Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

BACKGROUND No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. METHODS Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. RESULTS Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). CONCLUSIONS Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models

Quality assurance for automatically generated contours with additional deep learning

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior

Diabetic ketoacidosis at the onset of disease during a national awareness campaign: a 2-year observational study in children aged 0-18 years

After a previous survey on the incidence of diabetic ketoacidosis (DKA) at onset of type 1 diabetes in children in 2013-2014 in Italy, we aimed to verify a possible decline in the incidence of DKA at onset during a national prevention campaign

Pulmonary Arteriovenous Malformations (PAVMs) and Pregnancy: A Rare Case of Hemothorax and Review of the Literature

Pulmonary arteriovenous malformations (PAVMs) are anatomical abnormalities consisting in a direct connection between pulmonary arteries and veins. Most of PAVMs are related to Hereditary Hemorrhagic Teleangiectasia, whereas only 10 to 20% are isolated sporadic cases. PAVMs tend to increase in size naturally; however, several factors can influence their growth such as pulmonary arterial hypertension, puberty, and pregnancy. Clinical manifestations are related to the right-to-left shunting and include dyspnoea, hypoxia, and pulmonary hypertension. The presence of PAVMs during pregnancy is associated with an increased risk of complications such as their rupture, haemothorax, and hypovolemic shock. The treatment reserved to PAVMs was the surgical resection of the lung lobe involving the malformation. Due to the worldwide acceptance of endovascular technique, the transcatheter embolization (TCE) is today considered as the mainstay of treatment. Recent studies reported the safeness of the TCE during pregnancy if performed by an experienced radiologist, at second or third trimesters when radiation exposure is believed to have minimal effect on the foetus. However, although the TCE during pregnancy represents an option, the treatment prior to pregnancy has to be considered the auspicial solution. Our study reports the case of a dyspnoeic pregnant woman with unknown pAVM causing hemothorax and simultaneously treated for pAVM reparation, left lower lobe resection, and hysterectomy. Postoperative treatment of embolization was performed to definitively close the pAVM

La Grotta dei Personaggi di Montevago (AG), una nuova segnalazione di cavità ipogenica in Sicilia

La Grotta dei Personaggi è localizzata in Sicilia occidentale, nei pressi di Monte Magaggiaro, a S del centro abitato di Montevago (AG). Nell’area sono presenti sorgenti termali, caratterizzate da acque cloro-solfate alcalino-terrose con temperatura di circa 40 °C e pH 7. La Grotta dei Personaggi, conosciuta già dai primi anni del 1900 e nota anche per ritrovamenti archeologici, non era mai stata dettagliatamente rilevata e studiata. La cavità si sviluppa prevalentemente in calcari di piattaforma della formazione Inici (Giurassico inf.) e in calcari di scarpata e pelagici della formazione Buccheri (Giurassico inf.-sup.). Si tratta di una cavità sub-orizzontale che presenta uno sviluppo di circa 1,7 km, un dislivello ascendente di 15 m e discendente di 32 m. Il pattern dei condotti è labirintico e strettamente influenzato dalla struttura geologica; non ci sono veri e propri pozzi, ma soltanto fratture che si restringono in profondità; i rami ascendenti sono caratterizzati da cupole che si compenetrano verso l’alto. Tra le altre morfologie si riconoscono buchi da stillicidio, canali di condensazionecorrosione, sfere di condensazione, bocche alimentatrici, pilastri e tramezzi. All’interno della cavità sono presenti anche una colonia di chirotteri e diversi depositi di minerali ancora in fase di studio. L’analisi delle morfologie a grande e media scala e la presenza di acque termali nell'area fanno ipotizzare che la genesi di questa cavità sia legata a processi ipogenici