Pulmonary lobectomy is the gold standard intervention for lung cancer removal and consists of the complete resection of the affected lung lobe, which, coupled with the re-adaptation of the remaining thoracic structures, decreases the postoperative pulmonary function of the patient. Current clinical practice, based on spirometry and cardiopulmonary exercise tests, does not consider local changes, providing an average at-the-mouth estimation of residual functionality. Computational Fluid Dynamics (CFD) has proved a valuable solution to obtain quantitative and local information about airways airflow dynamics. A CFD investigation was performed on the airway tree of a leftupper pulmonary lobectomy patient, to quantify the effects of the postoperative alterations. The patient-specific bronchial models were reconstructed from pre-and postoperative CT scans. A parametric laryngeal model was merged to the geometries to account for physiological-like inlet conditions. Numerical simulations were performed in Fluent. The postoperative configuration revealed fluid dynamic variations in terms of global velocity (+23%), wall pressure (+48%), and wall shear stress (+39%). Local flow disturbances emerged at the resection site: a high-velocity peak of 4.92 m/s was found at the left-lower lobe entrance, with a local increase of pressure at the suture zone (18 Pa). The magnitude of pressure and secondary flows increased in the trachea and flow dynamics variations were observed also in the contralateral lung, causing altered lobar ventilation. The results confirmed that CFD is a patient-specific approach for a quantitative evaluation of fluid dynamics parameters and local ventilation providing additional information with respect to current clinical approaches. K E Y W O R D Scomputational fluid dynamics, imaged-based, pulmonary lobectomy, tracheobronchial tree modeling Marta Tullio and Lorenzo Aliboni equally contributed to this study.

The paper deals with the evaluation of the performance of an existing and previously validated CT based radiomic signature, developed in oropharyngeal cancer to predict human papillomavirus (HPV) status, in the context of anal cancer. For the validation in anal cancer, a dataset of 59 patients coming from two different centers was collected. The primary endpoint was HPV status according to p16 immunohistochemistry. Predefined statistical tests were performed to evaluate the performance of the model. The AUC obtained here in anal cancer is 0.68 [95% CI (0.32–1.00)] with F1 score of 0.78. This signature is TRIPOD level 4 (57%) with an RQS of 61%. This study provides proof of concept that this radiomic signature has the potential to identify a clinically relevant molecular phenotype (i.e., the HPV-ness) across multiple cancers and demonstrates potential for this radiomic signature as a CT imaging biomarker of p16 status.

Purpose: Bronchiectasis is a chronic disease characterized by an irreversible dilatation of bronchi leading to chronic infection, airway inflammation, and progressive lung damage. Three specific patterns of bronchiectasis are distinguished in clinical practice: cylindrical, varicose, and cystic. The predominance and the extension of the type of bronchiectasis provide important clinical information. However, characterization is often challenging and is subject to high interobserver variability. The aim of this study is to provide an automatic tool for the detection and classification of bronchiectasis through convolutional neural networks.Materials and Methods: Two distinct approaches were adopted: (i) direct network performing a multilabel classification of 32×32 regions of interest (ROIs) into 4 classes: healthy, cylindrical, cystic, and varicose and (ii) a 2-network serial approach, where the first network performed a binary classification between normal tissue and bronchiectasis and the second one classified the ROIs containing abnormal bronchi into one of the 3 bronchiectasis typologies. Performances of the networks were compared with other architectures presented in the literature.Results: Computed tomography from healthy individuals (n = 9, age = 47 ± 6, FEV 1 %pred = 109 ± 17, FVC%pred = 116 ± 17) and bronchiectasis patients (n = 21, age = 59 ± 15, FEV 1 %pred = 74 ± 25, FVC%pred = 91 ± 22) were collected. A total of 19,059 manually selected ROIs were used for training and testing. The serial approach provided the best results with an accuracy and F1 score average of 0.84, respectively. Slightly lower performances were observed for the direct network (accuracy = 0.81 and F1 score average = 0.82). On the test set, cylindrical bronchiectasis was the subtype classified with highest accuracy, while most of the misclassifications were related to the varicose pattern, mainly to the cylindrical class. Conclusion:The developed networks accurately detect and classify bronchiectasis disease, allowing to collect quantitative information regarding the radiologic severity and the topographical distribution of bronchiectasis subtype.

No abstract

Pulmonary lobectomy, which consists of the partial or complete resection of a lung lobe, is the gold standard intervention for lung cancer removal. The removal of functional tissue during the surgery and the re-adaptation of the remaining thoracic structures decrease the patient's post-operative pulmonary function. Residual functionality is evaluated through pulmonary function tests, which account for the number of resected segments without considering local structural alterations and provide an average at-the-mouth estimation. Computational Fluid Dynamics (CFD) has been demonstrated to provide patient-specific, quantitative, and local information about airways airflow dynamics. A CFD investigation was performed on image-based airway trees reconstructed before and after the surgery for twelve patients who underwent lobectomy at different lobes. The geometrical alterations and the variations in fluid dynamics parameters and in lobar ventilation between the pre and post-operative conditions were evaluated. The post-operative function was estimated and compared with current clinical algorithms and with actual clinical data. The post-operative configuration revealed a high intersubject variability: regardless of the lobectomy site, an increment of global velocity, wall pressure, and wall shear stress was observed. Local flow disturbances also emerged at, and downstream of, the resection site. The analysis of lobar ventilation showed severe variations in the volume flow rate distribution, highlighting the compensatory effects in the contralateral lung with an increment of inflow. The estimation of post-operative function through CFD was comparable with the current clinical algorithm and the actual spirometric measurements. The results confirmed that CFD could provide additional information to support the current clinical approaches both in the operability assessment and in the prescription of personalized respiratory rehabilitation.