For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient’s 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.

BACKGROUND AND PURPOSE: Despite increasing demand for fluoroscopy-guided lumbar puncture (FG-LP), there is limited quantitative and epidemiological data on patients undergoing this procedure. Additionally, data are scarce on the correlation of iliac crest landmarks to the actual anatomical lumbar level (intercristal line). The aim of this study is to determine if (1) body mass index (BMI) correlates with skin to spinal canal distance (SCD) and (2) the iliac crest landmark correlates with the presumed anatomical landmark on cross-sectional imaging. METHODS: In this retrospective, single-center IRB-approved study, we assessed 495 patients who underwent FG-LP and had lumbar computed tomography/magnetic resonance imaging within 6 months of presentation. SCD was measured on the sagittal view at the L3-L4, L4-L5, and L5-S1 intervertebral levels. RESULTS: In our cohort of 495 adults (mean age ± standard deviation [SD], 53.2 ± 16.4 years), there was a statistically significant linear correlation between BMI and SCD at each intervertebral level. Mean ± SD (R 2) SCD at L3-4, L4-5, and L5-S1 was 6.7 ± 1.6 cm (.5486), 7.4 ± 1.9 cm (.5894), and 7.8 ± 1.9 cm (.5968), respectively. The intercristal line aligned with L3-L4, L4-L5, and L5-S1 in 2.1%, 72.4%, and 6.2% of patients, respectively. CONCLUSIONS: There was direct, positive linear correlation between BMI and SCD at clinically relevant lumbar disc levels. Furthermore, there is considerable anatomical variance in the intervertebral space that aligns with the superior aspect of the iliac crest.

No abstract

No abstract

BACKGROUND: The Knosp criteria have been the historical standard for predicting cavernous sinus invasion, and therefore extent of surgical resection, of pituitary macroadenomas. Few studies have sought to reappraise the utility of this tool after recent advances in visualization and modeling of tumors in complex endoscopic surgery. OBJECTIVE: To evaluate our proposed alternative method, using 3-dimensional (3D) volumetric imaging, and whether it can better predict extent of resection in nonfunctional pituitary adenomas. METHODS: Patients who underwent endoscopic transsphenoidal resection of pituitary macroadenomas at our institution were reviewed. Information was collected on neurological, endocrine, and visual function. Volumetric segmentation was performed using 3D Slicer software. Relationship of tumor volume, clinical features, and Knosp grade on extent of resection was examined. RESULTS: One hundred forty patients were identified who had transsphenoidal resection of nonfunctional pituitary adenomas. Macroadenomas had a median volume of 6 cm 3 (IQR 3.4-8.7), and 17% had a unilateral Knosp grade of at least 3B. On multiple logistic regression, only smaller log-transformed preoperative tumor volume was independently associated with increased odds of gross total resection (GTR; odds ratio: 0.27, 95% CI: 0.07-0.89, P < .05) when controlling for tumor proliferative status, age, and sex (area under the curve 0.67). The Knosp criteria did not independently predict GTR in this cohort (P > .05, area under the curve 0.46). CONCLUSION: Increasing use of volumetric 3D imaging may better anticipate extent of resection compared with the Knosp grade metric and may have a greater positive predictive value for GTR. More research is needed to validate these findings and implement them using automated methods.