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Objectives: To investigate the value of CT radiomics combined with radiological features in predicting pathological grade of stage I invasive pulmonary adenocarcinoma (IPA) based on the International Association for the Study of Lung Cancer (IASLC) new grading system. Methods: The preoperative CT images and clinical information of 294 patients with stage I IPA were retrospectively analyzed (159 training set; 69 validation set; 66 test set). Referring to the IASLC new grading system, patients were divided into a low/intermediate-grade group and a high-grade group. Radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO), the logistic regression (LR) classifier was used to establish radiomics model (RM), clinicalradiological features model (CRM) and combined rad-score with radiological features model (CRRM), and visualized CRRM by nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the performance and fitness of models. Results: In the training set, RM, CRM, and CRRM achieved AUCs of 0.825 [95% CI (0.735-0.916)], 0.849 [95% CI (0.772-0.925)], and 0.888 [95% CI (0.819-0.957)], respectively. For the validation set, the AUCs were 0.879 [95% CI (0.734-1.000)], 0.888 [95% CI (0.794-0.982)], and 0.922 [95% CI (0.835-1.000)], and for the test set, the AUCs were 0.814 [95% CI (0.674-0.954)], 0.849 [95% CI (0.750-0.948)], and 0.860 [95% CI (0.755-0.964)] for RM, CRM, and CRRM, respectively. Conclusion: All three models performed well in predicting pathological grade, especially the combined model, showing CT radiomics combined with radiological features had the potential to distinguish the pathological grade of early-stage IPA. [ABSTRACT FROM AUTHOR]

Summary: Objective: To evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma. Methods: A total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes. Results: Statistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05). Conclusions: The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups. [ABSTRACT FROM AUTHOR]

Double primary malignancy though uncommon, we often encounter in our clinical practice. The lung malignancy is known to cause left vocal cord paralysis. Bilateral abductor paralysis secondary to adenocarcinoma of the lung with concurrent basal cell carcinoma of the face is not common. Proper counseling and timely management are needed in these cases of multiple primary malignancies. Early evaluation in all cases of hoarseness can help in early diagnosis and management. [ABSTRACT FROM AUTHOR]

Purpose: Predicting the malignancy of pure ground-glass nodules (GGNs) using CT is challenging. The optimal role of [18F]FDG PET/CT in this context has not been clarified. We compared the performance of [18F]FDG PET/CT in evaluating GGNs for predicting invasive adenocarcinomas (IACs) with CT. Methods: From June 2012 to December 2020, we retrospectively enrolled patients with pure GGNs on CT who underwent [18F]FDG PET/CT within 90 days. Overall, 38 patients with 40 ≥ 1–cm GGNs were pathologically confirmed. CT images were analyzed for size, attenuation, uniformity, shape, margin, tumor–lung interface, and internal/surrounding characteristics. Visual [18F]FDG positivity, maximum standardized uptake value (SUVmax), and tissue fraction-corrected SUVmax (SUVmaxTF) were evaluated on PET/CT. Results: The histopathology of the 40 GGNs were: 25 IACs (62.5%), 9 minimally invasive adenocarcinomas (MIA, 22.5%), and 6 adenocarcinomas in situ (AIS, 15.0%). No significant differences were found in CT findings according to histopathology, whereas visual [18F]FDG positivity, SUVmax, and SUVmaxTF were significantly different (P=0.001, 0.033, and 0.018, respectively). The size, visual [18F]FDG positivity, SUVmax, and SUVmaxTF showed significant diagnostic performance to predict IACs (area under the curve=0.693, 0.773, 0.717, and 0.723, respectively; P=0.029, 0.001, 0.018, and 0.013, respectively). In the multivariate logistic regression analysis, visual [18F]FDG positivity discriminated IACs among GGNs among various CT and PET findings (P=0.008). Conclusions: [18F]FDG PET/CT demonstrated superior diagnostic performance compared to CT in differentiating IAC from AIS/MIA among pure GGNs, thus it has the potential to guide the proper management of patients with pure GGNs. [ABSTRACT FROM AUTHOR]

Non–small cell lung cancer (NSCLC) has been found to have recurrent genetic abnormalities, and novel therapies targeting these aberrations have improved patient survival. In this study, specimens from benign tissue, primary tumors, and brain metastases were obtained at autopsy from a 55-year-old White female patient diagnosed with NSCLC and were examined using next-generation sequencing (NGS) and chromosomal microarray assay (CMA). No genetic aberrations were noted in the benign tissue; however, NGS identified a mutation in the KRAS proto-oncogene, GTPase (KRAS): KRAS exon 2 p.G12D in primary and metastatic tumor specimens. We observed 7 DNA copy number aberrations (CNAs) in primary and metastatic tumor specimens; an additional 7 CNAs were exclusively detected in the metastatic tumor specimens. These DNA alterations may be genetic drivers in the pathogenesis of the tumor specimen from our patient and may serve as biomarkers for the classification and prognosis of NSCLC. [ABSTRACT FROM AUTHOR]

The gene C5orf34 exhibits evolutionary conservation among mammals, and emerging evidence suggests its potential involvement in tumor development; however, comprehensive investigations of this gene are lacking. This study aims to elucidate the functional attributes and underlying mechanisms of C5orf34 in cancer. To evaluate its clinical predictive value, we conducted an analysis of the pan-cancerous expression, clinical data, mutation, and methylation data of C5orf34. Additionally, we investigated the correlation between C5orf34 and tumor mutant load (TMB), immune cell infiltration, and microsatellite instability (MSI) through relevant analyses. Furthermore, immunohistochemical (IHC) staining was employed to validate clinical samples, while knockdown and overexpression experiments and transcriptome RNA sequencing were utilized to examine the impact of C5orf34 on LUAD cells. According to our study, C5orf34 exhibits high expression levels in the majority of malignant tumors. The upregulation of C5orf34 is governed by DNA copy number alterations and methylation patterns, and it is closely associated with patients' survival prognosis and immune characteristics, thereby holding significant clinical implications. Furthermore, IHC staining analysis, cellular experiments, and transcriptome RNA sequencing have provided evidence supporting the role of C5orf34 in modulating the cell cycle to promote LUAD proliferation, migration, and invasion. This highlights its potential as a promising therapeutic target. The findings of this investigation suggest that C5orf34 may serve as a valuable biomarker for various tumor types and represent a potential target for immunotherapy, particularly in relation to the proliferation, migration, and apoptosis of LUAD cells. [ABSTRACT FROM AUTHOR]

Background: Various resistance mechanisms of the epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) have been reported, and approximately half of the cases show a T790M point mutation as resistance to EGFR-TKI. In addition, 3-14% of cases of non-small cell lung cancer transform into small cell lung carcinoma (SCLC) during treatment. However, there are few reported cases in which 2 mechanisms of resistance have been observed simultaneously. This report describes a 66-year-old man with initial presentation of stage IIA right-sided lung adenocarcinoma with EGFR gene exon 21 L858R mutation and 3 years of stable disease. During treatment with erlotinib, the patient developed SCLC and adenocarcinoma with EGFR exon 21 L858R and exon 20 T790M mutation. Case Report: A 66-year-old man underwent right pneumonectomy plus nodal dissection 2a for right hilar lung cancer and was diagnosed with an EGFR exon21 L858R mutated lung adenocarcinoma. Three years later, pleural dissemination was observed in the right chest wall. Although erlotinib was continued for 52 months, new metastases to the right ribs were detected. Chest wall tumor resection was performed. Based on the World Health Organization classification, the patient was diagnosed with combined SCLC, with EGFR exon21 L858R and exon20 T790M mutation. The patient received 4 cycles of carboplatin plus etoposide, 14 cycles of amrubicin, and 2 cycles of irinotecan. Chemotherapy continued for 25 months. Conclusions: Long-term survival was achieved by chemotherapy after transformation. Since EGFR mutation-positive lung cancer shows a variety of acquired resistances, it is important to consider the treatment strategy of performing re-biopsy. [ABSTRACT FROM AUTHOR]

