Visible Lung Research Articles

Knee-to-chest flexion manoeuvre to reduce respiratory distress after planned caesarean birth: a feasibility study

BackgroundPlanned caesarean section (CS) is a risk factor for neonatal respiratory distress caused by a greater volume of airway liquid in the absence of uterine contractions.Performing a newly conceptualised knee-to-chest...

Device-Less Data-Driven Cardiac and Respiratory Gating Using LAFOV PET Histo Images.

Background: The outstanding capabilities of modern Positron Emission Tomography (PET) to highlight small tumor lesions and provide pathological function assessment are at peril from image quality degradation caused by respiratory and cardiac motion. However, the advent of the long axial field-of-view (LAFOV) scanners with increased sensitivity, alongside the precise time-of-flight (TOF) of modern PET systems, enables the acquisition of ultrafast time resolution images, which can be used for estimating and correcting the cyclic motion. Methods: 0.25 s so-called [18F]FDG PET histo image series were generated in the scope of for detecting respiratory and cardiac frequency estimates applicable for performing device-less data-driven gated image reconstructions. The frequencies of the cardiac and respiratory motion were estimated for 18 patients using Short Time Fourier Transform (STFT) with 20 s and 30 s window segments, respectively. Results: The Fourier analysis provided time points usable as input to the gated reconstruction based on eight equally spaced time gates. The cardiac investigations showed estimates in accordance with the measured pulse oximeter references (p = 0.97) and a mean absolute difference of 0.4 ± 0.3 beats per minute (bpm). The respiratory frequencies were within the expected range of 10-20 respirations per minute (rpm) in 16 out of 18 patients. Using this setup, the analysis of three patients with visible lung tumors showed an increase in tumor SUVmax and a decrease in tumor volume compared to the non-gated reconstructed image. Conclusions: The method can provide signals that were applicable for gated reconstruction of both cardiac and respiratory motion, providing a potential increased diagnostic accuracy.

Effect of forceps biopsy on bronchial washing results in patients with endoscopically visible lung tumors

Effect of forceps biopsy on bronchial washing results in patients with endoscopically visible lung tumors

Unleashing deep features and radiomics to enhance best overall response prediction to immunotherapy in advanced non-small cell lung cancer.

e20611 Background: Predicting immunotherapy response in advanced non-small cell lung cancer (NSCLC) through non-invasive means could be a groundbreaking advancement. Deep features extracted from medical images by deep Convolutional Neural Networks (CNNs) along with radiomics have demonstrated promising results in different oncologic diseases. However, the method for extracting these deep features influences the predictive capabilities of the model. Methods: A retrospective analysis of a multicenter study was conducted in advanced NSCLC patients with high PD-L1 expression with the aim of developing an imaging features-based model for the prediction of best overall response (BOR) to a checkpoint inhibitor immunotherapy. All visible lung lesions, beyond RECIST 1.1, were manually segmented in baseline CT scans. Radiomics features were extracted at lesion-level, and various preprocessing methods and CNN architectures were tested for deep features extraction. Radiomics and deep features were harmonized using ComBat methodology. A distinctive feature signature was created for each patient by calculating the mean across all lesions. Using this data, machine learning models were trained to predict treatment response, specifically distinguishing between complete response (CR) and partial response (PR) from stable disease (SD) and progressive disease (PD). The study compared the performance of different deep features extraction methods and assessed shapley additive explanations (SHAP) values of radiomics for interpretability. Results: In the cohort of 116 patients (mean age: 67, 93% ever-smokers, 73% male), 1024 to 5475 quantitative imaging features per lesion were extracted. Among these patients, 67 patients were CR+PR, and 49 were SD+PD. The top-performing predictive model, trained with radiomics and deep features from a deep convolutional autoencoder, achieved an average AUC of 85%. SHAP values showed that impactful radiomics features were correlated with intensity changes in adjacent pixels describing the tumor heterogeneity, along with shape features potentially associated with malignancy. Ultimately, combining both features demonstrated statistical significance, leading to a performance improvement of ~2-5% compared to models exclusively using deep features. Conclusions: Diverse techniques for extracting deep features were evaluated, revealing autoencoder training as the most effective strategy for predicting immunotherapy response. Consequently, the findings suggest that employing deep learning to extract imaging features from baseline CT scans, combined with radiomics, represents a promising non-invasive approach for the predicting immunotherapy outcomes in advanced NSCLC patients.

