Minimally Invasive Adenocarcinoma Research Articles

Genomic landscape and tumor mutational features of resected preinvasive to invasive lung adenocarcinoma

IntroductionAdenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are considered pre-invasive forms of lung adenocarcinoma (LUAD) with a 5-year recurrence-free survival of 100%. We investigated genomic profiles in early tumorigenesis and distinguished mutational features of preinvasive to invasive adenocarcinoma (IAC) for early diagnosis.MethodsMolecular information was obtained from a 689-gene panel in the 90 early-stage LUAD Chinese patients using next-generation sequencing. Gene signatures were identified between pathology subtypes, including AIS/MIA (n=31) and IAC (n=59) in this cohort. Mutational and clinicopathological information was also obtained from the Cancer Genome Atlas (TCGA) as a comparison cohort.ResultsA higher mutation frequency of TP53, RBM10, MUC1, CSMD, MED1, LRP1B, GLI1, MAP3K, and RYR2 was observed in the IAC than in the AIS/MIA group. The AIS/MIA group showed higher mutation frequencies of ERBB2, BRAF, GRIN2A, and RB1. Comparable mutation rates for mutually exclusive genes (EGFR and KRAS) across cohorts highlight the critical transition to invasive LUAD. Compared with the TCGA cohort, EGFR, KRAS, TP53, and RBM10 were frequently mutated in both cohorts. Despite limited gene mutation overlap between cohorts, we observed variant mutation types in invasive LUAD. Additionally, the tumor mutation burden (TMB) values were significantly lower in the AIS/MIA group than in the IAC group in both the Chinese cohort (P=0.0053) and TCGA cohort (P<0.01).ConclusionThese findings highlight the importance of distinguishing preinvasive from invasive LUAD in the early stages of LUAD and both pathology and molecular features in clinical practice, revealing genomic tumor heterogeneity and population differences.

Abstract CT278: [DREAM] Double lung transplant registry aimed for lung-limited malignancies - a prospective registry study for medically refractory cancers

Abstract BackgroundAdenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are listed as ‘special circumstances’ for lung transplantation according to a consensus document from the International Society for Heart and Lung Transplantation (ISHLT) (Weill D et al., 2015). Five studies have reported on lung transplantation in carefully selected subsets of patients with multifocal lung adenocarcinoma (Raemdonck DV et al., 2016; Glanville AR et al., 2018). The 5-year survival rate of bilateral sequential lung transplantation was estimated at over 50% in patients with multifocal BAC, invasive adenocarcinoma, and NSCLC (Ahmad U et al., 2012; Paloyan EB et al., 2000; Zorn GL et al., 2003; de Perrot M et al., 2004). Another a case series reported a post-transplant recurrence-free survival of 23 to 56 months in three patients (Garver RI et al., 1999). Moreover, there is an unmet need for patients who have lung-limited metastasis after successful treatment for primary tumors such as sarcomas or colorectal cancer (CRC). As with liver transplantation for CRC patients who have liver-confined metastases (Dueland S et at., 2020; Hernandez-Alejandro R et al., 2022), the applicability of lung transplantation in lung-limited metastatic patients should be explored. There is also an unmet need for patients with respiratory failure with a history of cancer in the last 5 years. MethodsThis is a prospective registration trial to evaluate outcomes of patients who undergo double lung transplantation for the treatment of the select groups of medically refractory cancers (primary lung cancers or metastatic cancers in lungs). Overall survival (OS), disease-free survival (DFS), allograft rejection (AR) and allograft survival (AS) as well as molecular and genetic biomarkers will be monitored to investigate the correlation with prognosis.The study duration will be 10 years including surveillance. Recruitment is to occur during the first 5 years of the study. The goal is to enroll 125 participants through the Northwestern Medicine Clinical Programs.Essential Criteria:1.The tumor should be without any extrapulmonary metastasis as determined by standard of care diagnostic and staging workup.2.All standard of care or experimental oncological treatments known to improve survival should have failed or deemed infeasible 3.Patients should meet the general criteria for lung transplant evaluation and listing Study cohorts:•Cohort A: Primary lung cancers - Examples include, but not limited to, invasive mucinous/non-mucinous non-small cell lung cancers and multifocal carcinomas.•Cohort B: Metastatic cancers to the lung only - Examples include, but not limited to, germ cell tumors, head & neck tumors, colorectal tumors, renal cell tumors, testicular cancers.•Cohort C: Respiratory failure with a history of cancer in the last 5 years- Examples include, but not limited to interstitial lung disease (ILD), pulmonary fibrosis (idiopathic or secondary), advanced chronic obstructive pulmonary disease (COPD), bronchiectasis, emphysema, cystic fibrosis (CF), emphysema due to alpha-1 antitrypsin deficiency, and pulmonary arterial hypertension (PAH) Clinical trial registry number : NCT05671887.Enrollment began November 16, 2022. Trial is open and continues to enroll participants as of Jan 8, 2024. Citation Format: Young Kwang Chae, Liam Il-Young Chung, Rade Tomic, Ankit Bharat. [DREAM] Double lung transplant registry aimed for lung-limited malignancies - a prospective registry study for medically refractory cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT278.

Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma.

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.

Abstract 3933: Comprehensive molecular characterization of mitochondrial mutational landscape across the evolution of lung adenocarcinoma

Abstract The human mitochondrial genome is prone to genetic alterations, which lead to somatic mutations, changes in mtDNA copy number and altered expression of respiratory chain subunits. This is in part due to the fact that mtDNA has a limited ability to repair itself when it is damaged. Although a buildup of somatic mutations in mtDNA has been associated with increased cancer risk, the role of mtDNA alterations in malignant transformation of lung adenocarcinoma has not been addressed. Therefore, a clear understanding of changes in mtDNA along the pathway of lung tumorigenesis is critical for identifying molecular biomarkers related to carcinogenesis and tumor progression. Like all solid tumors, lung adenocarcinoma is thought to be initiated and to progress through a series of genetic alterations, including changes in mtDNA. Lungs resected for primary adenocarcinomas often harbor minute discrete foci of cytologically atypical pneumocyte proliferations designated as atypical adenomatous hyperplasia (AAH). Evidence suggests that AAH represents an initial step in the progression to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and ultimately fully invasive adenocarcinoma (ADA). In this study we used a unique cohort of 52 patients with matched specimens (n=133) representing different steps of disease progression (AAH, AIS, and MIA) coupled with novel ultra-deep mitochondrial sequencing (mtDNA-Seq) method to assess the mtDNA mutational landscape throughout the continuum of lung adenocarcinoma development. Utilizing a custom bioinformatics workflow, somatic mutations were detected in a subset of the premalignant non-invasive lesions, with overall higher mutational load observed in MIA and ACA specimens. Additionally, large degree of inter-patient heterogeneity were observed for mtDNA copy numbers in conjunction with the clonal histological evolution of pulmonary neoplasia. Moreover, an appreciable number of coding and non-coding mutations from regulatory regions of the mtDNA were detectable in the precursor AAH lesions and the continuum of LUAD progression. Majority of shared mutations showed heteroplasmic transition with increase in fractional abundance in ADA, compared to the AHH lesions, thereby indicating spatial clonal expansion as they progressed histologically. Here we report the first comprehensive characterization of mitochondrial mutational landscape in dynamic evolution of lung adenocarcinoma, and provide insight into the heterogeneity of clonal events associated with the progression from noninvasive to malignant disease. Citation Format: Alka Singh, Santanu Dasgupta, Ashwin L. Koppayi, Lisa Rooper, Vasudha Mishra, David Sidransky, Nishant Agrawal, Karthik Suresh, Evgeny Izumchenko. Comprehensive molecular characterization of mitochondrial mutational landscape across the evolution of lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3933.

Pulmonary Adenocarcinoma In Situ and Minimally Invasive Adenocarcinomas in European Patients Have Less KRAS and More EGFR Mutations Compared to Advanced Adenocarcinomas.

