Artificial intelligence-driven volumetric CT outcome score in cystic fibrosis: longitudinal and multicenter validation with/without modulators treatment.

OBJECTIVES: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations. MATERIALS AND METHODS: The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score. RESULTS: A total of 139 CF patients (median age, 15 years [interquartile range: 13-25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99. CONCLUSION: NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI. CLINICAL RELEVANCE STATEMENT: Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials. KEY POINTS: Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF). AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments. AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.

CT venography for the diagnosis of postpartum venous thromboembolism: a prospective multi-center cohort study.

OBJECTIVES: To assess the role of CT venography (CTV) in the diagnosis of venous thromboembolism (VTE) during the postpartum period. MATERIALS AND METHODS: This multicenter prospective cohort study was conducted between April 2016 and April 2020 in 14 university hospitals. All women referred for CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE) within the first 6 weeks postpartum were eligible. All CTPAs were performed on multidetector CT machines with the usual parameters and followed by CTV of the abdomen, pelvis, and proximal lower limbs. On-site reports were compared to expert consensus reading, and the added value of CTV was assessed for both. RESULTS: The final study population consisted of 123 women. On-site CTPA reports mentioned PE in seven women (7/123, 5.7%), all confirmed following expert consensus reading, three involving proximal pulmonary arteries and four limited to distal arteries. Positive CTV was reported on-site in nine women, five of whom had negative and two indeterminate CTPAs, bringing the VTE detection rate to 11.4% (14/123) (95%CI: 6.4-18.4, p = 0.03). Expert consensus reading confirmed all positive on-site CTV results, but detected a periuterine vein thrombosis in an additional woman who had a negative CTPA, increasing the VTE detection rate to 12.2% (15/123) (95%CI: 7.0-19.3, p = 0.008). Follow-up at 3 months revealed no adverse events in this woman, who was left untreated. Median Dose-Length-Product was 117 mGy.cm for CTPA and 675 mGy.cm for CTPA + CTV. CONCLUSION: Performing CTV in women suspected of postpartum PE doubles the detection of venous thromboembolism, at the cost of increased radiation exposure. CLINICAL RELEVANCE STATEMENT: CTV can help in the decision-making process concerning curative anticoagulation in women with suspected postpartum PE, particularly those whose CTPA results are indeterminate or whose PE is limited to the subsegmental level. KEY POINTS: Postpartum women are at risk of pulmonary embolism, and CT pulmonary angiography can give equivocal results. CT venography (CTV) positivity increased the venous thromboembolism detection rate from 5.7 to 11.4%. CTV may help clinical decision-making, especially in women with indeterminate CTPA results or subsegmental emboli.

Different pathways to lung cancer diagnosis in a real-life setting.

BACKGROUND: Most lung cancers are diagnosed at an advanced stage and therefore have a poor prognosis. One major challenge is to choose the most adapted sampling technique to obtain a rapid pathological diagnosis so as to start treatment as early as possible. A growing number of techniques have been developed in recent years. This study sought to assess the diagnostic efficiency of each, along with the respective duration of the diagnostic pathways. METHODS: This retrospective, bicentric, observational study enrolled patients with inoperable lung cancer (stage III or IV) diagnosed in 2018-2019. Diagnostic efficiency was assessed based on the different examinations performed to achieve a precise diagnosis (pathology, immunohistochemistry, and/or molecular biology). The time between the first medical contact and treatment initiation was also assessed. RESULTS: Overall, 625 patients were included (median age 67 years; men 67 %; adenocarcinoma 55 %). The most frequent examinations were bronchial endoscopy (n = 469, 75 %), followed by metastasis biopsy (n = 137, 21.9 %) and guided transthoracic core-needle biopsy (TCNB) (n = 116, 18.6 %). 372 patients had only one procedure (59.5 %), mainly bronchial endoscopy (n = 217, 34.7 %) and metastasis biopsy (n = 71, 11 %). The most efficient examination was thoracic surgery (surgical pleural biopsy, (n = 32, 100 %); mediastinoscopy (n = 26, 96.3 %); surgical pulmonary biopsy (n = 14, 93.3 %). The second most efficient examination was metastasis biopsy (n = 126, 94 %) followed by guided TCNB (n = 108, 93.1 %). The median time from first medical contact to first examination was 4 days (interquartile range 25 %-75 % 1-8). The median time from first medical contact to pathological result was 17 days (10-34). The median time from first medical contact to treatment start was 48 days (30-69). CONCLUSIONS: In order to make an accurate and rapid diagnosis of lung cancer, it is crucial to choose the most appropriate technique. Bronchial endoscopy remains the first-line examination for central lesions, as it is efficient and easily accessible. Guided TCNB and metastasis biopsy are the preferred techniques for peripheral lesions. The choice of the diagnostic technique should be part of a multidisciplinary approach and a dedicated pathway to optimize initial management.

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.

PURPOSE: The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations. MATERIALS AND METHODS: Using a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio. RESULTS: Quantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850-0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810-0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668-0.760) and of 0.723 (95% CI: 0.668-0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set. CONCLUSION: This study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.