PSII-1 Bacillus-based probiotic supplementation to sows and weaned progeny help to support live performance and manage gross lung lesions in a nursery H1N2 Influenza challenge

Abstract Recent studies have indicated that providing an effective probiotic to dams and their progeny allows the pigs to respond more favorably to viral challenges (Lerner et al., 2020; Bertram et al., 2022). Sixteen pregnant dams (parity 1 to 5) allotted by gestation pen were exposed to one of two treatments: Control (CON) where dams consumed a standard, non-probiotic containing, gestation and lactation diet or Bacillus (BAC) where dams consumed standard diets with a daily top dress applied to achieve 1.65 x 106 CFUs of Bacillus/gram of complete feed from 5 weeks before farrowing through lactation. Ninety-six nursery pigs were allotted (48 per treatment group) to the nursery portion of this study at weaning. The CON pigs selected for the nursery received non-probiotic containing feed and BAC pigs selected for the nursery received 1.10 x 106 CFUs Bacillus/gram of complete feed for the 3-wk nursery period of the study. Pigs were separated into 8 pens with 6 pigs per pen by treatment. Individual pig body weights and pen feed intakes were recorded on d 0, 7, 14, and 21. Following 14-d of feed acclimatization post-weaning, all pigs received an H1N2 Influenza viral challenge via intratracheal gavage (Haesebrouck et al., 1986). Clinical signs, including rectal temperatures, respiratory score, abdominal score, and demeanor score we collected daily beginning 1 d before challenge (d 13) through the end of the study. On d 21, following body weight collection, all pigs were euthanized, lungs were removed, and each of seven lung lobes (right and left cranial, right and left middle, accessory, and right and left caudal) were scored on percentage of lobes bearing gross (visible) lung lesions (Halbur et al., 1996). Pigs in the BAC group had better F:G ratios during the pre-challenge (1.177 vs. 1.309, P < 0.01), post-challenge (1.418 vs. 1.633, P < 0.01), and entire growth (1.287 vs. 1.464, P < 0.001) periods, compared with those of the CON group. The percentage of pigs in the BAC group with gross lung lesions in the left and right cranial, accessory, and left and right caudal lung lobes was less than those of the CON group (Table 1). Additionally, total lung lobes with lesions per pig (4.1 vs. 5.3, P < 0.001) were lower in pigs in the BAC group. These data indicate that application of a Bacillus-based probiotic (in this case BioPlus 2B) to sows and subsequently their weaned progeny is associated with better feed efficiency and fewer lung lesions following a challenge with H1N2 Influenza.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.

Cancer is a heterogenous, dynamic target. Individual patients, lesions, and cell populations may present with different characteristics and varying sensitivities to anticancer therapies. The purpose of this study was to investigate the utility of image-derived (radiomic) biomarkers for prediction of lesion-specific treatment response. Using a database of serial imaging from a Phase III clinical trial (NCT01440088), all visible lung metastases were contoured on chest computed tomography performed at baseline and after 2 cycles of chemotherapy. Metastatic leiomyosarcoma patients received either doxorubicin monotherapy (DM) or doxorubicin plus evofosfamide (DE). Percent change in volume with respect to baseline was evaluated as a metric of treatment response. Lesions that experienced a positive volume change greater than 50% were labeled as 'progressors'; all other lesions were labeled as 'non-progressors'. Radiomic features were extracted at baseline from the whole lesion and three peritumoral regions using PyRadiomics (version 3.0.1). Features were compared using the Mann-Whitney U-test and further used to classify between response categories. Logistic classification models were evaluated with 10-fold cross-validation using Area Under the Curve for the Receiver Operating Characteristic (AUROC) and Precision-Recall (AUPRC) curves as measures of model performance. Significance was evaluated by permutation tests. All code used to derive features is open source and available for download. In total, 204 lung metastases from 80 patients were evaluated. The fraction of progressing lesions was 19% and 27% for the DM and DE regimes, respectively. Differences in individual lesion response were observed in 20% of patients with multiple lung metastases. Significant differences in entropy were observed between response categories (false discovery rate < 5% after multiple testing correction): interior regions for lesions receiving DM, and peripheral regions for lesions receiving DE. The best performing models for the DM and DE regimens had AUROC of 0.87 and 0.76 and AUPRC 0.60 and 0.50, respectively (p < 0.05). Predicting lesion-specific responses using radiomic features represents a paradigm shift. Lesion-specific radiomic models indicate a 2 to 3-fold increase in predictive capacity in comparison to a no-skill classifier. These models, although preliminary, achieved a strong predictive value and could be used to predict lesion-specific treatment response.