Background: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. Methods: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. Results: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73–0.91]), 0.88 (95% CI [0.81–0.93]), and 0.93(95% CI [0.90–0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82–0.94]), 0.86 (95% CI [0.74–0.93]) and 0.94 (95% CI [0.91–0.96]), respectively. Conclusions: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. Trial registration: CRD42022375712. [ABSTRACT FROM AUTHOR]

Purpose: To ascertain the performance of dual-energy CT (DECT) with iodine quantification in differentiating malignant mediastinal and hilar lymph nodes (LNs) from benign ones, focusing on patients with lung adenocarcinoma. Materials and methods: In this study, patients with suspected lung cancer received a preoperative contrast-enhanced DECT scan from Jun 2018 to Dec 2020. Quantitative DECT parameters and the size were compared between metastatic and benign LNs. Their diagnostic performances were analyzed by the ROC curves and compared by using the two-sample t test. Results: 72 patients (23 men, 49 women; mean age 62.5 ± 10.1 years) fulfilled the inclusion criteria. A total of 98 LNs (67 benign, 31 metastatic) were analyzed. The iodine concentration normalized by muscle (NICmuscle) was significantly higher (P < 0.001) in metastatic LNs (4.79 ± 1.70) than in benign ones (3.00 ± 1.45). The optimal threshold of NICmuscle was 3.44, which yielded AUC: 0.798, sensitivity: 83.9%, specificity: 73.1%, accuracy: 76.5%, respectively. Applying the established size parameters with 10 mm as the threshold yielded AUC: 0.600, sensitivity: 29.0%, specificity: 91.0%, accuracy: 71.4%, respectively. The diagnostic performance of NICmuscle was significantly better (P = 0.007) than the performance obtained using the established size parameters. Conclusions: For lung adenocarcinoma, the quantitative measurement of NICmuscle derived from DECT is useful for differentiating benign and metastatic mediastinal and hilar LNs before surgical intervention. [ABSTRACT FROM AUTHOR]

Background: Metastasis in penile corpus cavernosum from adenocarcinoma of lung is a rare but fatal disease, which was reported in cases without series studies. It causes various clinical symptoms seriously affecting the quality of life. Case presentation: A 72-year-old male smoker patient, who had a history of adenocarcinoma of lung after targeted therapy 36 months before, was admitted to Jiangxi Cancer Hospital because of presenting with aggressive dysuria and penis pain for one hour. A Foley catheter was inserted into the patient’s bladder with difficulty. Immediately do a bladder puncture. Emergency pelvic computed tomography (CT): a soft tissue nodule of 1.1 cm×1.4 cm was found in the cavernous area of the middle part of the penis, and the proximal urethra was dilated with a wide diameter of about 1.5 cm. The diagnosis of metastatic lung adenocarcinoma from the primary was made by CT-guided biopsy. Conclusion: The penis may be a site of metastasis from primary lung cancer, especially for old patient. Metastasis to the penis usually indicates that the primary lung cancer is at an advanced stage and the prognosis is very poor. More research is necessary to understand the underlying mechanism of adenocarcinoma of lung metastasis. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas. Methods: Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression. Results: In 681 patients (median age, 65 years [interquartile range, 58–71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3–71.9%; specificity, 71.6–88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43–0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84–1.92 and 0.63–1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86–2.42 and 0.67–1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07–3.07; p = 0.03). Conclusion: The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome. Clinical relevance statement: Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability. Key Points: • Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. • Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. • Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Obstetrics & Gynecology is the property of TianJin Medical Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tianjin Medical Journal is the property of Tianjin Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose We investigated the clinical impact of genomic and pathway alterations in stage I epidermal growth factor receptor (EGFR)- mutant lung adenocarcinomas, which have a high recurrence rate despite complete surgical resection. Materials and Methods Out of the initial cohort of 257 patients with completely resected stage I EGFR-mutant lung adenocarcinoma, tumor samples from 105 patients were subjected to analysis using large-panel next-generation sequencing. We analyzed 11 canonical oncogenic pathways and determined the number of pathway alterations (NPA). Survival analyses were performed based on co-occurring alterations and NPA in three patient groups: all patients, patients with International Association for the Study of Lung Cancer (IASLC) pathology grade 2, and patients with recurrent tumors treated with EGFR-tyrosine kinase inhibitor (TKI). Results In the univariate analysis, pathological stage, IASLC grade, TP53 mutation, NPA, phosphoinositide 3-kinase pathway, p53 pathway, and cell cycle pathway exhibited significant associations with worse recurrence-free survival (RFS). Moreover, RPS6KB1 or EGFR amplifications were linked to a poorer RFS. Multivariate analysis revealed that pathologic stage, IASLC grade, and cell cycle pathway alteration were independent poor prognostic factors for RFS (p=0.002, p < 0.001, and p=0.006, respectively). In the grade 2 subgroup, higher NPA was independently associated with worse RFS (p=0.003). Additionally, in patients with recurrence treated with EGFR-TKIs, co-occurring TP53 mutations were linked to shorter progression-free survival (p=0.025). Conclusion Genomic and pathway alterations, particularly cell cycle alterations, high NPA, and TP53 mutations, were associated with worse clinical outcomes in stage I EGFR-mutant lung adenocarcinoma. These findings may have implications for risk stratification and the development of new therapeutic strategies in early-stage EGFR-mutant lung cancer patients. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. Methods: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. Results: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D−AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D−AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. Conclusion: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D−AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D−AIMSSA0HU% and both were independent risk factors. Clinical relevance statement: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. Key Points: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)–based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors. [ABSTRACT FROM AUTHOR]

Objective: Unusual or unexpected effect of treatment Background: Chemotherapy has been the conventional treatment method for advanced non-small-cell lung cancer (NSCLC). Nevertheless, the identification and comprehension of oncogenic driver alterations have paved the way for targeted therapies, significantly enhancing patient outcomes. The management of locally advanced NSCLC that is positive for ALK presents a challenge due to the lack of reported randomized controlled trials. The efficacy of neoadjuvant and adjuvant targeted therapy in this context remains uncertain. Case Report: A 54-year-old man was diagnosed with stage IIIB (pT1N3M0) upper right lung adenocarcinoma carrying the EML4-ALK fusion gene. Clinically, the patient had multiple enlarged lymph nodes in the right hilum and mediastinum, with the largest measuring approximately 28x19 mm by CT scan and we found that the L4 lymph node was invaded by metastasis. Then, the patient received 1 cycle of chemotherapy with paclitaxel in combination with nedaplatin and subsequently received maintenance treatment involving lorlatinib. Two months later, clinical evaluations revealed progressive reduction of the lesions, especially the reduced size of the mediastinal lymph nodes. Therefore, the patient underwent thoracoscopic partial lobectomy and lymphadenec-tomy and achieved pathological complete response (pCR). After 3 months, a follow-up CT scan was similar to the first postoperative CT scan and no tumor was found. Conclusions: This case demonstrates the potential advantage of lorlatinib as a neoadjuvant therapy in advanced ALK-positive NSCLC. It emphasizes the importance of identifying new therapeutic targets by next-generation sequencing (NGS) and implementing precise treatment strategies in clinical practice. [ABSTRACT FROM AUTHOR]

