Invasive Pulmonary Adenocarcinoma Research Articles

Morphologic Features of Invasion in Lung Adenocarcinoma: Diagnostic Pitfalls.

Reproducibility of pulmonary invasive adenocarcinoma diagnosis is poor when applying the World Health Organization (WHO) classification. In this article, we aimed first to explain by 3-dimensional morphology why simple pattern recognition induces pitfalls for the assessment of invasion as applied in the current WHO classification of pulmonary adenocarcinomas. The underlying iatrogenic-induced morphologic alterations in collapsed adenocarcinoma in situ overlap with criteria for invasive adenocarcinoma. Pitfalls in seemingly acinar and papillary carcinoma are addressed with additional cytokeratin 7 and elastin stains. In addition, we provide more stringent criteria for a better reproducible and likely generalizable classification.

109P A revised international association for the study of lung cancer grading system in invasive pulmonary adenocarcinoma: The inclusion of invasive mucinous adenocarcinomas

109P A revised international association for the study of lung cancer grading system in invasive pulmonary adenocarcinoma: The inclusion of invasive mucinous adenocarcinomas

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.

The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PurposeThe purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA. Materials and methodsSixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29–78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models. ResultsThe 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73–0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66–0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74–0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74–0.93) (P = 0.60). ConclusionBoth whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

Prognostic implications of CT-defined ground glass opacity in clinical stage I–IIA grade 3 invasive non-mucinous pulmonary adenocarcinoma

To investigate the prognostic impact of computed tomography (CT)-defined ground glass opacity (GGO) in patients with clinical stage I-IIA grade 3 invasive non-mucinous pulmonary adenocarcinoma (INPA). The present study retrospectively enrolled 187 patients diagnosed with stage I-IIA grade 3 INPA. Their clinicopathological, radiological, and genetic information was evaluated systematically, and a 5-year follow-up was conducted to monitor disease recurrence and mortality. Patients were stratified based on the presence of a GGO component, and the Cox proportional hazard model was employed to assess the influence of clinicopathological factors and genetic variables on tumour outcomes. Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared using the log-rank test. Significant differences were observed in both OS and RFS based on the presence of a GGO component. The group with GGO exhibited superior OS (p=0.002) and RFS (p=0.029). Multivariate analysis revealed that the presence of a GGO component (hazard ratio [HR]=0.412, 95% confidence interval [CI]: 0.177-0.959, p=0.040), clinical T2 stage (HR=2.473, 95% CI: 1.498-4.083, p<0.001), pathological N2 stage (HR=3.049, 95% CI: 1.800-5.167, p<0.001), and mixed high-grade patterns (HR=2.392, 95% CI: 1.418-4.036, p=0.001) were predictors of RFS. The presence of a GGO component is strongly associated with a favourable prognosis in grade 3 INPA.

Efficacy of thoracoscopic segmentectomy versus lobectomy in the treatment of early invasive lung adenocarcinoma: a propensity score matching study.

This study aimed to investigate and analyze the clinical application value of thoracoscopic segmentectomy and lobectomy in patients with invasive pulmonary adenocarcinoma. 286 patients with invasive pulmonary adenocarcinoma who underwent segmentectomy or lobectomy at the First Hospital of Jiaxing City from January 2018 to June 2020 were retrospectively analyzed. Patients were divided into a thoracoscopic segmentectomy group(n=97) and a lobectomy group (n=189). Patients were compared after obtaining 1:1 propensity score-matched cohorts. Outcome indicators included surgery-related indicators, immune-inflammation-related indicators, postoperative complications, recurrence, and metastasis. After 1:1 propensity score matching, 93 patients were included in each group. We found that the volume of intraoperative blood loss in the segmentectomy group was significantly less than in the lobectomy group (P=0.014). The duration of postoperative drainage (P = 0.005) and hospitalization (P=0.002) in the segmentectomy group were significantly shorter than in the lobectomy group. In terms of immunoinflammatory response, compared with the lobectomy group, white blood cells, neutrophils, SII, and NLR in the segmentectomy group were significantly lower than in the lobectomy group (P< 0.05). The recurrence-free survival (RFS) rates in the segmentectomy and lobectomy were 80.5% and 88.2% at 1 year and 35.1% and 52.6% at 3 years, respectively, and the difference was statistically significant (P<0.05). The segmentectomy group achieved similar outcomes to the lobectomy group at 1 year and 3 years (P > 0.05). Multivariate COX regression analysis showed that CAR was an independent risk factor for RFS in patients undergoing invasive adenocarcinoma surgery. Compared with lobectomy, thoracoscopic segmentectomy can effectively reduce the postoperative inflammatory response in patients with early invasive lung adenocarcinoma and promote patient recovery. Although segmentectomy is associated with a higher recurrence rate in the short term for patients with early invasive lung adenocarcinoma, the associated survival rate is similar to the lobectomy group. Segmentectomy should be considered in the treatment of early invasive lung adenocarcinoma. Meanwhile, postoperative CAR represents an independent risk factor for early postoperative recurrence in patients with IAC.

