On the performance of lung nodule detection, segmentation and classification.

Pulmonary nodules are often encountered by respiratory physicians, radiologists, and thoracic surgeons alike. The European Society of Thoracic Surgery (ESTS) and European Association of Cardiothoracic Surgery (EACTS) established a multidisciplinary collaboration of clinicians with expertise in managing pulmonary nodules with the aim of producing the first joint comprehensive review of the scientific literature, with a specific focus on the management of pure ground glass opacities and part-solid nodules. The scope of the document has been defined by the EACTS and ESTS governing bodies and focuses on six areas of main interest agreed upon by the Task Force. These include: management of solitary and multiple pure ground glass nodules, solitary part-solid nodules, identifying the non-palpable lesion, the role of minimally invasive surgery and the decision making behind sub-lobar versus lobar resection. The literature revealed that with the increasing use of incidental CT scans and CT lung cancer screening programs, the detection of early-stage lung cancer is going to rise with a higher number of potential cancers presenting on the ground glass and part-solid nodule spectrum. Given that the gold standard for improved survival is surgical resection, there is an urgent need for comprehensive characterisation of these nodules and guidelines that are directed towards their surgical management. It is recommended that standard decision-making tools are used to determine the risk of malignancy and thus guide referral for surgical management and decisions for surgical resection are made in a multidisciplinary setting with equitable consideration given to radiological characteristics, the evolution of the lesion, presence of solid component, patient fitness and co-morbidities. Given the recent surge in robust level I data comparing sublobar and lobar resection with the release of JCOG0802 and CALGB140503 data, a global overview of an individual case must be adopted into clinical practice. This set of recommendations are based on the available literature; however, close collaboration in the design and conduct of randomized controlled trials is still of utmost importance to answer further questions in this rapidly evolving field.

European Guidelines on the Surgical Management of Pure Ground Glass Opacities and Part Solid Nodules: Task Force of the EACTS and ESTS.

Background and Objective A highly nuanced relationship exists between obesity and lung cancer. The association between obesity and lung cancer risk/prognosis varies depending on age, gender, race, and the metric used to quantify adiposity. Increased body mass index (BMI) is counterintuitively associated with decreased lung cancer incidence and mortality, giving rise to the term ‘obesity paradox’. Potential explanations for this paradox are BMI being a poor measure of obesity, confounding by smoking and reverse causation. A literature search of this topic yields conflicting conclusions from various authors. We aim to clarify the relationship between various measures of obesity, lung cancer risk, and lung cancer prognosis. Methods The PubMed database was searched on 10 August 2022 to identify published research studies. Literature published in English between 2018 and 2022 were included. Sixty-nine publications were considered relevant, and their full text studied to collate information for this review. Key Content and Findings Lower lung cancer incidence and better prognosis was associated with increased BMI even after accounting for smoking and pre-clinical weight loss. Individuals with high BMI also responded better to treatment modalities such as immunotherapy compared to individuals with a normal BMI. However, these associations varied highly depending on age, gender, and race. Inability of BMI to measure body habitus is the main driver behind this variability. The use of anthropometric indicators and image-based techniques to quantify central obesity easily and accurately is on the rise. Increase in central adiposity is associated with increased incidence and poorer prognosis of lung cancer, contrasting BMI. Conclusions The obesity paradox may arise due to the improper use of BMI as a measure of body composition. Measures of central obesity better portray the deleterious effects of obesity and are more appropriate to be discussed when talking about lung cancer. The use of obesity metrics based on anthropometric measurements and imaging modalities has been shown to be feasible and practical. However, a lack of standardization makes it difficult to interpret the results of studies using these metrics. Further research must be done to understand the association between these obesity metrics and lung cancer.

/pdf/obesity-and-lung-cancer-a-narrative-review-1gzr0btf.pdf

Obesity and lung cancer—a narrative review

Combined chemotherapy plus programmed death-1 (PD-1) blockade is an established treatment against patients with advanced non-small cell lung cancer (NSCLC). However, a promising predictor besides programmed death ligand-1 expression remains uncertain. We examined the prognostic significance of baseline 18 F-FDG-positron emission tomography for predicting first-line combined chemotherapy plus PD-1 blockade in NSCLC patients. Forty-five patients with advanced NSCLC who received 18 F-FDG-positron emission tomography immediately before combined platinum-based chemotherapy with PD-1 blockade as first-line setting were eligible for this study, and assessment of maximum of standard uptake value (SUV max ), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on 18 F-FDG uptake was performed. The objective response rate, median progression-free survival, and overall survival were 51.2%, 206 days, and 681 days, respectively. High SUV max , TLG, and MTV significantly correlated with age and performance status (PS), C-reactive protein (CRP), and PS, CRP, albumin, and baseline tumor size, respectively. Univariate analysis identified albumin, TLG and MTV as significant predictors of progression-free survival, and CRP, albumin, TLG and MTV as significant factors for predicting overall survival. High TLG was confirmed as an independent factor associated with poor prognosis in multivariate analysis. In particular, TLG is identified as the most powerful predictor in patients with good PS, adenocarcinoma, programmed death ligand-1≥1%, and low baseline tumor size. The tumor metabolic volume by MTV and TLG at pretreatment was clarified as a significant predictor for combined chemotherapy with PD-1 blockade, but not maximal glycolytic level by SUV max . 