Transmembrane protein TMEM16A, which encodes calcium-activated chloride channel has been implicated in tumorigenesis. Overexpression of TMEM16A is associated with poor prognosis and low overall survival in multiple cancers including lung adenocarcinoma, making it a promising biomarker and therapeutic target. In this study, three structure-related sesquiterpene lactones (mecheliolide, costunolide and dehydrocostus lactone) were extracted from the traditional Chinese medicine Aucklandiae Radix and identified as novel TMEM16A inhibitors with comparable inhibitory effects. Their effects on the proliferation and migration of lung adenocarcinoma cells were examined. Whole-cell patch clamp experiments showed that these sesquiterpene lactones potently inhibited recombinant TMEM16A currents in a concentration- dependent manner. The half-maximal concentration (IC50) values for three tested sesquiterpene lactones were 29.9 ± 1.1 μM, 19.7 ± 0.4 μM, and 24.5 ± 2.1 μM, while the maximal effect (Emax) values were 100.0% ± 2.8%, 85.8% ± 0.9%, and 88.3% ± 4.6%, respectively. These sesquiterpene lactones also significantly inhibited the endogenous TMEM16A currents and proliferation, and migration of LA795 lung cancer cells. These results demonstrate that mecheliolide, costunolide and dehydrocostus lactone are novel TMEM16A inhibitors and potential candidates for lung adenocarcinoma therapy. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to apply external validation and stress tests to evaluate the generalizability of radiomics models built using various machine-learning methods for identifying the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs). Methods: This retrospective study enrolled 495 patients (514 pGGNs) confirmed as lung adenocarcinomas by postoperative pathology from three centers. All nodules were included in the primary cohort (randomly divided into training and test cohorts), two external validation cohorts, and two stress test cohorts. Six machine-learning radiomics models were constructed in the training cohort using the optimal features. Performance of radiomics models and clinical models were compared in primary cohort and external validation cohorts. The stress tests included stratified performance evaluation and shifted performance evaluation and contrastive evaluation under three single-condition modification settings. The predictive performance was validated by area under curve (AUC) of receiver operating characteristic (ROC). Results: Of the six radiomics models, the best logistic regression (LR) model was able to maintain high differential diagnostic capability (AUC: 0.849 ± 0.049) and good stability (relative standard deviation, 5.719%), but it showed poorer performance (AUC = 0.835) than the clinical model (AUC = 0.862) in the external validation cohort E1. The stress tests suggested LR model had no significant difference in performance between subgroups after stratification and had good consistency in the predictions before and after the three transformations (Kappa = 0.960, 0.840, and 0.933, respectively; p < 0.05, all). Conclusion: The rigorous testing procedure facilitates the selection of high-performance radiomics models with good clinical generalizability. [ABSTRACT FROM AUTHOR]

The World Health Organization's tumor classification guidelines are frequently updated and renewed as knowledge of cancer biology advances. For instance, in 2021, a novel lung tumor subtype named SMARCA4‐deficient, undifferentiated tumor (SMARCA4‐dUT, code 8044/3) was included. To date, there is no defined cell model for SMARCA4‐dUT that could be used to help thoracic clinicians and researchers in the study of this newly defined tumor type. As this tumor type was recently described, it is feasible that some cell models formerly classified as lung adenocarcinoma (LUAD) could now be better classified as SMARCA4‐dUT. Thus, in this work, we aimed to identify a bona fide cell model for the experimental study of SMARCA4‐dUT. We compared the differential expression profiles of 36 LUAD‐annotated cell lines and 38 cell lines defined as rhabdoid in repositories. These comparative results were integrated with the mutation and expression profiles of the SWI/SNF complex members, and they were surveyed for the presence of the SMARCA4‐dUT markers SOX2, SALL4, and CD34, measured by RT‐qPCR and western blotting. Finally, the cell line with the paradigmatic SMARCA4‐dUT markers was engrafted into immunocompromised mice to assess the histological morphology of the formed tumors and compare them with those formed by a bona fide LUAD cancer cell line. NCI‐H522, formerly classified as LUAD, displayed expression profiles nearer to rhabdoid tumors than LUAD tumors. Furthermore, NCI‐H522 has most of the paradigmatic features of SMARCA4‐dUT: hemizygous inactivating mutation of SMARCA4, severe SMARCA2 downregulation, and high‐level expression of stem cell markers SOX2 and SALL4. In addition, the engrafted tumors of NCI‐H522 did not display a typical differentiated glandular structure as other bona fide LUAD cell lines (A549) do but had rather a largely undifferentiated morphology, characteristic of SMARCA4‐dUT. Thus, we propose the NCI‐H522 as the first bona fide cell line model of SMARCA4‐dUT. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Introduction: The function of cytotoxic cells may be impacted by CD39 expression on CD8-positive T lymphocytes, leading to an imbalance in tumor immunity. Methods: We analyzed the correlation between the expression of CD39 on CD8-positive T lymphocytes and clinical information in 203 patients with lung adenocarcinoma. Results: The expression of CD39 on CD8-positive T lymphocytes was related to T stage, lymph node metastasis and distant metastasis. Furthermore, the CD39 expression reduced the secretion of antitumor cytokines, promoted PD-1 expression and decreased CD28 expression on CD8-positive T lymphocytes. Conclusions: This research demonstrated the potential value of CD39 on CD8-positive T lymphocytes as a negative regulator in cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features. Methods: The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results. Results: The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035–1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911–18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438–5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583–0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806–0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808–0.896) (sensitivity = 74.3%, specificity = 85.8%). Conclusion: The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC. Key Points: •The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583–0.699), 0.851 (95% CI 0.806–0.896), and 0.852 (95% CI 0.808–0.896). •Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. •The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma. [ABSTRACT FROM AUTHOR]

Background: Stage I lung adenocarcinoma is a heterogeneous group. Previous studies have shown the prognostic evaluation value of PET/CT in this cohort; however, few studies focused on stage I invasive adenocarcinoma manifesting as solid nodules. This study aimed to evaluate the recurrence risk for patients with stage I invasive lung adenocarcinoma manifesting as solid nodules based on 18F-FDG PET/CT, CT imaging signs, and clinicopathological parameters. Methods: We retrospectively enrolled 230 patients who underwent 18F-FDG PET/CT examination between January 2013 and July 2019. Metabolic parameters: maximum standard uptake value (SUVmax), mean standard uptake value, tumor metabolic volume (MTV), and total tumor glucose digestion were collected. Kaplan–Meier method was used to evaluate recurrence-free survival (RFS), and the multivariate Cox proportional hazards model was used to determine the independent risk factors associated with RFS. The time-dependent receiver operating characteristic curve (ROC) method was used to calculate the optimal cutoff value of metabolic parameters. Results: The 5-year RFS rate for all patients was 71.7%. Multivariate Cox analysis revealed that the International Association for the Study of Lung Cancer Pathology Committee (IASLC) pathologic grade 3 [Hazard ratio (HR), 3.96; 95% Confidence interval (CI), 1.11–14.09], the presence of cavity sign (HR 5.38; 95% CI 2.23–12.96), SUVmax (HR 1.23; 95% CI 1.13–1.33), and MTV (HR 1.05; 95% CI 1.01–1.08) were potential independent prognostic factors for RFS. Patients with IASLC grade 3, the presence of cavity sign, SUVmax > 3.9, or MTV > 5.4 cm3 were classified as high risk, while others were classified as low risk. There was a significant difference in RFS between the high-risk and low-risk groups (HR 6.04; 95% CI 2.17–16.82, P < 0.001), and the 5-year RFS rate was 94.1% for the low-risk group and 61.3% for the high-risk group. Conclusions: We successfully evaluate the recurrence risk of patients with stage I invasive adenocarcinoma manifesting as solid nodules for the first time. The 5-year RFS rate in the high-risk group was significantly lower than in the low-risk group (61.3% vs. 94.1%). Our study may aid in optimizing therapeutic strategies and improving survival benefits for those patients. [ABSTRACT FROM AUTHOR]