Abstract 1639: Human immune system mouse model of ER+ metastatic breast cancer

Abstract In a recent clinical trial in heavily pretreated estrogen receptor alpha-positive (ER+) metastatic breast cancer (MBC), ~23% of patients reached the primary endpoint of clinical benefit of &amp;gt;24 weeks on Fulvestrant plus the anti-androgen Enzalutamide. ESR1 mutant biopsies had more T regulatory cells (t test p&amp;lt;0.021) and macrophages (p&amp;lt;0.003) than ESR1 wild type (WT), which had more TP53 mutations and higher levels of cytotoxic T effector cells (p&amp;lt;0.01). In addition, PD-L1 increased in post- vs. pre-treatment biopsies (paired t test p&amp;lt;0.002). This increase in PD-L1 post-treatment warrants testing if the addition of a checkpoint inhibitor to the Fulvestrant-Enzalutamide combination will enhance therapy efficacy in ER+ MBC resistant to standard endocrine therapy. However, the lack of immune competent ER+ breast cancer pre-clinical models is a major barrier to these studies. Human immune system (HIS) mouse models were developed as a platform in which to test immune checkpoint inhibitors in other human cancers, and one HIS model was recently shown to support the growth and immune infiltration of ER+ patient-derived xenograft (PDX) primary tumors. We tested whether HIS mice are a robust model in which to define ER+ tumor-immune microenvironment interactions at metastatic sites. We implanted NCG mice (NOD-Prkdcem26Cd52Il2rgem26Cd22/NjuCrl) humanized by engraftment of human umbilical cord blood-derived CD34+ (huCD34+) stem cells with the estrogen-independent HCI-013EI (013EI) PDX, derived from an ER+, invasive lobular MBC which harbors the Y537S ESR1 mutation. Mice were monitored for tumor latency and growth rate, and primary tumors and organs were collected for immunohistochemistry, single-cell RNA sequencing (scRNAseq), and phasor-fluorescence lifetime imaging microscopy (FLIM). The ER mutated PDX 013EI had a longer tumor latency in HIS (median 77 days) vs. age-matched immune deficient control mice (27.5 days, log-rank p=0.043). Once formed, 013EI tumors in HIS mice grew slower than those in controls (mixed-effects analysis, p=0.035). HIS mice had visible lung, liver, and/or gastrointestinal tract metastases despite small primary tumors. Lung metastases from HIS mice retain ER expression and are infiltrated by human CD4+, CD8+, and CD68+ immune cells. Initial scRNAseq analysis also suggests infiltration by diverse immune cell populations in primary tumors from HIS mice. Initial phasor-FLIM for NADH autofluorescence suggests that 013EI tumors are highly glycolytic in HIS mice. In conclusion, our results show that huCD34+ HIS mice effectively support growth, metastasis, and immune infiltration of an ESR1 mutant MBC PDX. Ongoing studies will further define the tumor-immune microenvironment in this and ESR1 WT MBC PDX, focusing on immune cell infiltration, PD-L1 expression, steroid receptor status, metabolic signaling and changes in these parameters following novel combinations of endocrine, immunotherapy and metabolic agents. Citation Format: Ayodeji Olukoya, Shaymaa Bahnassy, Nicole S. Spoelstra, Lu Jin, Suman Ranjit, Anthony Elias, Jennifer K. Richer, Rebecca B. Riggins. Human immune system mouse model of ER+ metastatic breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1639.

Lesion-specific radiomics analysis shows promising results for early-stage efficacy assessment of IOA-244 in uveal melanoma.

