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Objective Intracerebral hemorrhage (ICH) volume is a strong predictor of outcome in patients presenting with acute hemorrhagic stroke. It is necessary to segment the hematoma for ICH volume estimation and for computerized extraction of features, such as spot sign, texture parameters, or extravasated iodine content at dual-energy computed tomography. Manual and semiautomatic segmentation methods to delineate the hematoma are tedious, user dependent, and require trained personnel. This article presents a convolutional neural network to automatically delineate ICH from noncontrast computed tomography scans of the head. Methods A model combining a U-Net architecture with a masked loss function was trained on standard noncontrast computed tomography images that were down sampled to 256 × 256 size. Data augmentation was applied to prevent overfitting, and the loss score was calculated using the soft Dice loss function. The Dice coefficient and the Hausdorff distance were computed to quantitatively evaluate the segmentation performance of the model, together with the sensitivity and specificity to determine the ICH detection accuracy. Results The results demonstrate a median Dice coefficient of 75.9% and Hausdorff distance of 2.65 pixels in segmentation performance, with a detection sensitivity of 77.0% and specificity of 96.2%. Conclusions The proposed masked loss U-Net is accurate in the automatic segmentation of ICH. Future research should focus on increasing the detection sensitivity of the model and comparing its performance with other model architectures.

Objective To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. Methods Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders’ assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. Results From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, −0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0–30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. Conclusions The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making. Key Points • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, −0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.

RECIST is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection. Disagreement among readers, and between readers and total tumor burden was analyzed, as a function of the total number of lesions, affected organs, and lesion growth. Disagreement aggravates when the number of lesions increases, when lesions are concentrated on few organs, and when lesion growth borders the thresholds of progressive disease and partial response. An intrinsic methodological error is observed in the estimation of total tumor burden (TTB) via RECIST. In a metastatic setting, RECIST displays a non-linear, unpredictable behavior. Our results demonstrate that RECIST can deliver an accurate estimate of total tumor burden in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the “selection of the largest lesions”, which introduce a bias that makes it hardly possible to perform an accurate estimate of the total tumor burden. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.