Although chimeric antigen receptor T cell (CAR-T) is a promising immunotherapy in hematological malignancies, there remain many obstacles to CART cell therapy for solid tumors. Identifying appropriate tumor-associated antigens (TAAs) is especially critical for success. Using a bioinformatics approach, we identified common potential TAAs for CAR-T cell immunotherapy in solid tumors. We used the GEO database as a training dataset to find differentially expressed genes (DEGs) and verified candidates using the TCGA database, obtaining seven common DEGs (HM13, SDC1, MST1R, HMMR, MIF, CD24, and PDIA4). Then, we used MERAV to analyze the expression of six genes in normal tissues to determine the ideal target genes. Finally, we analyzed tumor microenvironment factors. The results of major microenvironment factor analyses showed that MDSCs, CXCL1, CXCL12, CXCL5, CCL2, CCL5, TGF-ß, CTLA-4, and IFN-1 were significantly overexpressed in breast cancer. The expression of MST1R was positively correlated with TGF-ß, CTLA-4, and IFN-1. In lung adenocarcinoma, MDSCs, Tregs, CXCL12, CXCL5, CCL2, PD-L1, CTLA-4, and IFN-1 were significantly overexpressed in tumor tissues. The expression of MST1R was positively correlated with TGF-ß, CTLA-4, and IFN-1. In bladder cancer, CXCL12, CCL2, and CXCL5 were significantly overexpressed in tumor tissues. MST1R expression was positively correlated with TGF-ß. Our results demonstrate that MST1R has the potential as a new target antigen for treating breast cancer, lung adenocarcinoma, and bladder cancer and may be used as a progression indicator for bladder cancer. [ABSTRACT FROM AUTHOR]

Objectives: To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists. Methods: A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy. Results: The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847–0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882–0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737–0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779–0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793–0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673–0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05). Conclusions: The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert. Key Points: • Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening. [ABSTRACT FROM AUTHOR]

Background: The cytological features of interstitial pneumonia (IP)–related lung adenocarcinoma (LADC) have not been clearly described. This study aimed to describe its cytomorphological features, uncover potential problems in practical cytological diagnosis, and provide possible solutions. Methods: Bronchial brushing cytology samples from 40 IP‐related LADC cases (the IP group) and 110 control cases (LADC unrelated to IP; the non‐IP group) were analyzed. All patients underwent surgery after brushing cytology, and their histopathological subtypes were determined. The authors reviewed the cytological features and focused particularly on cytoplasmic mucin production. Results: In the IP group, neoplastic cells with cytoplasmic mucin were detected at a significantly higher frequency (44.4% [8 of 18] vs. 6.3% [4 of 64]), and most of them were invasive mucinous adenocarcinomas (IMAs). Twenty‐two of the 40 LADC cases in the IP group failed to be judged as "malignant/positive" (thus, they were judged to be "equivocal and/or negative"). The frequency of equivocal and/or negative judgments was 55.0% (22 of 40) in the IP group and 41.8% (46 of 110) in the non‐IP group. The cytological diagnosis of IMA was difficult because it showed only slight nuclear atypia. Therefore, the authors examined the immunocytochemical expression of hepatocyte nuclear factor 4α (HNF4α), a diagnostic marker for IMA. As a result, four of the six cases that were judged to be equivocal in the IP group showed positive signals and could be retrospectively judged as malignant/positive. Conclusions: The cytological diagnosis of IP‐related LADC may be more difficult because of the larger proportion of IMA. Immunocytochemistry for HNF4α can be used to improve diagnostic confidence in IP‐related LADC. The cytological diagnosis of interstitial pneumonia (IP)–related lung adenocarcinoma (LADC) may be more difficult because of the larger proportion of invasive mucinous adenocarcinoma. Immunocytochemistry for hepatocyte nuclear factor 4α can be used to improve diagnostic confidence in IP‐related LADC. [ABSTRACT FROM AUTHOR]

BACKGROUND: As a tumor suppressor in cancers, STK11 is repressed by mutation or epigenetic inactivation of malignant progression. However, the mechanism by which dysregulated STK11 affects lung adenocarcinoma (LUAD) is elusive. METHODS: Differential expression analysis was performed on mRNA data downloaded from The Cancer Genome Atlas (TCGA)-LUAD. Referring to publications, we determined the target mRNA, and predicted its upstream transcription factor (TF) via bioinformatics approaches. NFIC and STK11 expression levels in LUAD cells were examined by qRT -PCR and western blot. Cell function assays were performed to investigate how overexpressed STK11 affected LUAD cell proliferation, migration, invasion, and apoptosis. The targeted relationship between STK11 and NFIC was validated via chromatin immunoprecipitation (ChIP) and dual-luciferase assays. Rescue assay validated the regulation of the NFIC/STK11 axis on LUAD cell malignant phenotypes. RESULTS: STK11 was prominently downregulated in LUAD cell lines. Overexpressed STK11 notably repressed LUAD cell proliferation, migration and invasion, and enhanced cell apoptosis. Further, bioinformatics analysis revealed that NFIC was an upstream TF of STK11, which was prominently downregulated in LUAD. That NFIC bound to the STK11 promoter region was confirmed by ChIP and dual-luciferase assays. Rescue assay suggested that overexpressed NFIC suppressed the malignant progression of LUAD, while silenced STK11 might reverse such suppression. CONCLUSIONS: Collectively, the TF NFIC repressed the malignant progression of LUAD by activating the transcription of STK11, suggesting that targeting NFIC/STK11 axis may be an effective strategy for the clinical treatment of LUAD. [ABSTRACT FROM AUTHOR]

Purpose: Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. Materials and methods: A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity. Results: No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05). Conclusions: The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Lung adenocarcinomas manifesting as subsolid nodules (SSN‐LUADs) possess distinct dormant behaviour. This study was designed to compare the immune landscapes of normal lungs (nLungs), SSN‐LUADs and LUADs manifesting as solid nodules (SN‐LUADs) so as to better understand the status of anti‐tumour immunity in SSN‐LUADs. Mass cytometry by time‐of‐flight analysis was performed on 299, 570 single cells from nLung, SSN‐LUAD and SN‐LUAD tissues. The immune cells were identified by phenotype, and the percentages of different immune cell subclusters were compared between SSN‐LUADs, SN‐LUADs and nLungs. Elevated percentage of CD8+ T cells were identified in SSN‐LUADs compared with in nLungs and SN‐LUADs. Elevated CD56bright NK cells and decreased CD56dim NK cells were identified in both SSN‐LUADs and SN‐LUADs compared with in nLungs. The immune landscape of SSN‐LUAD fits the theory of equilibrium phase of immunoediting, thus functional adaptive anti‐tumour immunity but impaired innate anti‐tumour immunity potentially contributes to the maintaining of its dormant behaviour. [ABSTRACT FROM AUTHOR]