3068 Background: Radiomics is an image based approach that allows for characterization and quantification of tumor lesions in cancer patients. Radiomics has been proven capable of potentially adding value in the diagnostic and prognostic patient managment. In this study we evaluated the potential of Radiomics to bring additional insight also in early drug development. Methods: All the visible malignant lung and liver metastasis lesions of 7 uveal melanoma patients (86% of women, 60±11y) treated with IOA-244 (EudraCT 2019-000686-20) were manually segmented and analyzed in their size and shape via a radiomics approach. The CT scans at baseline and first follow-up (8 weeks) were included in the study and compared. Descriptive statistics and linear mixed effect (LME) models were used to quantify volumetric lesion-specific response to treatment. Response has been defined both as continuous variable and in three discrete categories (lesion shrinkage, stable and progressive disease for a volume change of [-100%;-0%];[0%-+25%] and &gt; 25%, respectively). The influence of lesion shape at baseline (e.g. compactness, elongation or surface roughness among others) on the treatment response has been explore through LME models as well. Results: We identified and segmented 126 metastatic lesions (70 lung and 56 liver) from baseline scans and 122 lesions (71 lung and 51 liver) from post treatment scans. Of those, 64% could be consistently mapped between visits, resulting in a total of 147 matching lesions on which the radiomics analysis was performed. We found 19% of complete response and 16% of new lesions appearing. 8 weeks after treatment start, we observed non progressive disease in 61% of all lesions, of which 42% was shrinking. LME did not show a significant change in lesion volume between visits, but the mean difference between visits was negative. LME did show that lesion shape is significantly different between progressors and non-progressors at baseline for lung lesions (compact and irregular lesions are more likely to respond), and that there are moderate correlations (0.4-0.7) between tumor shape and volume change for liver lesions (compact lesions have a larger volume drop). Conclusions: This work demonstrates both the clinical potential of IOA-244 for treatment of Uveal Melanoma patients with lesions in the lung and in the liver and the potential of radiomics individual lesion analysis for clinical research in the very early stages of drug development. Lesion evolution volumetric assessment has allowed a more accurate and sensitive understanding of IOA-244 efficacy and impact across different lesions, in both lung and liver. Radiomics showed a promising response of selected population to IOA-244 over the first time point (W0-W8). A further radiomics analysis on next follow-up scans would allow a radiological proof of treatment-induced changes and long-term patient outcome prediction.

Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs.

Background A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT. Purpose To evaluate whether a deep learning (DL)-based CAD system can improve diagnostic yield for newly visible lung metastasis on chest radiographs in patients with cancer. Materials and Methods A regulatory-approved CAD system for lung nodules was implemented to interpret chest radiographs from patients referred by the medical oncology department in clinical practice. In this retrospective diagnostic cohort study, chest radiographs interpreted with assistance from a CAD system after the implementation (January to April 2019, CAD-assisted interpretation group) and those interpreted before the implementation (September to December 2018, conventional interpretation group) of the CAD system were consecutively included. The diagnostic yield (frequency of true-positive detections) and false-referral rate (frequency of false-positive detections) of formal reports of chest radiographs for newly visible lung metastasis were compared between the two groups using generalized estimating equations. Propensity score matching was performed between the two groups for age, sex, and primary cancer. Results A total of 2916 chest radiographs from 1521 patients (1546 men, 1370 women; mean age, 62 years) and 5681 chest radiographs from 3456 patients (2941 men, 2740 women; mean age, 62 years) were analyzed in the CAD-assisted interpretation and conventional interpretation groups, respectively. The diagnostic yield for newly visible metastasis was higher in the CAD-assisted interpretation group (0.86%, 25 of 2916 [95% CI: 0.58, 1.3] vs 0.32%, 18 of 568 [95% CI: 0.20, 0.50%]; P = .004). The false-referral rate in the CAD-assisted interpretation group (0.34%, 10 of 2916 [95% CI: 0.19, 0.64]) was not inferior to that in the conventional interpretation group (0.25%, 14 of 5681 [95% CI: 0.15, 0.42]) at the noninferiority margin of 0.5% (95% CI of difference: -0.15, 0.35). Conclusion A deep learning-based computer-aided detection system improved the diagnostic yield for newly visible metastasis on chest radiographs in patients with cancer with a similar false-referral rate. © RSNA, 2021 Online supplemental material is available for this article.