Pulmonary adenocarcinoma (ADC) is a very diverse disease, both genetically and histologically, which displays extensive intratumor heterogeneity with numerous acquired mutations. ADC is the most common type of lung cancer and is believed to arise from adenocarcinoma in situ (AIS) which then progresses to minimally invasive adenocarcinoma (MIA). In patients of European ethnicity, we analyzed genetic mutations in AIS (n = 10) and MIA (n = 18) and compared the number of genetic mutations with advanced ADC (n = 2419). Using next-generation sequencing, the number of different mutations detected in both AIS (87.5%) and MIA (94.5%) were higher (p < 0.001) than in advanced ADC (53.7%). In contrast to the high number of mutations in Kirsten rat sarcoma virus gene (KRAS) in advanced ADC (34.6%), there was only one case of AIS with KRAS G12C mutation (3.5%; p < 0.001) and no cases of MIA with KRAS mutation (p < 0.001). In contrast to the modest prevalence of epidermal growth factor receptor (EGFR) mutations in advanced ADC (15.0%), the fraction of EGFR mutant cases was higher in both in AIS (22.2%) and MIA (59.5%; p < 0.001). The EGFR exon 19 deletion mutation was more common in both MIA (50%; n = 6/12) and ADC (41%; n = 149/363), whereas p.L858R was more prevalent in AIS (75%; n = 3/4). In contrast to pulmonary advanced ADC, KRAS driver mutations are less common, whereas mutations in EGFR are more common, in detectable AIS and MIA.

Radiologic Parameters Predicting the Histologic Invasiveness of Pure Ground-glass Nodules

BackgroundThis study aimed to investigate the diagnostic performance of combined computed tomography (CT) and fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) for predicting histologic invasiveness of pure ground-glass nodules (pGGNs). MethodsThe study analyzed 91 patients who underwent resection of pGGNs and examined the correlation of pathologic invasiveness with preoperative CT and FDG PET findings. ResultsOverall, 24, 36, and 31 patients had adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAD), respectively. Compared with AIS and MIA, IAD was significantly correlated with larger CT size (P = .001), maximum CT value (P = .026), and high maximum standardized uptake value (SUVmax; P < .001). Multivariable logistic analyses revealed that CT size (odds ratio [OR], 3.848; P = .019) and SUVmax (OR, 4.968; P = .009) were independent predictors of histologic invasiveness. Receiver operating characteristic curve analysis revealed that a cutoff CT size value of 18 mm predicted histologic invasiveness with a sensitivity and specificity of 65% and 80%, respectively; similarly, a cutoff SUVmax value of 1.5 predicted histologic invasiveness with a sensitivity and specificity of 61% and 90%, respectively. Of 20 lesions with CT size ≥18 mm and SUVmax ≥1.5, 16 (80%) were IAD. Of 54 lesions with CT size <18 mm and SUVmax <1.5, 46 (85%) were non-IAD lesions. Furthermore, all pGGNs with SUVmax >2.5 were IAD. ConclusionsCT size and SUVmax were significantly correlated with the histologic invasiveness of pGGNs. These factors may aid in determining optimal surgical procedures.

The differential diagnosis of lung precursor glandular lesions, micro-invasive adenocarcinoma, and invasive adenocarcinoma using low dose spectral computed tomography perfusion imaging.

Few studies about the association between computed tomography (CT) perfusion imaging parameters and invasiveness in lung adenocarcinoma (LUAD) have been conducted using low dose spectral CT perfusion imaging. The purpose of this study was to investigate application of spectral revolution CT low-dose perfusion imaging in the differential diagnosis of different pathological subtypes of LUAD. This was a cross-sectional study based on historical data from January 2018 to May 2019 in Peking University Cancer Hospital & Institute. A total of 62 cases were enrolled, including 2 cases of atypical adenomatous hyperplasia (AAH), 3 cases of adenocarcinoma in situ (AIS), 4 cases of minimally invasive adenocarcinoma (MIA), and 53 cases of invasive adenocarcinoma (IAC), all confirmed with pathology. The inclusion and exclusion criteria were regulated. Using Revolution low-dose CT perfusion imaging (GE, USA), the CT perfusion parameters of hemodynamics were obtained: blood flow (BF), blood volume (BV), impulse residue function time of arrival (IRF TO), maximum slope of increase (MSI), mean transit time (MTT), permeability surface area product (PS), positive enhancement integral (PEI), and maximum enhancement time (Tmax). Univariate analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare the differences of CT perfusion quantitative parameters among AAH, AIS, MIA, and IAC. Mann-Whitney test was used to compare the difference of CT perfusion imaging parameters between preinvasive lesions (AAH and AIS) and invasive lung cancer (MIA and IAC). Statistically significant differences in IRF TO were observed in LUAD with different invasiveness, namely, among AIS, MIA, and IAC groups (0.56±0.74 vs. 0.54±1.08 vs. 4.39±2.19, P=0.004). Statistically significant differences in IRF TO were also observed between pre-invasive lesions group (AAH and AIS) and invasive lung cancer group (MIA and IAC) (1.12±1.27 vs. 3.75±2.79, P=0.031), and between AAH + AIS + MIA groups and IAC group (0.83±1.13 vs. 4.12±2.69, P<0.001). There were no statistically significant differences in other CT perfusion parameters of hemodynamics among different pathological subtypes of LUAD (P>0.05). The low-dose perfusion parameter IRF TO of revolution CT has the potential to be employed in the differential diagnosis of different pathological subtypes of LUAD.

Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study

BackgroundRadiomics is a method within medical image analysis that involves the extraction of quantitative data from radiologic scans, often in conjunction with machine learning algorithms to phenotype disease appearance, prognosticate disease outcome, and predict treatment response. However, variance in CT scanner acquisition parameters, such as convolution kernels or pixel spacing, can impact radiomics texture feature values. PurposeThe extent to which the parameters influence radiomics features continues to be an active area of investigation. In this study, we describe a novel approach, Acquisition Impact on Radiomics Estimation (AcquIRE), to rank the impact of CT acquisition parameters on radiomic texture features. MethodsIn this work, we used three chest CT imaging datasets (n = 749 patients) from nine sites comprising: i) lung granulomas and adenocarcinomas (D1) (10 and 52 patients, respectively); ii) minimal and frank invasive adenocarcinoma (D2) (74 and 145 patients); and iii) early-stage NSCLC patients (D3) (315 patients). Datasets D2 and D3 were collected from four sites each, and D1 from a single site. For each patient, 744 texture features and nine acquisition parameters were extracted and utilized to evaluate which parameters impact radiomic features the most. The AcquIRE method establishes a relative assessment between acquisition parameters and radiomic texture featuresa through the creation of a classification model, which is then utilized to assess the rank of the acquisition parameters. ResultsAcross the use cases, CT software version and convolution kernel parameters were found to have the most variance. In D1, it was observed that the Haralick texture feature family was the least affected by variations in acquisition parameters, while the Gabor feature family was the most impacted. However, in datasets D2 and D3, the Gabor features were found to be the least affected. Our findings suggest that the impact on radiomic parameters is as much a function of the problem in question as it is acquisition parameters. ConclusionsThe software version and convolution kernel parameters impacted the radiomics feature the most.

Behind the Indolent Facade: Uncovering the Molecular Features and Malignancy Potential in Lung Minimally Invasive Adenocarcinoma by Single-Cell Transcriptomics.

The increased use of low-dose computed tomography screening has led to more frequent detection of early stage lung tumors, including minimally invasive adenocarcinoma (MIA). To unravel the intricacies of tumor cells and the immune microenvironment in MIA, this study performs a comprehensive single-cell transcriptomic analysis and profiles the transcriptomes of 156,447 cells from fresh paired MIA and invasive adenocarcinoma (IA) tumor samples, peripheral blood mononuclear cells, and adjacent normal tissue samples from three patients with synchronous multiple primary lung adenocarcinoma. This study highlights a connection and heterogeneity between the tumor ecosystem of MIA and IA. MIA tumor cells exhibited high expression of aquaporin-1 and angiotensin II receptor type 2 and a basal-like molecular character. Furthermore, it identifies that cathepsin B+ tumor-associated macrophages may over-activate CD8+ T cells in MIA, leading to an enrichment of granzyme K+ senescent CD8+ T cells, indicating the possibility of malignant progression behind the indolent appearance of MIA. These findings are further validated in 34 MIA and 35 IA samples by multiplexed immunofluorescence. These findings provide valuable insights into the mechanisms that maintain the indolent nature and prompt tumor progression of MIA and can be used to develop more effective therapeutic targets and strategies for MIA patients.