Background: Non‐small cell lung cancer (NSCLC) comprises the majority (~85%) of all lung tumors, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being the most frequently diagnosed histological subtypes. Multi‐modal omics profiling has been carried out in NSCLC, but no studies have yet reported a unique metabolite‐related gene signature and altered metabolic pathways associated with LUAD and LUSC. Methods: We integrated transcriptomics and metabolomics to analyze 30 human lung tumors and adjacent noncancerous tissues. Differential co‐expression was used to identify modules of metabolites that were altered between normal and tumor. Results: We identified unique metabolite‐related gene signatures specific for LUAD and LUSC and key pathways aberrantly regulated at both transcriptional and metabolic levels. Differential co‐expression analysis revealed that loss of coherence between metabolites in tumors is a major characteristic in both LUAD and LUSC. We identified one metabolic onco‐module gained in LUAD, characterized by nine metabolites and 57 metabolic genes. Multi‐omics integrative analysis revealed a 28 metabolic gene signature associated with poor survival in LUAD, with six metabolite‐related genes as individual prognostic markers. Conclusions: We demonstrated the clinical utility of this integrated metabolic gene signature in LUAD by using it to guide repurposing of AZD‐6482, a PI3Kβ inhibitor which significantly inhibited three genes from the 28‐gene signature. Overall, we have integrated metabolomics and transcriptomics analyses to show that LUAD and LUSC have distinct profiles, inferred gene signatures with prognostic value for patient survival, and identified therapeutic targets and repurposed drugs for potential use in NSCLC treatment. [ABSTRACT FROM AUTHOR]

Copyright of Turkiye Klinikleri Archives of Lung is the property of Turkiye Klinikleri and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A 67-year-old man visited our plastic surgery clinic complaining of a palpable protruding mass (2.0x2.5 cm) in the right occipital region. To establish an appropriate treatment plan for the cystic mass, brain magnetic resonance imaging was performed. A 2.2 cm nodular lesion with peripheral enhancement in the right occipital region of the scalp was confirmed. In addition, two rim-enhancing nodular lesions up to 9 mm with marked perilesional edema in the right frontal lobe were confirmed. The findings suggested metastasis from cancer. After further evaluations, a mass in the right lower lung field was identified as adenocarcinoma of the lung. Histological examination characterized the excised lesion as a cutaneous metastasis from lung adenocarcinoma. This case report shows that a cystic mass, which commonly occurs in the scalp, may indicate lung cancer. In particular, if a cystic mass of the scalp is identified in a person at high risk for lung cancer, appropriate evaluation and urgent treatment should be performed. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the CT and PET-CT features of solitary pulmonary capillary hemangioma (SPCH) with clinicopathologic correlations. This retrospective study included 17 patients with histologically proven SPCH from four tertiary institutions. The clinical, pathological and imaging findings of SPCH were reviewed. The CT features assessed included lesion location, size, density, contour, margin, enhancement, presence of air bronchogram, perivascular lucency and pleural retraction, and 18F-fluorodeoxyglucose uptake on PET-CT. Changes in the size during the follow-up period were also evaluated. Imaging features were correlated with the clinicopathologic findings. The mean age of the patients was 47 years (range 30–60 years). All SPCHs were incidentally detected during screening CT examinations (n = 13, 76%) or during cancer work-up (n = 4, 24%). Most SPCHs appeared as part-solid nodules (n = 15, 88%), the remaining appeared as a pure ground-glass nodule or a pure solid nodule, respectively. Most had smooth contours (n = 16, 94%), while one had a lobulated contour. Nine SPCHs (53%) showed ill-defined margins. Air bronchogram was present in ten (59%) SPCHs, and perivascular lucency in two (12%). All SPCHs exhibited hypoattenuation on contrast-enhanced CT and hypometabolism on PET-CT. During the follow-up period (mean 14.8 ± 17.7 months), the lesions showed no change in size or density in ten SPCHs (59%), decreased or fluctuation in size and density in three (18%). SPCH is often incidentally detected in young and middle-aged adults, commonly as an ill-defined part-solid nodule that may accompany air bronchogram, perivascular lucency, and fluctuation in size or density on CT and hypometabolism on PET-CT. [ABSTRACT FROM AUTHOR]

Overexpression of OCIAD2 in lung adenocarcinoma has already been reported in several research articles, but the molecular mechanism involved remains unknown. Promoter CpG methylation is a representative form of epigenetic gene regulation, and a considerable number of tumor suppressor genes show hypermethylation in many cancers. In contrast, promoter CpG hypomethylation causes oncogene overexpression, resulting in carcinogenesis and malignant progression. In the present study, we investigated the CpG methylation and expression status of OCIAD2 using tumor tissues and adjacent normal tissues from seven cases of lung adenocarcinoma. We also examined the relationship between CpG methylation status and outcome in 58 patients with adenocarcinoma. Pyrosequencing showed that CpG sites in OCIAD2 promoter regions were more frequently demethylated in tumor tissues than in adjacent normal tissues, and reverse transcription‐quantitative polymerase chain reaction revealed overexpression of OCIAD2 in lung adenocarcinoma. There was a correlation between OCIAD2 CpG demethylation and the level of mRNA expression, and statistical analysis showed that CpG hypomethylation of OCIAD2 was associated with poor outcomes. Our results suggest that overexpression of OCIAD2 might be caused mainly by CpG hypomethylation and that OCIAD2 methylation status might be a useful prognostic indicator in lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether radiomics can help radiologists and thoracic surgeons accurately predict invasive adenocarcinoma (IAC) manifesting as part-solid nodules (PSNs) with solid components <6 mm and provide a basis for rational clinical decision-making. Materials and Methods: In total, 1,210 patients (mean age ± standard deviation: 54.28 ± 11.38 years, 374 men and 836 women) from our hospital and another hospital with 1,248 PSNs pathologically diagnosed with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or IAC were enrolled in this study. Among them, 1,050 cases from our hospital were randomly divided into a derivation set (n = 735) and an internal validation set (n = 315), 198 cases from another hospital were used for external validation. Each labeled nodule was segmented, and 105 radiomics features were extracted. Least absolute shrinkage and selection operator (LASSO) was used to calculate Rad-score and build the radiomics model. Multivariable logistic regression was conducted to identify the clinicoradiological predictors and establish the clinicalradiographic model. The combined model and predictive nomogram were developed based on identified clinicoradiological independent predictors and Rad-score using multivariable logistic regression analysis. The predictive performances of the three models were compared via receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was performed on both the internal and external validation sets to evaluate the clinical utility of the nomogram. Results: The radiomics model showed superior predictive performance than the clinical-radiographic model in both internal and external validation sets (Az values, 0.884 vs. 0.810, p = 0.001; 0.924 vs. 0.855, p < 0.001, respectively). The combined model showed comparable predictive performance to the radiomics model (Az values, 0.887 vs. 0.884, p = 0.398; 0.917 vs. 0.924, p = 0.271, respectively). The clinical application value of the nomogram developed based on the Rad-score, maximum diameter, and lesion shape was confirmed, and DCA demonstrated that application of the Rad-score would be beneficial for radiologists predicting invasive lesions. Conclusions: Radiomics has the potential as an independent diagnostic tool to predict the invasiveness of PSNs with solid components <6 mm. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oncology is the property of Chinese Journal of Oncology / Zhonghua Zhongliu Zazhi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: To evaluate the long‐term outcomes after surgical resection for stage I lung adenocarcinoma based on the percentage of lepidic component (LC) and invasive tumor size (IS). Methods: The clinicopathological characteristics of 1049 patients with stage I lung adenocarcinoma who underwent surgery between 2006 and 2016 were retrospectively reviewed. Tumors were categorized into groups: A (LC ≥ 50%) and B (LC < 50%). Groups A0 and A1 consisted of minimally invasive adenocarcinomas (MIA) and other lepidic‐predominant invasive adenocarcinomas, respectively. Group B was categorized into B1 (IS ≤ 1 cm), B2 (1 < IS≤2 cm), and B3 (2 < IS≤3 cm) by invasive tumor size and divided into subgroups (B1[lep+]/[lep−], B2[lep+]/[lep−], and B3[lep+]/[lep−]) according to the presence[lep+] or absence[lep−] of LCs. Cumulative incidence of recurrence (CIR) and cancer‐specific survival (CSS) were examined. Results: LC decreased with increasing IS. Only 24 (8.5%) tumors in group A had an IS >1 cm. 10‐year CIR and CSS were 15.2% and 86.0%. LC and IS were found to be independent predictors of CSS. Patients in group A had 1.4% 10‐year CIR and 100% 10‐year CSS. In group B, a significantly higher CIR and worse CSS were observed as IS increased (p < 0.001), but LC was not a predictor for CSS (p = 0.593). No significant differences in CIR or CSS were found in presence of LC or not when LC < 50% (B1[lep+]/[lep−], B2[lep+]/[lep−], and B3[lep+]/[lep−]: p = 0.36/0.48, p = 0.82/0.94, and p = 0.90/0.37, respectively). Conclusions: LC≥50% tumors demonstrated excellent prognosis regardless of IS. The outcomes of LC < 50% tumors were well predicted by IS, corresponding to the T‐staging system. The predictive value of LC for prognosis became insignificant. [ABSTRACT FROM AUTHOR]