CT classification of acute myeloid leukemia with pulmonary infiltration.

To characterize and categorize the CT findings of pulmonary leukemic infiltration (PLI) in patients with acute myeloid leukemia (AML). Among 435 patients with AML, 20 patients with PLI were retrospectively selected, and clinical characteristics and CT findings were analyzed. PLI was categorized into four patterns according to CT findings: type A, multiple nodules and/or masses; type B, bilateral perihilar airspace opacities (GGA or consolidation) without any nodules or masses; type C, mixture of type A and B; and type D, PLI without visible abnormal lung opacity. The difference in overall survival among four CT patterns was also examined. The frequency of complex karyotypes was higher in AML patients with PLI than in whole AML patients. Percentages of patients with CT findings of type A, B, C, and D were 35%, 20%, 35%, and 10%, respectively. There was a clear difference in the localization of opacities according to the type of infiltrates, i.e., nodules/masses were mainly detected in the lower/peripheral portion. Conversely, GGA was mainly located in the upper/central portion. The median overall survival from diagnosis of PLI was 262days (range 12-1148). The CT pattern was not significantly associated with survival (p = 0.3), with the exception of patients with type C tending to have significantly better outcomes compared to patients with type B (p = 0.05). This classification can contribute in accurate non-invasive diagnosis and possibly in the estimation of prognosis.

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset.

Assess if deep learning-based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs). This reader study included 173 images from cancer-positive patients (n = 98) and 346 images from cancer-negative patients (n = 196) selected from National Lung Screening Trial (NLST). Eight readers, including three radiology residents, and five board-certified radiologists, participated in the observer performance test. AI algorithm provided image-level probability of pulmonary nodule or mass on CXRs and a heatmap of detected lesions. Reader performance was compared with AUC, sensitivity, specificity, false-positives per image (FPPI), and rates of chest CT recommendations. With AI, the average sensitivity of readers for the detection of visible lung cancer increased for residents, but was similar for radiologists compared to that without AI (0.61 [95% CI, 0.55-0.67] vs. 0.72 [95% CI, 0.66-0.77], p = 0.016 for residents, and 0.76 [95% CI, 0.72-0.81] vs. 0.76 [95% CI, 0.72-0.81, p = 1.00 for radiologists), while false-positive findings per image (FPPI) was similar for residents, but decreased for radiologists (0.15 [95% CI, 0.11-0.18] vs. 0.12 [95% CI, 0.09-0.16], p = 0.13 for residents, and 0.24 [95% CI, 0.20-0.29] vs. 0.17 [95% CI, 0.13-0.20], p < 0.001 for radiologists). With AI, the average rate of chest CT recommendation in patients positive for visible cancer increased for residents, but was similar for radiologists (54.7% [95% CI, 48.2-61.2%] vs. 70.2% [95% CI, 64.2-76.2%], p < 0.001 for residents and 72.5% [95% CI, 68.0-77.1%] vs. 73.9% [95% CI, 69.4-78.3%], p = 0.68 for radiologists), while that in cancer-negative patients was similar for residents, but decreased for radiologists (11.2% [95% CI, 9.6-13.1%] vs. 9.8% [95% CI, 8.0-11.6%], p = 0.32 for residents and 16.4% [95% CI, 14.7-18.2%] vs. 11.7% [95% CI, 10.2-13.3%], p < 0.001 for radiologists). AI algorithm can enhance the performance of readers for the detection of lung cancers on chest radiographs when used as second reader. • Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer. • With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer. • With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.

Abstract PO-086: Detection of cancer lesions in histopathological lung images using a sparse PCA network