Collapsin response mediator protein 5-associated optic neuropathy: clinical characteristics, radiologic clues, and outcomes.

Collapsin response mediator protein 5-associated optic neuropathy (CRMP5-ON) is a rare entity of autoimmune optic neuropathy. This study aimed to review the neuro-ophthalmic findings and outcomes in a series of patients with CRMP5-ON to further characterize its clinical phenotype, radiologic clues, and outcomes. This was a retrospective case series and a single-center medical chart review of all patients with CRPM5-seropositive ON at the Department of Neurology, Beijing Tongren Hospital, from December 1, 2020, to March 31, 2023. The main outcome measures were neuro-ophthalmic manifestations, radiologic characteristics, and clinical outcomes of CRMP5-ON; coexisting neural autoantibody, paraneoplastic associations, and the impact of immunosuppressant therapy. Five patients were identified. Four (80%) were female, and the average age at onset was 59.4 years (range 53-69 years), with an average follow-up of 15.3 months (range 1.4-28.7 months). The average best-corrected visual acuity (BCVA) at nadir was 20/120 (range 20/20 to count fingers). Seven of ten affected eyes (70%) showed diffuse defects of the central field. Painless bilateral involvement and optic disk edema occurred in 100% of patients, combined with vitritis, uveitis, or retinitis in four (80%). Four patients (80%) had MRI abnormalities along the optic nerve (one patient with optic nerve enhancement and three patients had optic nerve sheath enhancement or peribulbar fat enhancement). Three patients (60%) had optic neuropathy with other neurologic symptoms. Four patients (80%) had confirmed cancer (two were small-cell lung carcinoma, one was papillary thyroid carcinoma and another was thymoma and invasive pulmonary adenocarcinoma). All cancers were identified after the presentation of the optic neuropathy. The intervention included IVIG, IVMP, surgery and chemotherapy. The average BCVA at the last follow-up was 20/50 (range 20/20 to count fingers). Three patients had surgery during the initial hospitalization, and were stable during the follow-up. Among two patients who received IVMP, both had improvement after treatment, although one patient had worsening non-ocular neurologic symptoms during the steroid taper. CRMP5-ON presented with optic disc edema, often bilateral involved and combined with vitreitis, retinitis, or uveitis. CRMP5-ON can present with MRI optic nerve or perineural optic nerve enhancement, especially in the optic nerve sheath. CRMP5-ON is closely related to paraneoplastic neurologic syndrome. Cancer screening and intervention are crucial to prognosis.

Deep neural networks to predict pathological invasiveness through surgical resection images: A computer vision based prospective clinical trial in pulmonary nodules.