Purpose The incidence of BRAF V600E mutation in non–small cell lung carcinoma (NSCLC) is lower than 2%, which poses difficulties in finding legitimate patients for targeted therapy. We investigated the predictive factors pertaining to BRAF V600E and the effectiveness of the VE1 antibody as a screening method for patient selection. Materials and Methods The study was designed into two steps. In a first group, BRAF-mutated NSCLCs were identified from sequencing data to determine the features of BRAF V600E mutation. The results of the first group helped the collection of adenocarcinomas with a papillary or micropapillary pattern but without epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) alterations as a second group so that the frequency of BRAF V600E mutation could be calculated. The sensitivity and specificity of the VE1 were compared with BRAF V600E status. Results Among 39 BRAF-mutated NSCLCs in the first group, 20 (51%) were V600E. BRAF V600E mutation was more common in female patients and showed no significant correlation with smoking status. Nineteen cases were adenocarcinomas without EGFR and ALK alterations. The most common patterns of invasion were papillary and micropapillary along with central fibrosis. The sensitivity and specificity of the VE1 were 90.0% and 92.3%, respectively. In the second group, 6.7% of cases were VE1-positive, indicating that the prevalence was significantly higher than that reported in previous studies (0.3%-1.8%). Conclusion BRAF V600E-mutated NSCLCs could be enriched with the application of clinicopathologic parameters, which are not perfect. Therefore, additional VE1 immunohistochemistry may be useful as a screening method. [ABSTRACT FROM AUTHOR]

Objectives: To establish a multi-classification model for precisely predicting the invasiveness (pre-invasive adenocarcinoma, PIA; minimally invasive adenocarcinoma, MIA; invasive adenocarcinoma, IAC) of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: By the inclusion and exclusion criteria, this retrospective study enrolled 346 patients (female, 297, and male, 49; age, 55.79 ± 10.53 (24-83)) presenting as pGGNs from 1292 consecutive patients with pathologically confirmed lung adenocarcinoma. A total of 27 clinical were collected and 1409 radiomics features were extracted by PyRadiomics package on python. After feature selection with L2,1-norm minimization, logistic regression (LR), extra w(ET) and gradient boosting decision tree (GBDT) were used to construct the three-classification model. Then, an ensemble model of the three algorithms based on model ensemble strategy was established to further improve the classification performance. Results: After feature selection, a hybrid of 166 features consisting of 1 clinical (short-axis diameter, ranked 27th) and 165 radiomics (4 shape, 71 intensity and 90 texture) features were selected. The three most important features are wavelet-HLL_firstorder_Minimum, wavelet-HLL_ngtdm_Busyness and square_firstorder_Kurtosis. The hybrid-ensemble model based on hybrid clinical-radiomics features and the ensemble strategy showed more accurate predictive performance than other models (hybrid-LR, hybrid-ET, hybrid-GBDT, clinical-ensemble and radiomics-ensemble). On the training set and test set, the model can obtain the accuracy values of 0.918 ± 0.022 and 0.841, and its F1-scores respectively were 0.917 ± 0.024 and 0.824. Conclusion: The multi-classification of invasive pGGNs can be precisely predicted by our proposed hybrid-ensemble model to assist patients in the early diagnosis of lung adenocarcinoma and prognosis. [ABSTRACT FROM AUTHOR]

Background: To investigate the prognostic value of magnetic resonance imaging (MRI) findings in the prognosis of patients with leptomeningeal metastasis from lung adenocarcinoma. Methods: Patients with lung adenocarcinoma complicated with cytologically confirmed leptomeningeal metastasis who visited Peking Union Medical College Hospital (blinded for review) between January 2012 and July 2019 were retrospectively reviewed. We collected the patients' clinical and neuroimaging findings and pathological data. The presence of leptomeningeal enhancement on initial contrast MRI was used to divide patients into MRI‐positive and MRI‐negative groups. Univariate and multivariate analyses were performed to evaluate prognostic factors. Results: Eighty‐six patients (38 men and 48 women; median age = 56 [range, 25–80]) were included. Seventy‐three patients (84.9%) had targetable genetic alterations. Only 30 patients (34.88%) had leptomeningeal enhancement on initial contrast MRI. No significant differences were observed in the distribution of demographics, driver gene status, intracranial pressure, complicated brain/spinal metastasis, or treatment strategies between the two groups. The median overall survival of patients in the MRI‐positive group was significantly shorter than that in the negative group (182 days vs. 352 days, p = 0.036). Cox regression analysis indicated that the presence of leptomeningeal enhancement on the initial diagnostic magnetic resonance imaging was an independent predictor of an unfavourable prognosis of leptomeningeal metastasis (hazard ratio = 1.707, p = 0.044). Conclusions: This is the first time that positive initial contrast‐enhanced magnetic resonance imaging of the neuroaxis has been proposed as a risk factor for the prognosis of leptomeningeal metastasis from lung adenocarcinoma with contemporary survival data. [ABSTRACT FROM AUTHOR]