Abstract Purpose: Lung cancer has been the leading cause of cancer-related deaths worldwide. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMMs) have become integral in identifying and evaluating unique pathways that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer aided detection tools, for the accurate and efficient analysis of these histopathology images. Our work demonstrates a simple machine learning approach called sparse principal component analysis (PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&amp;E). Methods: Our method comprises four steps: 1) cascaded sparse PCA; 2) graph-based PCA hashing; 3) block-wise histograms; and 4) support vector machine (SVM) classification. In our proposed architecture, sparse PCA is employed to learn the filter banks of the multiple stages. This is followed by a graph-based PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this sparse PCA are then fed to an SVM classifier. We tested the proposed sparse PCA network on H&amp;E slides obtained from an inducible KrasG12D lung cancer mouse model. Our dataset consists of N = 21 whole slide histopathology lung images with 9 non-tumor bearing control mice and 12 mice with visible lung tumors. Tumor lesions from 12 lung images with visible tumors were visually identified by three trained individuals, which served as ground truth. The size of each image in our dataset is 2048 × 2048 pixels. Each image was divided into non-overlapping image patches of size 20 × 20 pixels consisting of a total of 12,361 cancer lesion patches and 207,839 non-cancer patches. We used 50% of the data for training and 50% of the data for testing our proposed sparse PCA network. We evaluated our algorithm using conventional metrics that have been used for evaluation of classification algorithms, namely precision (P), recall (R), and coverage measure (F-score). Results: The automatic cancer lesion detection results were compared with manually annotated ground truth. The proposed method achieves a cancer lesion detection accuracy of 97.98% with P = 0.8624, R = 0.9062 and F-score = 0.8790. The proposed method was found to take on average 17 minutes to train and learn a good representation for accurate and efficient classification of cancerous lesions within the images. Conclusion: We demonstrated a simple machine learning methodology for detection of cancerous lesions within histopathological lung images. Experimental results show that the proposed method is able to classify the regions of interest both efficiently and accurately. Future work will focus on feature extraction of individual tumors and tumor location within lungs. Citation Format: Sundaresh Ram, Wenfei Tang, Alexander J. Bell, Cara Spencer, Alexander Buschhuas, Charles R. Hatt, Marina P. di Magliano, Stefanie Galban, Craig J. Galban. Detection of cancer lesions in histopathological lung images using a sparse PCA network [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-086.

Scoping the Problem

Scoping the Problem

Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach

In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures.

PET in the diagnostic management of infectious/inflammatory pulmonary pathologies: a revisit in the era of COVID-19.

PET in the diagnostic management of infectious/inflammatory pulmonary pathologies: a revisit in the era of COVID-19.

Abstract 5075: Naproxen blocks spontaneous lung adenoma progression to adenocarcinoma in Kras-G12V mice

Abstract The aim of the present study was to investigate the effects of a non-steroidal anti-inflammatory drug (NSAID), naproxen, on a spontaneous mouse model of lung adenocarcinoma. Lung cancer is the most frequently diagnosed cancer and the primary cause of cancer-related deaths worldwide. Inflammation plays an important role in the development and progression of lung and several other cancers. The association between NSAID use and lung cancer risk suggests a beneficial effect on patients’ lung cancer progression. Preventing lung cancer can help to significantly reduce cancer-related mortality. Compared with other NSAIDs, naproxen is relatively safer in terms of cardiovascular risk. Eight-week-old transgenic KrasG12V male and female mice (n=20) or littermate wild-type (n=5) mice were fed (AIN76A) diets containing naproxen (0 ppm or 400 ppm) in modified AIN76-A diet for 28 weeks. At 36 weeks of age, mice were euthanized. Lungs were collected and evaluated for lung tumor incidence and multiplicity, and were saved in formalin for histopathological identification of adenoma and adenocarcinoma. By 12 weeks of age, KrasG12V mice developed visible lung tumors that enlarged in size and progressed to adenocarcinoma by 24-36 weeks of age. Lung tumor incidence was observed in 100% of KrasG12V mice. Dietary administration of naproxen did not show any overt-toxicities. No significant changes were observed in body weight gain or any major organ’s (liver, kidney, and pancreas) gross morphology or weight in mice fed with naproxen compared with mice fed control diet. Due to reduced tumor burden, lungs of the naproxen-fed mice weighed less (230.6± 12.2 mg, p=0.019) (Mean±SEM) than those of the control group (384.0±68.1 mg). Importantly, mice fed control diet developed 19.8±0.96 total lung tumors (2.5±0.3 adenoma, 17.3±0.7 adenocarcinoma). The results suggest that naproxen inhibits total lung tumor formation by ~52% (9.4±0.85;p&amp;lt;0001) and adenocarcinoma by ~64% (6.1±0.6; p&amp;lt;0001), compared with control diet. However, we observed no significant difference in the number of adenomas in mice fed with control diet and mice fed naproxen diet. These data suggest that naproxen delays the progression of lung adenoma to adenocarcinoma. Biomarker analysis of lung tumors from mice exposed to naproxen showed significantly reduced tumor cell proliferation (PCNA, Cyclin D1) and increased apoptosis (p21, Caspase-3). These results support a chemopreventive role of naproxen in spontaneous-induced lung adenocarcinoma formation. (Supported by the Kerley-Cade Chair Endowment &amp; partly by the NCI-PREVENT program HHSN261201500038i) Citation Format: Gaurav Kumar, Venkateshwar Madka, Craig Logsdon, Anil Singh, Nicole Stratton, Stanley Lightfoot, Altaf Mohammed, Chinthalapally V. Rao. Naproxen blocks spontaneous lung adenoma progression to adenocarcinoma in Kras-G12V mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5075.