Vascular invasion predicts the subgroup of lung adenocarcinomas ≤2.0 cm at risk of poor outcome treated by wedge resection compared to lobectomy

BackgroundRecent randomized control trials (JCOG0802 and CALGB140503) have shown sublobar resection to be noninferior to lobectomy for non–small cell lung cancer (NSCLC) ≤2.0 cm. We have previously proposed histologic criteria stratifying lung adenocarcinoma into indolent low malignant potential (LMP) and aggressive angioinvasive adenocarcinomas, resulting in better prognostication than provided by World Health Organization grade. Here we determine whether pathologic classification is reproducible and whether subsets of adenocarcinomas predict worse outcomes when treated by wedge resection compared to lobectomy. MethodsA retrospective cohort of 108 recipients of wedge resection and 187 recipients of lobectomy for stage I/0 lung adenocarcinomas ≤2.0 cm was assembled from 2 institutions. All tumors were classified by a single pathologist, and interobserver reproducibility was assessed in a subset (n = 92) by 5 pathologists. ResultsAngioinvasive adenocarcinoma (21%-27% of cases) was associated with worse outcomes when treated with wedge resection compared to lobectomy (5-year recurrence-free survival, 57% vs 85% [P = .007]; 5-year disease-free survival [DSS], 70% vs 90% [P = .043]; 7-year overall survival, 37% vs 58% [P = .143]). Adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and LMP exhibited 100% 5-year DSS regardless of the surgical approach. Multivariable analysis showed that angioinvasion, tumor size, margin status, and extent of nodal sampling were significantly associated with recurrence but not with surgical procedure. There was substantial interobserver reproducibility among the pathologists for the diagnosis of angioinvasive adenocarcinoma (κ = 0.71) and the combined indolent AIS/MIA/LMP group (κ = 0.74). ConclusionsThe majority (∼75%) of lung adenocarcinomas ≤2 cm are adequately managed with wedge resection; however, angioinvasive adenocarcinomas (∼25%) treated by wedge resection with suboptimal nodal sampling exhibit poor outcomes, with a 40% to 45% rate of recurrence within 5 years and 60% to 65% overall mortality at 7 years.

Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography.

The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study.

The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground-glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. We developed and validated a three-dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre-training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end-to-end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). Our 3D deep transfer learning model provides a noninvasive, low-cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

Expression Changes in Programmed Death Ligand 1 from Precancerous Lesions to Invasive Adenocarcinoma in Subcentimeter Pulmonary Nodules: A Large Study of 2022 Cases in China.

This large-scale, multicenter, retrospective observational study aimed to evaluate the clinicopathological and molecular profiles associated with programmed death-ligand 1 (PD-L1) expression in precancerous lesions and invasive adenocarcinoma in subcentimeter pulmonary nodules. Patients with histologically confirmed atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (ADC) were included. PD-L1 expression was evaluated at each center using a PD-L1 immunohistochemistry 22C3 pharmDx kit (Agilent, Santa Clara, CA, USA). The tumor proportion score (TPS) cutoff values were set at ≥ 1% and ≥ 50%. A total of 2022 nodules from 1844 patients were analyzed. Of these, 9 (0.45%) nodules had PD-L1 TPS ≥ 50%, 187 (9.25%) had PD-L1 TPS 1-49%, and 1826 (90.30%) had PD-L1 TPS < 1%. A total of 378 (18.69%), 1016 (50.25%), and 628 (31.06%) nodules were diagnosed as AAH/AIS, MIA, and ADC, respectively, by pathology. A total of 1377 (68.10%), 591 (25.67%), and 54 (2.67%) nodules were diagnosed as pure ground-glass opacity (GGO), mixed GGO, and solid nodules, respectively, by computed tomography. There was a significant difference between PD-L1 expression and anaplastic lymphoma kinase (ALK) mutation status (P < 0.001). PD-L1 expression levels were significantly different from those determined using the International Association for the Study of Lung Cancer (IASLC) grading system (P < 0.001). PD-L1 expression was significantly associated with radiological and pathological invasiveness and driver mutation status in subcentimeter pulmonary nodules. The significance of PD-L1 expression in the evolution of early-stage lung adenocarcinoma requires further investigation.

Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study.

Broad generalization of radiomics-assisted models may be impeded by concerns about variability. This study aimed to evaluate the merit of combatting batch effect (ComBat) harmonization in reducing the variability of voxel size-related radiomics in both phantom and clinical study in comparison with image resampling correction method. A pulmonary phantom with 22 different types of nodules was scanned by computed tomography (CT) with different voxel sizes. The variability of voxel size-related radiomics features was evaluated using concordance correlation coefficient (CCC), dynamic range (DR), and intraclass correlation coefficient (ICC). ComBat and image resampling compensation methods were used to reduce variability of voxel size-related radiomics. The percentage of robust radiomics features was compared before and after optimization. Pathologically differential diagnosis of invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) (AIS-MIA group) was used for clinical validation in 134 patients. Before optimization, the number of excellent features in the phantom and clinical data was 26.12% and 32.31%, respectively. The excellent features were increased after image resampling and ComBat correction. For clinical optimization, the effect of the ComBat compensation method was significantly better than that of image resampling, with excellent features reaching 90.96% and poor features only amounting to 4.96%. In addition, the hierarchical clustering analysis showed that the first-order and shape features had better robustness than did texture features. In clinical validation, the area under the curve (AUC) of the testing set was 0.865 after ComBat correction. The ComBat harmonization can optimize voxel size-related CT radiomics variability in pulmonary nodules more efficiently than image resampling harmonization.