3068 Background: Pathological subtypes associate with surgical strategies of pulmonary nodule, during which thoracic surgeons make surgical decision through intraoperative frozen resection diagnosis. However, frozen section is time-consuming, vague and maybe misleading due to the limited sample and subjective judgements from pathologists. This study developed a predictive deep neural network for pathological invasiveness of pulmonary nodules based on the surgical resection images of gross specimens. Methods: We prospectively collected the specimen image under standardized lighting conditions in operating theaters from patients with pulmonary nodules treated by surgery resection from June 2020 to September 2021 in Guangdong Provincial People’s Hospital. Images were assigned into training cohort, validating cohort and test cohort as 8:1:1. With data augmentation, DenseNet was applied to classify the pulmonary nodules into high-risk and low-risk group. High-risk group was defined as grade 2 and grade 3 invasive pulmonary adenocarcinoma according to IASLC, while low-risk group was defined as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and grade 1 invasive pulmonary adenocarcinoma. Predictive efficiency was evaluated through area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, accuracy. Model performance would be compared with frozen section pathological diagnosis. Results: Among 1022 patients with pulmonary nodules enrolled, we have acquired 1080 images of the resected nodules in Guangdong Provincial People’s Hospital, with a mean age of 56 years and 39.1% of male patients. The median age of patients were 57 years in training cohort and 56 years in validating cohort, with 38.1% male patients in training cohort and 42.5% male patients in test cohort. This study included 864 images in training cohort, 108 images in validating cohort and 108 images in test cohort. Mean diameter of the nodules were 14.6mm in training cohort and 17.2mm in test cohort. In terms of predictive performance, the AUC value of ROC curves was 96.0% in training cohort and 95.0% in test cohort. In training cohort, the sensitivity of model was 97.6%, with the accuracy of 97.5% and specificity of 97.6%. In test cohort, the sensitivity of model was 97.6%, with the specificity of 90.2% and accuracy of 95.4%. Compared with frozen resection in testing cohort, our deep neural network had higher accuracy in pathological prediction (95.4% vs 88.9%). Conclusions: This study developed and validated a predictive algorithm on pathological invasiveness of pulmonary nodules through computer vision technique with highly sensitivity and specificity, which could assist the rapid evaluation for pathological invasiveness and intraoperative surgical decisions.

Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study

The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002–1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304–1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value − 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481–0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748–413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004–1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and − 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.

A Simplified Version of the IASLC Grading System for Invasive Pulmonary Adenocarcinomas With Improved Prognosis Discrimination.

Tumor grading enables better management of patients and treatment options. The International Association for the Study of Lung Cancer (IASLC) Pathology Committee has recently released a 3-tier grading system for invasive pulmonary adenocarcinoma consisting of predominant histologic patterns plus a cutoff of 20% of high-grade components including solid, micropapillary, and complex glandular patterns. The goal of this study was to validate the prognostic value of the new IASLC grading system and to compare its discriminatory performance to the predominant pattern-based grading system and a simplified version of the IASLC grading system without complex glandular patterns. This was a single-site retrospective study based on a 20-year data collection of patients that underwent lung cancer surgery. All invasive pulmonary adenocarcinomas confirmed by the histologic review were evaluated in a discovery cohort (n=676) and a validation cohort (n=717). The median duration of follow-up in the combined dataset (n=1393) was 7.5 years. The primary outcome was overall survival after surgery. The 3 grading systems had strong and relatively similar predictive performance, but the best parsimonious model was the simplified IASLC grading system (log-rank P =1.39E-13). The latter was strongly associated with survival in the validation set ( P =1.1E-18) and the combined set ( P =5.01E-35). We observed a large proportion of patients upgraded to the poor prognosis group using the IASLC grading system, which was attenuated when using the simplified IASLC grading system. In conclusion, we identified a histologic simpler classification for invasive pulmonary adenocarcinomas that outperformed the recently proposed IASLC grading system. A simplified grading system is clinically convenient and will facilitate widespread implementation.

An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening.

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. 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. 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). 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. • 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.