Will That Pulmonary Nodule Become Cancerous? A Risk Prediction Model for Incident Lung Cancer.

This prospective investigation derived a prediction model for identifying risk of incident lung cancer among patients with visible lung nodules identified on computed tomography (CT). Among 2,924 eligible patients referred for evaluation of a pulmonary nodule to the Stony Brook Lung Cancer Evaluation Center between January 1, 2002 and December 31, 2015, 171 developed incident lung cancer during the observation period. Cox proportional hazard models were used to model time until disease onset. The sample was randomly divided into discovery (n = 1,469) and replication (n = 1,455) samples. In the replication sample, concordance was computed to indicate predictive accuracy and risk scores were calculated using the linear predictions. Youden index was used to identify high-risk versus low-risk patients and cumulative lung cancer incidence was examined for high-risk and low-risk groups. Multivariable analyses identified a combination of clinical and radiologic predictors for incident lung cancer including ln-age, ln-pack-years smoking, a history of cancer, chronic obstructive pulmonary disease, and several radiologic markers including spiculation, ground glass opacity, and nodule size. The final model reliably detected patients who developed lung cancer in the replication sample (C = 0.86, sensitivity/specificity = 0.73/0.81). Cumulative incidence of lung cancer was elevated in high-risk versus low-risk groups [HR = 14.34; 95% confidence interval (CI), 8.17-25.18]. Quantification of reliable risk scores has high clinical utility, enabling physicians to better stratify treatment protocols to manage patient care. The final model is among the first tools developed to predict incident lung cancer in patients presenting with a concerning pulmonary nodule.

Performance of Deep Learning Model in Detecting Operable Lung Cancer With Chest Radiographs.

The aim of this study was to evaluate the diagnostic performance of a trained deep convolutional neural network (DCNN) model for detecting operable lung cancer with chest radiographs (CXRs). The institutional review board approved this study. A deep learning model (DLM) based on DCNN was trained with 17,211 CXRs (5700 CT-confirmed lung nodules in 3500 CXRs and 13,711 normal CXRs), finally augmented to 600,000 images. For validation, a trained DLM was tested with 1483 CXRs with surgically resected lung cancer, marked and scored by 2 radiologists. Furthermore, diagnostic performances of DLM and 6 human observers were compared with 500 cases (200 visible T1 lung cancer on CXR and 300 normal CXRs) and analyzed using free-response receiver-operating characteristics curve (FROC) analysis. The overall detection rate of DLM for resected lung cancers (27.2±14.6 mm) was a sensitivity of 76.8% (1139/1483) with a false positive per image (FPPI) of 0.3 and area under the FROC curve (AUC) of 0.732. In the comparison with human readers, DLM demonstrated a sensitivity of 86.5% at 0.1 FPPI and a sensitivity of 92% at 0.3 FPPI with AUC of 0.899 at an FPPI range of 0.03 to 0.44 for detecting visible T1 lung cancers, which were superior to the average of 6 human readers [mean sensitivity; 78% (range, 71.6% to 82.6%) at an FPPI of 0.1% and 85% (range, 80.2% to 89.2%) at an FPPI of 0.3, AUC of 0.819 (range, 0.754 to 0.862) at an FPPI of 0.03 to 0.44). A DLM has high diagnostic performance in detecting operable lung cancer with CXR, demonstrating a potential of playing a pivotal role for lung cancer screening.