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence.

To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance. 187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve. The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05). For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.

Pulmonary MRI with ultra-short TE using single- and dual-echo methods: comparison of capability for quantitative differentiation of non- or minimally invasive adenocarcinomas from other lung cancers with that of standard-dose thin-section CT.

The purpose of this study was thus to compare capabilities for quantitative differentiation of non- and minimally invasive adenocarcinomas from other of pulmonary MRIs with ultra-short TE (UTE) obtained with single- and dual-echo techniques (UTE-MRISingle and UTE-MRIDual) and thin-section CT for stage IA lung cancer patients. Ninety pathologically diagnosed stage IA lung cancer patients who underwent thin-section standard-dose CT, UTE-MRISingle, and UTE-MRIDual, surgical treatment and pathological examinations were included in this retrospective study. The largest dimension (Dlong), solid portion (solid Dlong), and consolidation/tumor (C/T) ratio of each nodule were assessed. Two-tailed Student's t-tests were performed to compare all indexes obtained with each method between non- and minimally invasive adenocarcinomas and other lung cancers. Receiver operating characteristic (ROC)-based positive tests were performed to determine all feasible threshold values for distinguishing non- or minimally invasive adenocarcinoma (MIA) from other lung cancers. Sensitivity, specificity, and accuracy were then compared by means of McNemar's test. Each index showed significant differences between the two groups (p < 0.0001). Specificities and accuracies of solid Dlong for UTE-MRIDual2nd echo and CTMediastinal were significantly higher than those of solid Dlong for UTE-MRISingle and UTE-MRIDual1st echo and all C/T ratios except CTMediastinal (p < 0.05). Moreover, the specificities and accuracies of solid Dlong and C/T ratio were significantly higher than those of Dlong for each method (p < 0.05). Pulmonary MRI with UTE is considered at least as valuable as thin-section CT for quantitative differentiation of non- and minimally invasive adenocarcinomas from other stage IA lung cancers. Pulmonary MRI with UTE's capability for quantitative differentiation of non- and minimally invasive adenocarcinomas from other lung cancers in stage IA lung cancer patients is equal or superior to that of thin-section CT. • Correlations were excellent for pathologically examined nodules with the largest dimensions (Dlong) and a solid component (solid Dlong) for all indexes (0.95 ≤ r ≤ 0.99, p < 0.0001). • Pathologically examined Dlong and solid Dlong obtained with all methods showed significant differences between non- and minimally invasive adenocarcinomas and other lung cancers (p < 0.0001). • Solid tumor components are most accurately measured by UTE-MRIDual2nd echo and CTMediastinal, whereas the ground-glass component is imaged by UTE-MRIDual1st echo and CTlung with high accuracy. UTE-MRIDual predicts tumor invasiveness with 100% sensitivity and 87.5% specificity at a C/T threshold of 0.5.

Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images.

