Purpose: To develop quantitative molecular imaging biomarkers of immune-related adverse event (irAE) development in malignant melanoma (MM) patients receiving immune-checkpoint inhibitors (ICI) imaged with ¹⁸F-FDG PET/CT. Methods: ¹⁸F-FDG PET/CT images of 58 MM patients treated with anti-PD-1 or anti-CTLA-4 ICI were retrospectively analyzed for indication of irAE. Three target organs, most commonly affected by irAE, were considered: bowel, lung, and thyroid. Patient charts were reviewed to identify which patients experienced irAE, irAE grade, and time to irAE diagnosis. Target organs were segmented using a convolutional neural network (CNN), and novel quantitative imaging biomarkers — SUV percentiles (SUV_X%) of ¹⁸F-FDG uptake within the target organs — were correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. Patients who did not experience irAE were used to establish normal ranges for target organ ¹⁸F-FDG uptake. Results: A total of 31% (18/58) patients experienced irAE in the three target organs: bowel (n=6), lung (n=5), and thyroid (n=9). Optimal percentiles for identifying irAE were bowel (SUV_95%, AUROC=0.79), lung (SUV_95%, AUROC=0.98), and thyroid (SUV_75%, AUROC=0.88). Optimal cut-offs for irAE detection were bowel (SUV_95%>2.7 g/mL), lung (SUV_95%>1.7 g/mL), and thyroid (SUV_75%>2.1 g/mL). Normal ranges (95% confidence interval) for the SUV percentiles in patients without irAE were bowel [1.74, 2.86 g/mL], lung [0.73, 1.46 g/mL], and thyroid [0.86, 1.99 g/mL]. Conclusions: Increased ¹⁸F-FDG uptake within irAE-affected organs provides predictive information about the development of irAE in MM patients receiving ICI and represents a potential quantitative imaging biomarker for irAE. Some irAE can be detected on ¹⁸F-FDG PET/CT well before clinical symptoms appear.

The evaluation of treatment response remains a challenge in glioma cases because the neuro oncological therapy can lead to the development of treatment-related changes (TRC) that mimic true progression (TP). Positron emission tomography (PET) using O-(2-[¹⁸F] fluoroethyl-)-L-tyrosine (¹⁸F-FET) has been shown to be a useful tool for detecting TRC and TP. We assessed the diagnostic performance of different ¹⁸F-FET PET segmentation approaches and different imaging biomarkers for differentiation between late TRC and TP in glioma patients. Isocitrate dehydrogenase (IDH) status was evaluated as a predictor of disease outcome. In our study, the proportion of TRC in IDH wild type (IDHwt) and IDH mutant (IDHm) subgroups was without significant difference. We found that the diagnostic value of static and dynamic biomarkers of ¹⁸F-FET PET for discrimination between TRC and TP depends on the IDH mutation status of the tumor. Dynamic ¹⁸F-FET PET acquisition proved helpful in the IDH wild type (IDHwt) subgroup, as opposed to the IDH mutant (IDHm) subgroup, providing an early indication to discontinue dynamic imaging in the IDHm subgroup.

Simulation indicates that PET image could be improved by upgrading a conventional ring with a probe placed close to the imaged object. In this paper, timing issues related to a PET probe using high-resistivity silicon as a detector material are addressed. The final probe will consist of several (four to eight) 1-mm thick layers of silicon detectors, segmented into 1 × 1 mm² pads, each pad equivalent to an independent p + nn+ diode. A proper matching of events in silicon with events of the external ring can be achieved with a good timing resolution. To estimate the timing performance, measurements were performed on a simplified model probe, consisting of a single 1-mm thick detector with 256 square pads (1.4 mm side), coupled with two VATAGP7s, application-specific integrated circuits. The detector material and electronics are the same that will be used for the final probe. The model was exposed to 511 keV annihilation photons from an ²²Na source, and a scintillator (LYSO)–PMT assembly was used as a timing reference. Results were compared with the simulation, consisting of four parts: (i) GEANT4 implemented realistic tracking of electrons excited by annihilation photon interactions in silicon, (ii) calculation of propagation of secondary ionisation (electron–hole pairs) in the sensor, (iii) estimation of the shape of the current pulse induced on surface electrodes and (iv) simulation of the first electronics stage. A very good agreement between the simulation and the measurements were found. Both indicate reliable performance of the final probe at timing windows down to 20 ns.

A feature-centric explainable artificial intelligence pipeline showed that the Mirai model uses lesion features on mammograms for lesion detection and breast cancer risk prediction. A simplified model using only calcification features achieved comparable risk prediction performance to the original Mirai model in a screen-negative population.

Objectives: This study investigated the performance and robustness of radiomics in predicting COVID-19 severity in a large public cohort. Methods: A public dataset of 1110 COVID-19 patients (1 CT/patient) was used. Using CTs and clinical data, each patient was classified into mild, moderate, and severe by two observers: (1) dataset provider and (2) a board-certified radiologist. For each CT, 107 radiomic features were extracted. The dataset was randomly divided into a training (60%) and holdout validation (40%) set. During training, features were selected and combined into a logistic regression model for predicting severe cases from mild and moderate cases. The models were trained and validated on the classifications by both observers. AUC quantified the predictive power of models. To determine model robustness, the trained models was cross-validated on the inter-observer’s classifications. Results: A single feature alone was sufficient to predict mild from severe COVID-19 with and (p<< 0.01). The most predictive features were the distribution of small size-zones (GLSZM-SmallAreaEmphasis) for provider’s classification and linear dependency of neighboring voxels (GLCM-Correlation) for radiologist’s classification. Cross-validation showed that both . In predicting moderate from severe COVID-19 , first-order-Median alone had sufficient predictive power of . For radiologist’s classification, the predictive power of the model increased to as the number of features grew from 1 to 5. Cross-validation yielded and . Conclusions: Radiomics significantly predicted different levels of COVID-19 severity. The prediction was moderately sensitive to inter-observer classifications, and thus need to be used with caution. Key points: Interpretable radiomic features can predict different levels of COVID-19 severity Machine Learning-based radiomic models were moderately sensitive to inter-observer classifications, and thus need to be used with caution