Comparison of predictive powers of functional and anatomic dosimetric parameters for radiation-induced lung toxicity in locally advanced non-small cell lung cancer

PurposeTo investigate the predictive value of the perfusion (Q) single-photon emission computed tomography (SPECT)-weighted dose–function histogram (DFH) obtained mid-treatment (mid-Tx) with radiotherapy (RT) for radiation-induced lung toxicity (RILT) in patients with non-small cell lung cancer (NSCLC). Materials and methodsThe study population consisted of NSCLC patients who were undergoing RT treatment and enrolled in prospective imaging studies. Q-SPECT was performed prior to and during RT (at ∼40–45 Gy). A baseline dose–volume histogram (DVH) and mid-Tx DVH based on simulation CT as well as a baseline DFH and mid-Tx DFH based on Q-SPECT were calculated. Only patients with stage III NSCLC and visible functional lung (FL) changes on the mid-Tx scan were eligible for this enriched analysis. RILT was graded according to a reported scale. ResultsForty-two stage III NSCLC patients met the criteria for inclusion. The accumulative incidence of grade ≥2 RILT was 31% in this high-risk population. Significant differences in functional metrics such as functional lung volume FV5–FV20 at increments of 5 Gy and functional MLD (FMLD) were observed between patients with and without grade ≥2 RILT (p < 0.05). Similar results were also obtained for anatomical metrics from V5–V20 and MLD (p < 0.05). The areas under the receiver operating characteristic curves (AUCs) ranged from 0.724to 0.812 for baseline DVH parameters, from 0.745 to 0.830 for mid-Tx DVH parameters, from 0.764 to 0.878 for baseline DFH parameters, and from 0.767 to 0.891 for mid-Tx DFH parameters. Further principal components analysis showed that the AUCs were 0.814/0.817 and 0.790/0.857 for baseline/mid-Tx DVH and baseline/mid-Tx DFH, respectively. ConclusionsMid-Tx DFH parameters based on Q-SPECT were significantly elevated in patients with grade ≥2 RILT in this study population. Among the metrics compared, mid-Tx DFH seemed to have better predictive accuracy, but this difference did not reach statistical difference.

Outcome of First-Line Hysterectomy for Gestational Trophoblastic Neoplasia in Patients No Longer Wishing to Conceive and Considered With Isolated Lung Metastases: A Series of 30 Patients.

This study aimed to assess the outcome of first-line hysterectomy in patients diagnosed as having gestational trophoblastic neoplasia (GTN) whose postoperative imaging showed lung images considered as metastases. From 1999 to 2016, patients no longer wishing to conceive, treated by their initial physician by hysterectomy, and whose postoperative imaging workup showed lung images considered as metastasis were identified in the French Trophoblastic Disease Reference Center database. We sought to identify significant predictive factors of requiring salvage chemotherapy. Thirty patients were identified with a maximum number of 2 visible lung nodules and a median largest size of 14 mm on chest x-ray. Nine of these patients had an International Federation of Gynecology and Obstetrics score of higher than 6, and there were no postterm GTN. Twenty-two patients (73.33%; 95% confidence interval, 54.11-87.72; P = 0.0053) normalized their human chorionic gonadotropin (hCG) without salvage chemotherapy, whereas 7 received 1 line of salvage monochemotherapy (8-day methotrexate) and 1 required 2 lines of monochemotherapy (5-day actinomycin D after failure of methotrexate). After a 12.45-month median follow-up (range, 3-48.4 months) since the first normalized hCG, none of these patients died. The median interval between successful hysterectomy and hCG normalization was 3.15 months (range, 1.6-8.7 months). Patients who required salvage chemotherapy had a median size of the largest lung metastasis on chest computed tomography of 4 mm larger than those cured by hysterectomy (P = 0.0455). For GTN patients no longer wishing to conceive with lung metastases discovered postoperatively, treated by hysterectomy, and whose hCG is decreasing, it is reasonable to expect and to inform patients that approximately 27% will require salvage chemotherapy. However, in patients with lung metastases discovered preoperatively, evidence to recommend first-line hysterectomy is insufficient and these patients should receive first-line chemotherapy.