Background: The treatment efficacy of angiogenesis inhibitor could be underestimated at an early stage based on tumor volume changes. Intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) can quantitatively assess tumors at the cellular level, but it is unclear whether it can provide useful information for assessing treatment response of anti‐angiogenic treatment for lung adenocarcinoma. Purpose To determine the use of IVIM‐DWI for non‐invasive monitoring of the early response to anti‐angiogenic treatment in the orthotopic transplantation of lung adenocarcinoma model. Study Type: Prospective. Population Thirty‐seven nude mice were randomized into two groups: treatment group (received bevacizumab + cisplatin, N = 20) and control group (received saline, N = 17). Field Strength/Sequence: Single‐shot turbo spin‐echo (TSE) IVIM‐DWI, TSE T2‐weighted imaging at 3.0 T. Assessment Tumor volume, IVIM parameters (apparent diffusion coefficient [ADC], diffusivity [D], perfusion fraction [f], and pseudo‐diffusion coefficient [D*]) were measured before and 2 hours, 3, 7, 10 and 14 days after treatment. Regions of interest were manually drawn along the inner edge of the tumor by two radiologists with 5 and 10‐year experience in magnetic resonance imaging. Pathological examinations (hematoxylin and eosin stain, cluster of differentiation 34) were performed. Statistical Tests: Kolmogorov–Smirnov test, repeated‐measure two‐way analysis of variance test, Mann–Whitney U test, Pearson correlation analysis, receiver operating characteristic curve. P < 0.05 was considered statistically significant. Results: The tumor volume of the two groups was significantly different only on day 14 (control group vs. treatment group, 43.15 ± 18.28 mm3 vs. 28.41 ± 1.71 mm3). ADC2h, ADC10d, D2h, D7d, D10d, and D14d were significantly higher, while f10d and f14d were significantly lower in the treatment group compared to those of the control group. Both the △ADC2h (r = −0.631) and △D2h (r = −0.700) showed moderate correlations with the relative tumor volume on day 14. Data Conclusion: IVIM has the potential to predict and monitor the early response to anti‐angiogenic treatment, earlier than size changes, for lung adenocarcinoma. Level of Evidence: 2 Technical Efficacy: Stage 4 [ABSTRACT FROM AUTHOR]

Simple Summary: Epithelial growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the first-line therapy for patients with advanced-stage lung adenocarcinoma with EGFR mutations. However, 17–31% of these patients do not respond to therapy, making early evaluation of treatment response crucial. This prospective study investigates the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for timely prediction of response and survival of these patients. We evaluated 30 patients with stage IIIB/IV lung adenocarcinomas and EGFR mutations, receiving first-line EGFR-TKI therapy. 18F-FDG PET/CT was performed before and two weeks after initiation of treatment. Positron Emission Tomography Response Criteria in Solid Tumors served as an independent predictor of non-progressive disease; baseline and change of metabolic tumor volume represented independent predictors of progression-free survival and overall survival, respectively. Therefore, 18F-FDG PET/CT is an early predictor of outcomes and individual prognosis of patients with stage IIIB/IV lung adenocarcinomas and EGFR mutations receiving first-line EGFR-TKI therapy. This study aims to investigate the role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in early prediction of response and survival following epithelial growth factor receptor (EGFR)–tyrosine kinase inhibitor (TKI) therapy in patients with advanced lung adenocarcinomas and EGFR mutations. Thirty patients with stage IIIB/IV lung adenocarcinomas and EGFR mutations receiving first-line EGFR-TKIs were prospectively evaluated between November 2012 and May 2015. EGFR mutations were quantified by delta cycle threshold (dCt). 18F-FDG PET/CT was performed before and 2 weeks after treatment initiation. PET response was assessed based on PET Response Criteria in Solid Tumors (PERCIST). Baseline and percentage changes in the summed standardized uptake value, metabolic tumor volume (bsumMTV and ΔsumMTV, respectively), and total lesion glycolysis of ≤5 target lesions/patient were calculated. The association between parameters (clinical and PET) and non-progression disease after 3 months of treatment in CT based on the Response Evaluation Criteria in Solid Tumors Version 1.1 (nPD3mo), progression-free survival (PFS), and overall survival (OS) were tested. The median follow-up time was 19.6 months. The median PFS and OS were 12.0 and 25.3 months, respectively. The PERCIST criteria was an independent predictor of nPD3mo (p = 0.009), dCt (p = 0.014) and bsumMTV (p = 0.014) were independent predictors of PFS, and dCt (p = 0.014) and ΔsumMTV (p = 0.005) were independent predictors of OS. 18F-FDG PET/CT achieved early prediction of outcomes in patients with advanced lung adenocarcinomas and EGFR mutations receiving EGFR-TKIs. [ABSTRACT FROM AUTHOR]

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Molecular targeted therapies are the standard of care for front‐line treatment of metastatic non‐small‐cell lung cancers (NSCLCs) harboring driver gene mutations. However, despite the initial dramatic responses, the emergence of acquired resistance is inevitable. Acquisition of secondary mutations in the target gene (on‐target resistance) is one of the major mechanisms of resistance. The mouse pro‐B cell line Ba/F3 is dependent on interleukin‐3 for survival and proliferation. Upon transduction of a driver gene, Ba/F3 cells become independent of interleukin‐3 but dependent on the transduced driver gene. Therefore, the Ba/F3 cell line has been a popular system to generate models with oncogene dependence and vulnerability to specific targeted therapies. These models have been used to estimate oncogenicity of driver mutations or efficacies of molecularly targeted drugs. In addition, Ba/F3 models, together with N‐ethyl‐N‐nitrosourea mutagenesis, have been used to derive acquired resistant cells to investigate on‐target resistance mechanisms. Here, we reviewed studies that used Ba/F3 models with EGFR mutations, ALK/ROS1/NTRK/RET fusions, MET exon 14 skipping mutations, or KRAS G12C mutations to investigate secondary/tertiary drug resistant mutations. We determined that 68% of resistance mutations reproducibly detected in clinical cases were also found in Ba/F3 models. In addition, sensitivity data generated with Ba/F3 models correlated well with clinical responses to each drug. Ba/F3 models are useful to comprehensively identify potential mutations that induce resistance to molecularly targeted drugs and to explore drugs to overcome the resistance. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to discuss whether a diameter of 3 cm is a threshold for diagnosing lung adenocarcinomas presenting with radiological pure ground-glass mass (PGGM, pure ground-glass opacity > 3 cm) as adenocarcinomas in situ or minimally invasive adenocarcinomas (AIS-MIAs). Another aim was to identify CT features and patient prognosis that differentiate AIS-MIAs from invasive adenocarcinomas (IACs) in patients with PGGMs. Methods: From June 2007 to October 2015, 69 resected PGGMs with HRCT and followed up for ≥ 5 years were included in this study and divided into AIS-MIA (n = 13) and IAC (n = 56) groups. Firth's logistic regression model was performed to determine CT characteristics that helped distinguish IACs from AIS-MIAs. The discriminatory power of the significant predictors was tested with the area under the receiver operating characteristics curve (AUC). Disease recurrence was also evaluated. Results: Univariable and multivariable analyses identified that the mean CT attenuation (odds ratio: 1.054, p = 0.0087) was the sole significant predictor for preoperatively discriminating IACs from AIS-MIAs in patients with PGGMs. The CT attenuation had an excellent differentiating accuracy (AUC: 0.981), with the optimal cut-off value at −600 HU (sensitivity: 87.5%; specificity: 100%). Additionally, no recurrence was observed in patients manifesting with PGGMs > 3 cm, and the 5-year recurrence-free survival and overall survival rates were both 100%, even in cases of IAC. Conclusions: This study demonstrated that PGGMs > 3 cm could still be AIS-MIAs. When PGGMs are encountered in clinical practice, the CT value may be the only valuable parameter to preoperatively distinguish IACs from AIS-MIAs. Key Points: • Patients with pure ground-glass opacity > 3 cm in diameter are rare but can be diagnosed as adenocarcinomas in situ or minimally invasive adenocarcinomas. • The mean CT attenuation is the sole significant CT parameter that differentiates invasive adenocarcinoma from adenocarcinoma in situ or minimally invasive adenocarcinoma in patients with pure ground-glass opacity > 3 cm. • Lung adenocarcinoma with pure ground-glass opacity > 3 cm has an excellent prognosis, even in cases of invasive adenocarcinoma. [ABSTRACT FROM AUTHOR]