Prognostic Potential of Metabolic Activity on 18F-FDG Accumulation in Advanced NSCLC Receiving Combining Chemotherapy Plus PD-1 Blockade

Purpose: Tumor promote disease progression by reprogramming their metabolism and that of distal organs, so it is of great clinical significance to study the changes in glucose metabolism at different tumor stages and their effect on glucose metabolism in other organs. Methods: A retrospective single-centre study was conducted on 253 NSCLC (non-small cell lung cancer) patients with negative lymph nodes and no distant metastasis. According to the AJCC criteria, the patients were divided into different groups based on tumor size: stage IA, less than 3 cm (group 1, n = 121); stage IB, greater than 3-4 cm (group 2, n = 64); stage IIA, greater than 4-5 cm (group 3, n = 36); and stage IIB, greater than 5-7 cm (group 4, n = 32). All of the patients underwent baseline 18F-FDG PET/CT scans, and the primary lesion SUVmax (maximum standardized uptake value), liver SUVmean (mean standardized uptake value), spleen SUVmean, TLR (Tumor-to-liver SUV ratio) and TSR (Tumor-to-spleen SUV ratio) were included in the study, combined with clinical examination indicators to evaluate DFS (disease free survival). Results: In NSCLC patients, with the increase in the maximum diameter of the tumor, the SUVmax of the primary lesion gradually increased, and the SUVmean of the liver gradually decreased. The primary lesion SUVmax, liver SUVmean, TLR and TSR were related to disease recurrence or death. The best predictive parameters were different when the tumor size differed. SUVmax had the highest efficiency when the tumor size was less than 4 cm (AUC:0.707 (95% CI, 0.430-0.984) tumor size < 3 cm), (AUC:0.726 (95% CI, 0.539-0.912) tumor size 3-4 cm), liver SUVmean had the highest efficiency when the tumor size was 4-5 cm (AUC:0.712 (95% CI, 0.535-0.889)), and TLR had the highest efficiency when the tumor size was 5-7 cm (AUC:0.925 (95%CI, 0.820-1.000)). Conclusions: In patients with early NSCLC, glucose metabolism reprogramming occurs in the primary lesion and liver. With the increase in tumor size, different metabolic parameters should be selected to evaluate the prognosis of patients.

Glucose Metabolism Reprogramming of Primary Tumor and the Liver Is Associated With Disease-Free Survival in Patients With Early NSCLC.

Association between metabolic overweight/obesity phenotypes and readmission risk in patients with lung cancer: A retrospective cohort study

Background Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice. Methods Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. Results The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies. Conclusions DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs. 

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study

Background The recognition of anatomical variants is essential in preoperative planning for lung cancer surgery. Although three-dimensional (3-D) reconstruction provided an intuitive demonstration of the anatomical structure, the recognition process remains fully manual. To render a semiautomated approach for surgery planning, we developed an artificial intelligence (AI)–based chest CT semantic segmentation algorithm that recognizes pulmonary vessels on lobular or segmental levels. Hereby, we present a retrospective validation of the algorithm comparing surgeons’ performance. Methods The semantic segmentation algorithm to be validated was trained on non-contrast CT scans from a single center. A retrospective pilot study was performed. An independent validation dataset was constituted by an arbitrary selection from patients who underwent lobectomy or segmentectomy in three institutions during Apr. 2020 to Jun. 2021. The golden standard of anatomical variants of each enrolled case was obtained via expert surgeons’ judgments based on chest CT, 3-D reconstruction, and surgical observation. The performance of the algorithm is compared against the performance of two junior thoracic surgery attendings based on chest CT. Results A total of 27 cases were included in this study. The overall case-wise accuracy of the AI model was 82.8% in pulmonary vessels compared to 78.8% and 77.0% for the two surgeons, respectively. Segmental artery accuracy was 79.7%, 73.6%, and 72.7%; lobular vein accuracy was 96.3%, 96.3%, and 92.6% by the AI model and two surgeons, respectively. No statistical significance was found. In subgroup analysis, the anatomic structure-wise analysis of the AI algorithm showed a significant difference in accuracies between different lobes (p = 0.012). Higher AI accuracy in the right-upper lobe (RUL) and left-lower lobe (LLL) arteries was shown. A trend of better performance in non-contrast CT was also detected. Most recognition errors by the algorithm were the misclassification of LA1+2 and LA3. Radiological parameters did not exhibit a significant impact on the performance of both AI and surgeons. Conclusion The semantic segmentation algorithm achieves the recognition of the segmental pulmonary artery and the lobular pulmonary vein. The performance of the model approximates that of junior thoracic surgery attendings. Our work provides a novel semiautomated surgery planning approach that is potentially beneficial to lung cancer patients.

/pdf/ai-based-chest-ct-semantic-segmentation-algorithm-enables-4dmgajgk.pdf

AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels

Objectives:Increasing interests have been focused on using artificial intelligence (AI) to extend prognostic value of medical imaging. Feature extraction is a critical step for successful applicati...

https://journals.sagepub.com/doi/pdf/10.1177/1536012119846025

Prognostic and Predictive Values of Metabolic Parameters of 18F-FDG PET/CT in Patients With Non-Small Cell Lung Cancer Treated With Chemotherapy.

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Association between metabolic overweight/obesity phenotypes and readmission risk in patients with lung cancer: A retrospective cohort study

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study

AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels

Prognostic and Predictive Values of Metabolic Parameters of 18F-FDG PET/CT in Patients With Non-Small Cell Lung Cancer Treated With Chemotherapy.