To evaluate the role of quantitative features of intranodular vessels based on deep learning in distinguishing pulmonary adenocarcinoma invasiveness. This retrospective study included 512 confirmed ground-glass nodules from 474 patients with 241 precursor glandular lesions (PGL), 126 minimally invasive adenocarcinomas (MIA), and 145 invasive adenocarcinomas (IAC). The pulmonary blood vessels were reconstructed on noncontrast computed tomography images using deep learning-based region-segmentation and region-growing techniques. The presence of intranodular vessels was evaluated based on the automatic calculation of vessel prevalence, vascular categories, and vessel volume percentage. Further comparisons were made between different invasive groups by the Mantel-Haenszel χ 2 test, χ 2 test, and analysis of variance. The detection rate of intranodular vessels in PGL (33.2%) was significantly lower than that of MIA (46.8%, P = 0.011) and IAC (55.2%, P < 0.001), while the vascular categories were similar (all P > 0.05). Vascular changes were more common in IAC and MIA than in PGL, mainly in increased vessel volume percentage (12.4 ± 19.0% vs. 6.3 ± 13.1% vs. 3.9 ± 9.4%, P < 0.001). The average intranodular artery and vein volume percentage of IAC (7.5 ± 14.0% and 5.0 ± 10.1%) was higher than that of PGL (2.1 ± 6.9% and 1.7 ± 5.8%) and MIA (3.2 ± 9.1% and 3.1 ± 8.7%), with statistical significance (all P < 0.05). The quantitative analysis of intranodular vessels on noncontrast computed tomography images demonstrated that the ground-glass nodules with increased internal vessel prevalence and volume percentages had higher possibility of tumor invasiveness.

A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma.

The radiological features of computed tomography (CT) images and the sequence of radiomics signatures in continuous slices of lung CT lesions are helpful in identifying subtypes of lung adenocarcinoma. A model based on bidirectional long short-term memory (Bi-LSTM) and multihead attention can learn the rules of this sequence well. In this study, 421 patients with 427 lesions confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC) were recruited from three hospitals. The radiomics signatures of the identified lesion regions in each CT image were extracted using 'PyRadiomics' software, and the corresponding radiological features were subsequently documented and collected. Then, the top 100 features were extracted by the minimum redundancy maximum relevance (mRMR) feature ranking method. A model based on the radiological and imaging features was established to classify the lesions using Bi-LSTM and multihead attention. The diagnostic performance of the model was measured by the area under the curve (AUC) of the receiver operating characteristic (ROC). The model combined radiological features and radiomics signatures. The AUCs of the model in the training, testing, and validation groups were 0.985, 0.94 and 0.981, respectively, and the accuracy was 0.92, 0.976 and 0.91, respectively. In addition, we trained two other models [convolutional neural network (CNN) + multihead attention, long short-term memory (LSTM) + multihead attention] and compared them using the testing dataset. The precision of the two models was 0.89 and 0.88, respectively, and the accuracy was 0.88 and 0.87, respectively. Bi-LSTM and multihead attention based on radiomics signatures and radiological features provide a way to distinguish AIS, MIA, and IAC.

Immune microenvironment analysis and novel biomarkers of early-stage lung adenocarcinoma evolution.

Lung cancer is the deadliest and most diagnosed type of cancer worldwide. The 5-year survival rate of lung adenocarcinoma (LUAD) dropped significantly when tumor stages advanced. Patients who received surgically resecting at the pre-invasive stage had a 5-year survival rate of nearly 100%. However, the study on the differences in gene expression profiles and immune microenvironment among pre-invasive LUAD patients is still lacking. In this study, the gene expression profiles of three pre-invasive LUAD stages were compared using the RNA-sequencing data of 10 adenocarcinoma in situ (AIS) samples, 12 minimally invasive adenocarcinoma (MIA) samples, and 10 invasive adenocarcinoma (IAC) samples. The high expression levels of PTGFRN (Hazard Ratio [HR] = 1.45; 95% Confidence Interval [CI]: 1.08-1.94; log-rank P = 0.013) and SPP1 (HR = 1.44; 95% CI: 1.07-1.93; log-rank P = 0.015) were identified to be associated with LUAD prognosis. Moreover, the early LUAD invasion was accompanied by the enhancement of antigen presentation ability, reflected by the increase of myeloid dendritic cells infiltration rate (Cuzick test P < 0.01) and the upregulation of seven important genes participating in the antigen presentation, including HLA-A (Cuzick test P = 0.03), MICA (Cuzick test P = 0.01), MICB (Cuzick test P = 0.01), HLA-DPA1 (Cuzick test P = 0.04), HLA-DQA2 (Cuzick test P < 0.01), HLA-DQB1 (Cuzick test P = 0.03), and HLA-DQB2 (Cuzick test P < 0.01). However, the tumor-killing ability of the immune system was inhibited during this process, as there were no rising cytotoxic T cell activity (Cuzick test P = 0.20) and no increasing expression in genes encoding cytotoxic proteins. In all, our research elucidated the changes in the immune microenvironment during early-stage LUAD evolution and may provide a theoretical basis for developing novel early-stage lung cancer therapeutic targets.