Optical method to determine in vivo capillary hematocrit, hemoglobin concentration, and 3-D network geometry in skeletal muscle.

OBJECTIVE: The aim of this study was to develop a tool to visualize and quantify hemodynamic information, such as hemoglobin concentration and hematocrit, within microvascular networks recorded in vivo using intravital video microscopy. Additionally, we aimed to facilitate the 3-D reconstruction of the microvascular networks. METHODS: Digital images taken from an intravital video microscopy preparation of the extensor digitorum longus muscle in rats for 25 capillary segments were used. The developed algorithm was used to delineate capillaries of interest, calculate the optical density for each pixel in the image, and reconstruct the 3-D capillary geometry using the calculated light path-lengths. Subsequently, the mean corpuscular hemoglobin concentration (MCHC), hemoglobin concentration, and hematocrit for these capillaries were calculated. We evaluated the hematocrit values determined by our methodology by comparing them to those obtained using a previously published method. RESULTS: The hematocrit values from the proposed optical method were strongly correlated with those calculated using published methods r2 (25) = .92, p < .001, and demonstrated excellent agreement with a mean difference of 1.3% and a coefficient of variation (CV) of 11%. The average MCHC, hemoglobin concentration, and light path-lengths were 23.83 g/dl, 8.06 g/dl, and 3.92 µm, respectively. CONCLUSION: The proposed methodology can quantify hemodynamic measurements and produce functional images for visualization of the microcirculation in vivo.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making.

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)-based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests.

Purpose: We developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC). Approach: We retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort ( n = 101 ), validated in the testing cohort ( n = 34 ) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan-Meier analysis. Results: The model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 ( p < 0.005 ) and 0.60 ( p = 0.008 ), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training ( p < 0.005 ) and the testing ( p = 0.03 ) cohorts. Conclusions: Our radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.