Application of the WHO Classification of Thoracic Tumors (2021) grading system in invasive pulmonary adenocarcinoma and its correlation with the targeted genes' variations

Objective: To investigate the applicability of the 2021 WHO classification of thoracic tumors' new grading system for invasive pulmonary adenocarcinoma (IPA) with different clinical stages and its correlation with the characteristics of targeted genes' variation. Methods: A total of 2 467 patients with surgically resected primary IPA in Shanghai Pulmonary Hospital, Shanghai, China from September to December 2020 were retrospectively analyzed. Eligible cases were graded using the new grading system of IPA of the 2021 WHO classification of thoracic tumors. The clinicopathological data and targeted-gene abnormality were collected. The utility of new grading system of IPA in different clinical stages was investigated. The correlation of clinicopathological features and targeted-gene abnormality in different grades of IPA were compared. Results: All 2 311 cases of IPA were included. There were 2 046 cases of stage Ⅰ IPA (88.5%), 169 cases of stage Ⅱ (7.3%), and 96 cases of stage Ⅲ (4.2%). According to the new classification system of IPA, 186 cases (9.1%), 1 413 cases (69.1%) and 447 cases (21.8%) of stage-Ⅰ adenocarcinoma were classified as Grade 1, Grade 2 and Grade 3, respectively. However, there were no Grade 1 adenocarcinomas in stages Ⅱ and Ⅲ cases. Among stage-Ⅱ and Ⅲ IPA cases, there were 38 Grade 2 cases (22.5%) and 131 Grade 3 cases (77.5%), and 3 Grade 2 cases (3.1%) and 93 Grade 3 cases (96.9%), respectively. In stage-Ⅰ cases, no tumor cells spreading through airspace (STAS), vascular invasion or pleural invasion was found in Grade 1 of IPA, while the positive rates of STAS in Grade 2 and 3 IPA cases were 11.3% (159/1 413) and 73.2% (327/447), respectively. There was a significant difference among the three grades (P<0.01). Similarly, the rates of vascular and pleural invasion in Grade 3 IPA cases were 21.3% (95/447) and 75.8% (339/447), respectively, which were significantly higher than those of 1.3% (19/1 413) and 3.0% (42/1 413) in Grade 2 (P<0.01). EGFR mutational rates in Grades 1, 2 and 3 IPA were 65.7% (94/143), 76.4% (984/1 288) and 51.3% (216/421), respectively. The differences among the three grades were statistically significant (P<0.01). No fusion genes were detected in Grade 1 IPA, while the positive rates of ROS1 and ALK fusion genes in Grade 3 were 2.4% (10/421) and 8.3% (35/421), respectively, which were significantly higher than that of 0.5% (7/1 288) and 1.6% (20/1 288) in Grade 2 (P<0.01). In stage-Ⅱ cases, only EGFR mutation rate in Grade 2 adenocarcinoma (31/37, 83.8%) was higher than that in Grade 3 adenocarcinoma (71/123, 57.7%; P<0.01). However, the correlation between the new grade system of IPA and the distribution characteristics of targeted-gene variation cannot be evaluated in stage Ⅲ cases. Conclusions: The new grading system for IPA is mainly applicable to clinical stage-Ⅰ patients. Tumor grades of IPA are strongly correlated with the high-risk factors of prognosis and the distribution features of therapeutic targets. It is of great significance and clinical value to manage postoperative patients with early-stage IPA.

An integrated nomogram combined semantic-radiomic features to predict invasive pulmonary adenocarcinomas in subjects with persistent subsolid nodules.

Patients with persistent pulmonary subsolid nodules have a relatively high incidence of lung adenocarcinoma. Preoperative early diagnosis of invasive pulmonary adenocarcinoma spectrum lesions could help avoid extensive advanced cancer management and overdiagnosis in lung cancer screening programs. In total, 260 consecutive patients with persistent subsolid nodules ≤30 mm (n=260) confirmed by surgical pathology were retrospectively investigated from February 2016 to August 2020 at the Kaohsiung Veterans General Hospital. All patients underwent surgical resection within 3 months of the chest CT exam. The study subjects were divided into a training cohort (N=195) and a validation cohort (N=65) at a ratio of 3:1. The purpose of our study was to develop and validate a least absolute shrinkage and selection operator-derived nomogram integrating semantic-radiomic features in differentiating preinvasive and invasive pulmonary adenocarcinoma lesions, and compare its predictive value with clinical-semantic, semantic, and radiologist's performance. In the training cohort of 195 subsolid nodules, 106 invasive lesions and 89 preinvasive lesions were identified. We developed a least absolute shrinkage and selection operator-derived combined nomogram prediction model based on six predictors (nodular size, CTR, roundness, GLCM_Entropy_log10, HISTO_Entropy_log10, and CONVENTIONAL_Humean) to predict the invasive pulmonary adenocarcinoma lesions. Compared with other predictive models, the least absolute shrinkage and selection operator-derived model showed better diagnostic performance with an area under the curve of 0.957 (95% CI: 0.918 to 0.981) for detecting invasive pulmonary adenocarcinoma lesions with balanced sensitivity (92.45%) and specificity (88.64%). The results of Hosmer-Lemeshow test showed P values of 0.394 and 0.787 in the training and validation cohorts, respectively, indicating good calibration power. We developed a least absolute shrinkage and selection operator-derived model integrating semantic-radiomic features with good calibration. This nomogram may help physicians to identify invasive pulmonary adenocarcinoma lesions for guidance in personalized medicine and make more informed decisions on managing subsolid nodules.

