustment for statistically significant univariable predictors age, body mass index, sarcopenic obesity, visceral obesity, American Society of Anesthesiologists classification ≥3, injury severity score and Glasgow Coma Scale ≤ 8 points, the Cox proportional hazard model identified sarcopenia as significant prognostic factor of 30-day mortality (hazard ratio 2.84; 95% confidence interval 1.38-5.85; P = 0.004), which was confirmed in Kaplan-Meier survival analysis (log-rank P = 0.006). In a subanalysis of 363 survivors, linear multivariable regression analysis revealed no significant associations of body composition parameters with ICU LOS and duration of mechanical ventilation.

CONCLUSIONS

In a multivariable analysis of mechanically ventilated trauma patients, CT-defined sarcopenia was significantly associated with 30-day mortality whereas no associations of body composition parameters with ICU LOS and duration of mechanical ventilation were observed.

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma

Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)'s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods: A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results: Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions: Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

PURPOSE

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases.

METHODS

Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered.

RESULTS

The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%.

CONCLUSIONS

Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Abdominal compartment syndrome: what radiologist needs to know

The intra-abdominal hypertension (IAH) and the abdominal compartment syndrome (ACS) are life-threatening conditions with a significant rate of mortality; therefore, early detection is paramount in their optimal management. IAH is diagnosed when the intra-abdominal pressure (IAP) is more than 12 mmHg. It can occur when the intra-abdominal volume increases (ileus, ascites, trauma, pancreatitis, etc.) and/or the abdominal wall compliance decreases. IAH can cause decreased venous flow, low cardiac output, renal impairment, and decreased respiratory compliance. Consequently, these complications can lead to multiple organ failure and induce the abdominal compartment syndrome (ACS) when IAP rises above 20 mmHg. The diagnosis is usually made with intravesical pressure measurement. However, this measurement was not always possible to obtain; therefore, alternative diagnostic techniques should be considered. In this setting, computed tomography (CT) may play a crucial role, allowing the detection and characterization of pathological conditions that may lead to IAH. This review is focused on the pathogenesis, clinical features, and radiological findings of ACS, because their presence allows radiologists to raise the suspicion of IAH/ACS in critically ill patients, guiding the most appropriate treatment.

Hepatobiliary-specific magnetic resonance contrast agents: role in biliary trauma

Non-iatrogenic traumatic bile duct injuries (NI-TBIs) are a rare complication after abdominal trauma, with an estimated prevalence of 2.8-7.4% in patients underwent blunt liver injuries. They may be overlooked in patients with extensive multi-organ trauma, particularly hepatic, splenic and duodenal injuries, which have a prevalence of 91%, 54% and 54%, respectively. Whole body contrast-enhanced computed tomography (CE-CT) represents the examination of choice in polytraumatized hemodynamically stable patients, as it allows a comprehensive evaluation of vascular, parenchymal, bone and soft tissues injuries, but the diagnosis of any biliary leaks is limited to the evaluation of nonspecific imaging findings and on findings evolution in the follow-up, such as the progressive growth of fluid collections. Furthermore, biliary complications, such as the occurrence of biloma or biliary peritonitis, may become manifest several days after the initial trauma, often with unspecific progressive signs and symptoms. Although CT and ultrasonography can suggest bile leaks based on several nonspecific imaging findings (e.g., fluid collections), magnetic resonance imaging (MRI) using hepatobiliary contrast agents helps to identify the site and entity of post-traumatic biliary disruption. Indeed, MRI allows to obtain cholangiographic sequences that may show post-traumatic active bile leakage and cysto-biliary communications by direct visualisation of contrast material extravasation into fluid collections, increasing the preoperative accuracy of NI-TBIs. Few data are available about MRI use in the follow-up of trauma with NI-TBI management. So, in the present mini review, its role is reviewed and our preliminary experience in this field is reported.

Incidental Focal Spleen Lesions: Integrated Imaging and Pattern Recognition Approach to the Differential Diagnosis

Spleen lesions and pseudolesions, detected incidentally in imaging, are not uncommon and may require further work-up. The imaging appearance of focal splenic lesions (FSLs) may not be pathognomonic, because of considerably overlapping features. Consequently, all imaging techniques lack specificity to fully characterize FSLs. Clinical correlation is mandatory, so as, first of all, to categorize the patient as having or not having a history of solid or hematologic malignancy. Nowadays, many patients have old imaging studies available for comparison and, consequently, it is important to understand if the lesion was previously present or not, and if the size is the same or has changed. In the absence of comparison studies, and with a lack of imaging features of benignity, further investigation may be necessary, using PET, biopsy, or short-term follow-up. Some algorithms have been proposed to manage incidental FSLs; however, none of these strategies has been validated by prospective studies to date. In this review we illustrate the topic of incidental FSLs and we analyze a number of published algorithms.

Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3.5, Perplexity and Bing

Structured reporting may improve the radiological workflow and communication among physicians. Artificial intelligence applications in medicine are growing fast. Large language models (LLMs) are recently gaining importance as valuable tools in radiology and are currently being tested for the critical task of structured reporting. We compared four LLMs models in terms of knowledge on structured reporting and templates proposal. LLMs hold a great potential for generating structured reports in radiology but additional formal validations are needed on this topic.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

Dose Reduction Strategies for Pregnant Women in Emergency Settings

In modern clinical practice, there is an increasing dependence on imaging techniques in several settings, and especially during emergencies. Consequently, there has been an increase in the frequency of imaging examinations and thus also an increased risk of radiation exposure. In this context, a critical phase is a woman's pregnancy management that requires a proper diagnostic assessment to reduce radiation risk to the fetus and mother. The risk is greatest during the first phases of pregnancy at the time of organogenesis. Therefore, the principles of radiation protection should guide the multidisciplinary team. Although diagnostic tools that do not employ ionizing radiation, such as ultrasound (US) and magnetic resonance imaging (MRI) should be preferred, in several settings as polytrauma, computed tomography (CT) nonetheless remains the examination to perform, beyond the fetus risk. In addition, protocol optimization, using dose-limiting protocols and avoiding multiple acquisitions, is a critical point that makes it possible to reduce risks. The purpose of this review is to provide a critical evaluation of emergency conditions, e.g., abdominal pain and trauma, considering the different diagnostic tools that should be used as study protocols in order to control the dose to the pregnant woman and fetus.