Oligometastatic disease (OMD) is an emerging state of disease with limited metastatic tumor burden. It should be distinguished from polymetastatic disease due the potential curative therapeutic options of OMD. Imaging plays a pivotal role in the diagnosis and follow-up of patients with OMD. The imaging tools needed in the case of OMD will differ according to different parameters, which include primary tumor type, timing between measurement and treatment, potential metastatic location and the patient’s individual risk for metastasis. In this article, OMD is defined and the use of different imaging modalities in several oncologic situations are described in order to better understand OMD and its specific implication for radiologists.

Mutations in isocitrate dehydrogenase (IDH) represent an independent predictor of better survival in patients with gliomas. We aimed to assess grade and IDH mutation status in patients with untreated gliomas, by evaluating the respective value of ¹⁸F-FET PET/CT via dynamic and texture analyses. A total of 73 patients (male: 48, median age: 47) who underwent an ¹⁸F-FET PET/CT for initial glioma evaluation were retrospectively included. IDH status was available in 61 patients (20 patients with WHO grade 2 gliomas, 41 with grade 3–4 gliomas). Time–activity curve type and 20 parameters obtained from static analysis using LIFEx© v6.30 software were recorded. Respective performance was assessed using receiver operating characteristic curve analysis and stepwise multivariate regression analysis adjusted for patients’ age and sex. The time–activity curve type and texture parameters derived from the static parameters showed satisfactory-to-good performance in predicting glioma grade and IDH status. Both time–activity curve type (stepwise OR: 101.6 (95% CI: 5.76–1791), p = 0.002) and NGLDM coarseness (stepwise OR: 2.08 × 10⁴³ (95% CI: 2.76 × 10¹²–1.57 × 10⁷⁴), p = 0.006) were independent predictors of glioma grade. No independent predictor of IDH status was found. Dynamic and texture analyses of ¹⁸F-FET PET/CT have limited predictive value for IDH status when adjusted for confounding factors. However, they both help predict glioma grade.

The diagnosis of interstitial lung disease may be challenging, especially in atypical disease. Various factors must be considered when performing and reading a chest CT examination for interstitial lung disease, because each of them may represent a source of misinterpretation. Firstly, technical aspects must be mastered, including acquisition and reconstruction parameters as well as post-processing. Secondly, mistakes in interpretation related to the inaccurate description of predominant features, potentially leading to false-positive findings, as well as satisfaction of search must be avoided. In all cases, clinical context, coexisting chest abnormalities and previous examinations must be integrated into the analysis to suggest the most appropriate differential diagnosis.

Background: Pathological complete response (pCR) serves as a prognostic surrogate endpoint for long-term clinical outcomes in breast cancer patients receiving neoadjuvant systemic therapy (NAST). This study aims to develop and evaluate machine learning-based biomarkers for predicting pCR and recurrence-free survival (RFS). Methods: This retrospective monocentric study included 235 women (mean age 46 ± 11 years) with non-metastatic breast cancer treated with NAST. We developed various machine learning models using clinical features (age, genetic mutations, TNM stage, hormonal receptor expression, HER2 status, and histological grade), along with morphological features (size, T2 signal, and surrounding edema) and radiomics data extracted from pre-treatment MRI. Patients were divided into training and test groups with different MRI models. A customized machine learning pipeline was implemented to handle these diverse data types, consisting of feature selection and classification components. Results: The models demonstrated superior prediction ability using radiomics features, with the best model achieving an AUC of 0.72. Subgroup analysis revealed optimal performance in triple-negative breast cancer (AUC of 0.80) and HER2-positive subgroups (AUC of 0.65). Conclusion: Machine learning models incorporating clinical, qualitative, and radiomics data from pre-treatment MRI can effectively predict pCR in breast cancer patients receiving NAST, particularly among triple-negative and HER2-positive breast cancer subgroups.

Background Gliomas are the most frequent malignant brain tumors and are heterogeneous in histology, genetics, and outcome. Mutations in isocitrate dehydrogenase (IDH) have been described by several studies as an independent predictor of better survival. We aimed at evaluating the respective value of O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (¹⁸F-FET) PET/CT dynamic and texture analysis in patients with untreated glioma to assess grade and IDH mutation status. Methods In total, 73 patients (male: 48, median age: 47 [36–59]) who underwent an ¹⁸F-FET PET/CT for initial glioma evaluation were retrospectively included. Twenty patients had a WHO grade II and forty-one a grade III-IV glioma. IDH status was available in 61 patients. Time-activity-curve (TAC) type and 20 parameters (conventional, texture, shape and histogram derived) obtained from static analysis using the LIFEx© software were recorded. Respective performance was assessed using receiver operating characteristic (ROC) curve analysis and stepwise multivariate regression analysis. Results TAC type and several texture parameters derived from static parameters showed satisfactory to good performance to predict glioma grade and IDH status. Both TAC type (stepwise OR: 28.8 [95%CI: 4.73-175.14], p < 0.001) and NGLDM coarseness (stepwise OR: 9.7 x 10¹⁹ [95%CI: 1.47–6.5 x 10³⁹], p = 0.048) were independent predictors of glioma grade. However, only TAC type was an independent predictor of IDH status (OR: 3.74 [95%CI: 1.17-12.0], p = 0.026). Conclusions ¹⁸F-FET PET/CT dynamic and texture analysis may help predict both glioma grade and IDH status, especially in low-grade tumors.

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