A 76‐year‐old man was referred to our hospital with a cough. Chest computed tomography (CT) revealed a 45‐mm mass in the lingular segment of the left upper lobe. Transbronchial tumor biopsies showed adenocarcinoma. Contrast‐enhanced CT and bone scintigraphy revealed lung, pleura, and bone metastases. The patient was diagnosed with left upper lobe adenocarcinoma cT2bN3M1c stage IVB. A genetic analysis of the primary tumor using the Oncomine Dx Target Test Multi‐CDx system revealed positivity for epidermal growth factor receptor (EGFR) (L858R) and CTNNB1 mutations. Based on these findings, the patient was treated with osimertinib (80 mg/day) as first‐line therapy. Six months later, the tumor increased in size, indicating progressive disease. Osimertinib was stopped and second‐line therapy with carboplatin (area under the curve 5) and pemetrexed (500 mg/m2) was initiated. After three cycles of chemotherapy, the patient developed dementia and disorientation. Contrast‐enhanced magnetic resonance imaging of the head showed miliary brain metastases. Miliary dissemination is a rare form of brain metastasis. Miliary patterns of lung metastases have been strongly associated with the EGFR exon 19 deletion. The radiological features of miliary brain metastases of non‐small cell lung cancer with the exon 19 deletion have been reported. To the best of our knowledge, this is the first case report of lung cancer with miliary brain metastases and co‐mutations of EGFR (L858R) and CTNNB1. In conclusion, co‐mutations of EGFR (L858R) and CTNNB1 and the discontinuation of EGFR‐tyrosine kinase inhibitor may contribute to the development of miliary brain metastases. Further case studies are warranted. [ABSTRACT FROM AUTHOR]

Objectives: The interpretability of convolutional neural networks (CNNs) for classifying subsolid nodules (SSNs) is insufficient for clinicians. Our purpose was to develop CNN models to classify SSNs on CT images and to investigate image features associated with the CNN classification. Methods: CT images containing SSNs with a diameter of ≤ 3 cm were retrospectively collected. We trained and validated CNNs by a 5-fold cross-validation method for classifying SSNs into three categories (benign and preinvasive lesions [PL], minimally invasive adenocarcinoma [MIA], and invasive adenocarcinoma [IA]) that were histologically confirmed or followed up for 6.4 years. The mechanism of CNNs on human-recognizable CT image features was investigated and visualized by gradient-weighted class activation map (Grad-CAM), separated activation channels and areas, and DeepDream algorithm. Results: The accuracy was 93% for classifying 586 SSNs from 569 patients into three categories (346 benign and PL, 144 MIA, and 96 IA in 5-fold cross-validation). The Grad-CAM successfully located the entire region of image features that determined the final classification. Activated areas in the benign and PL group were primarily smooth margins (p < 0.001) and ground-glass components (p = 0.033), whereas in the IA group, the activated areas were mainly part-solid (p < 0.001) and solid components (p < 0.001), lobulated shapes (p < 0.001), and air bronchograms (p < 0.001). However, the activated areas for MIA were variable. The DeepDream algorithm showed the image features in a human-recognizable pattern that the CNN learned from a training dataset. Conclusion: This study provides medical evidence to interpret the mechanism of CNNs that helps support the clinical application of artificial intelligence. Key Points: • CNN achieved high accuracy (93%) in classifying subsolid nodules on CT images into three categories: benign and preinvasive lesions, MIA, and IA. • The gradient-weighted class activation map (Grad-CAM) located the entire region of image features that determined the final classification, and the visualization of the separated activated areas was consistent with radiologists' expertise for diagnosing subsolid nodules. • DeepDream showed the image features that CNN learned from a training dataset in a human-recognizable pattern. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to determine the invasiveness of ground-glass nodules (GGNs) using a 3D multi-task deep learning network. Methods: We propose a novel architecture based on 3D multi-task learning to determine the invasiveness of GGNs. In total, 770 patients with 909 GGNs who underwent lung CT scans were enrolled. The patients were divided into the training (n = 626) and test sets (n = 144). In the test set, invasiveness was classified using deep learning into three categories: atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA). Furthermore, binary classifications (AAH/AIS/MIA vs. IA) were made by two thoracic radiologists and compared with the deep learning results. Results: In the three-category classification task, the sensitivity, specificity, and accuracy were 65.41%, 82.21%, and 64.9%, respectively. In the binary classification task, the sensitivity, specificity, accuracy, and area under the ROC curve (AUC) values were 69.57%, 95.24%, 87.42%, and 0.89, respectively. In the visual assessment of GGN invasiveness of binary classification by the two thoracic radiologists, the sensitivity, specificity, and accuracy of the senior and junior radiologists were 58.93%, 90.51%, and 81.35% and 76.79%, 55.47%, and 61.66%, respectively. Conclusions: The proposed multi-task deep learning model achieved good classification results in determining the invasiveness of GGNs. This model may help to select patients with invasive lesions who need surgery and the proper surgical methods. Key Points: • The proposed multi-task model has achieved good classification results for the invasiveness of GGNs. • The proposed network includes a classification and segmentation branch to learn global and regional features, respectively. • The multi-task model could assist doctors in selecting patients with invasive lesions who need surgery and choosing appropriate surgical methods. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate a deep learning–based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size. Methods: Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2 cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis. Results: The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for the training, tuning, and validation sets, respectively. The mean AUC of the radiologists was 0.835 in the validation set, without significant differences between radiologists and the model (p = 0.97). The sensitivity, specificity, and accuracy of the model were 71% (32/45), 87% (39/45), and 79% (71/90), respectively, whereas the corresponding values of the radiologists were 75.2% (203/270), 76.7% (207/270), and 75.9% (410/540) with a 5-mm threshold for the solid portion size. Conclusions: The performance of the model for differentiating IPA from preinvasive lesions or MIA was comparable to that of the radiologists' measurements of solid portion size. Key Points: • A deep learning–based model differentiated IPA from preinvasive lesions or MIA with AUCs of 0.914 and 0.956 for the training and tuning sets, respectively. • In the validation set including subsolid nodules of 2 cm or smaller, the model showed an AUC of 0.833, being on par with the performance of the solid portion size measurements made by the radiologists (AUC, 0.835; p = 0.97). • SSNs with a solid portion measuring > 10 mm on CT showed a high probability of being IPA (positive predictive value, 93.5–100.0%). [ABSTRACT FROM AUTHOR]

Background: Paraneoplastic hyper-eosinophilia associated with metastatic lung adenocarcinoma is a rare finding and has been associated with a poor prognosis when present. Early hyper-eosinophilia appearing following non-small cell lung cancer (NSCLC) treatment with immune checkpoint inhibitors (ICI) has been previously reported with contradictory outcomes. Case summary: We present the case of an elderly man with newly diagnosed metastatic lung adenocarcinoma and baseline hyper-eosinophilia, treated with pembrolizumab, and showing evidence of significant and rapid disease progression suggestive of hyper-progressive disease, worsening baseline hyper-eosinophilia, and a fatal outcome within 1 month of therapy initiation. Conclusion: Pre-treatment hyper-eosinophilia could represent a predictive factor of an unfavorable response to ICI treatment in cases of NSCLC. Additional similar cases are needed to draw a more conclusive relationship. [ABSTRACT FROM AUTHOR]