Application of the Novel Grading System of Invasive Pulmonary Adenocarcinoma in a Real Diagnostic Scenario: A Brief Report of 9353 Cases

IntroductionThe International Association for the Study of Lung Cancer proposed a novel grading system of invasive pulmonary adenocarcinoma (IPA), but the application of this grading system and its genotypic characterization in the real diagnostic scenario has never been reported. MethodsWe prospectively collected and analyzed the clinicopathological and genotypic features of a cohort of 9353 consecutive patients with resected IPA, including 7134 patients with detection of common driver mutation. ResultsIn the entire cohort, 3 (0.3%) of lepidic, 1207 (19.0%) of acinar, and 126 (23.6%) of papillary predominant IPAs were diagnosed as grade 3. In chronological order, an evident downtrend of the proportion of grade 2 was observed in chronological order. Conversely, the diagnostic ratio of grade 1 (8.0%–14.5%) and grade 3 (27.9%–32.3%) experienced a gradual rise. EGFR mutation was more frequently detected in grade 2 (77.5%) and grade 1 (69.7%) IPA than grade 3 (53.7%, p < 0.001), whereas the mutation rates of KRAS, BRAF, ALK, and ROS1 were higher in grade 3 IPA. More importantly, the rate of EGFR mutation gradually fell as the proportion of high-grade components increased, to 24.3% in IPA with more than 90% high-grade components. ConclusionsThe grading system for IPA could be applied to stratify patients with different clinicopathological and genotypic features in a real diagnostic scenario.

Combined analysis of gut microbiome and serum metabolomics reveals novel biomarkers in patients with early-stage non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the predominant form of lung cancer and is one of the most fatal cancers worldwide. Recently, the International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system based on the predominant and high-grade histological patterns for invasive pulmonary adenocarcinoma (IPA). To improve outcomes for NSCLC patients, we combined serum metabolomics and fecal microbiology to screen biomarkers in patients with early-stage NSCLC and identified characteristic microbial profiles in patients with different grades of IPA. 26 genera and 123 metabolites were significantly altered in the early-stage NSCLC patients. Agathobacter, Blautia, Clostridium, and Muribaculacea were more abundant in the early-stage NSCLC patients compared with healthy controls. For the different grades of IPA, the characteristic microorganisms are as follows: Blautia and Marinobacter in IPA grade type 1; Dorea in IPA grade type 2; and Agathobacter in IPA grade type 3. In the metabolome results, the early-stage NSCLC group mainly included higher levels of sphingolipids (D-erythro-sphingosine 1-phosphate, palmitoyl sphingomyelin), fatty acyl (Avocadyne 1-acetate, 12(S)-HETE, 20-Carboxy-Leukotriene B4, Thromboxane B3, 6-Keto-prostaglandin f1alpha, Sebacic acid, Tetradecanedioic acid) and glycerophospholipids (LPC 20:2, LPC 18:0, LPC 18:4, LPE 20:2, LPC 20:1, LPC 16:1, LPC 20:0, LPA 18:2, LPC 17:1, LPC 17:2, LPC 19:0). Dysregulation of pathways, such as sphingolipid metabolism and sphingolipid signaling pathway may become an emerging therapeutic strategy for early-NSCLC. Correlation analysis showed that gut microbiota and serum metabolic profiles were closely related, while Muribaculacea and Clostridium were the core genera. These findings provide new biomarkers for the diagnosis of early-stage NSCLC and the precise grading assessment of prognostic-related IPAs, which are of clinical importance and warrant further investigation of the underlying molecular mechanisms.

Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma.

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. A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2weeks 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. 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). 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.

The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy.

Grading system for resected invasive pulmonary adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) was validated as a strong prognostic indicator. Nonetheless, the efficacy of utilizing such grading system in prognostic assessment of patients receiving neoadjuvant therapy still needs elucidating. A retrospective study was conducted including patients with resected adenocarcinoma following neoadjuvant chemotherapy or targeted therapy from August 2012 to December 2020 in Shanghai Pulmonary Hospital. All the surgical specimens were re-evaluated and graded. The prognostic value of the grading system was further validated. Ultimately, a total of 198 patients were enrolled in this study, and subdivided into three cohorts according to the grading system. There were 13 (6.6%), 37 (18.7%), and 148 (74.7%) patients belonging to Grades 1, 2, and 3, respectively. IASLC grading system demonstrated significant power in prognosis differentiation of the entire cohort [recurrence-free survival (RFS), p < 0.001; overall survival (OS), p < 0.001] and the neoadjuvant chemotherapy and targeted therapy cohorts separately, and was further verified as a significant prognostic indicator for RFS and OS in multivariable Cox analysis. Since the majority of the patients (84.8%) did not achieve major pathologic response (MPR), representing a wide spectrum of survival, the prognostic value of grading system in non-MPR cohort was further evaluated. Similar results were also obtained that IASLC grading system was assessed significant in univariable analysis of RFS (p < 0.001) and univariable analysis of OS (p = 0.001). The prognostic efficacy of pathological evaluation of the residual proportion of pulmonary adenocarcinoma post-neoadjuvant therapy using IASLC grading system was preliminarily verified. Such grading system might assist prognostic evaluation of neoadjuvant cohort other than traditional pathological parameters.

Nadir and Finical-Minimally Invasive Adenocarcinoma Lung

Minimally invasive adenocarcinoma emerges as a miniature, solitary, localized, primary pulmonary adenocarcinoma ≤ 3.0 centimetre magnitude and ≤ 0.5 centimetre zone of stromal invasion or non-invasive (lepidic) pattern of neoplastic evolution. Minimally invasive adenocarcinoma lung is predominantly comprised of non-mucinous component and is confined within peripheral pulmonary parenchyma. Preponderantly asymptomatic, poorly defined areas appear concurrent with lepidic pattern of tumour growth whereas solid foci are concordant with focal tumour invasion. Cogent histopathological subtypes appear as non-mucinous variant, mucinous variant and mixed variant. Stromal invasion configures acinar, papillary, micropapillary, solid, colloid, foetal or invasive mucinous lesions. Neoplastic cells appear immune reactive to CK7, thyroid transcription factor1(TTF-1) or Napsin A and immune non-reactive to p40. Minimally invasive adenocarcinoma lung requires segregation from neoplasms such as invasive pulmonary adenocarcinoma, lepidic predominant pattern, adenocarcinoma in situ or atypical adenomatous hyperplasia lung. The gradually progressive neoplasm demonstrates ‘ground glass’ opacities. Appropriate neoplastic discernment necessitates comprehensive histological assessment of entire surgical resection specimens. Minimally invasive adenocarcinoma lung can be appropriately treated with surgical extermination of the neoplasm followed by extensive monitoring. Neoplasm is devoid of lymphatic invasion, vascular invasion, tumour necrosis or pleural invasion.

Heteroclite and Turgid-Atypical Adenomatous Hyperplasia Lung

Atypical adenomatous hyperplasia emerges as a miniature, localized proliferation of atypical pneumocytes which layer intact alveolar spaces. Atypical adenomatous hyperplasia is contemplated as a precursor lesion to invasive pulmonary adenocarcinoma.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma

The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.