Solid Component Size Research Articles

Resected lung adenocarcinoma with lymph node metastasis: is ground glass opacity component a prognostic factor?

Ground glass opacity (GGO)-featured lung adenocarcinoma generally has excellent prognosis, and here is rarely the occurrence of lymph node metastasis. We conducted a retrospective cohort study to explore the prognostic impact of GGO component in node-positive lung adenocarcinomas. A total of 669 patients with pathologic N1/N2 lung adenocarcinoma receiving R0 resection and systemic lymph node dissection from 2008 to 2015 were reviewed, including 635 solid and 34 part-solid lesions. Propensity score matching (PSM) was performed to compare survival outcomes of solid and part-solid lesions, in order to determine the prognostic value of GGO component. Cox proportional hazard model was performed to identify significant prognostic factors for resected node positive lung adenocarcinoma. About 5.1% (34 of 669) of resected node-positive lung adenocarcinoma presented as part-solid nodules on computed tomography (CT) images in this cohort. The median nodule size on CT of the 34 part-solid lesions was 31 mm (range, 15-68 mm), median solid component size on CT was 24 mm (range, 12-62 mm), and median consolidation/tumor ratio was 0.8 (range, 0.64-0.95). After 1:4 PSM, 136 patients and 34 patients were matched from the solid and part-solid groups. No significant difference in either recurrence-free survival (RFS) (P=0.71) or overall survival (OS) (P=0.82) was found between the solid and part-solid groups. Multivariable Cox regression showed that pN stage was the strongest prognostic factor for RFS and OS. GGO component was not an independent prognostic factor toward for RFS [P=0.75; hazard ratio (HR) =0.93; 95% confidence interval (CI): 0.59-1.46] or OS (P=0.53; HR =1.19; 95% CI: 0.69-2.05). A minority of resected node-positive lung adenocarcinoma presents as GGO component on CT. The presence of GGO component does not predict better prognosis in node-positive lung adenocarcinoma.

Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma

BackgroundAccurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma.MethodsA total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model.ResultsThe GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets.ConclusionsThe nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

What Is an Adequate Margin During Sublobar Resection of ≤3 cm N0 Subsolid Lung Adenocarcinomas?

BackgroundSublobar resection offers noninferior survival vs lobectomy for ≤2 cm non-small cell lung cancer and is commonly used for subsolid tumors. Although data exist for solid tumors, the minimum adequate margin of resection for subsolid adenocarcinomas remains unclear. MethodsThis was a retrospective review of 1101 adenocarcinoma resections at our institution from 2006 to 2022. Inclusion criteria were tumors ≤3 cm with ≥10% radiographic ground glass, excised by sublobar resection. Exclusions were positive nodes or positive or unreported margin. The primary outcome was the rate of local recurrence (LR) at multiple thresholds of margin distance. The relationship between margin distance and solid component size was also explored. ResultsInclusion criteria were met by 194 patients. Median (interquartile range) tumor diameter and margin distance were 12 mm (9-17 mm) and 10 mm (5-17 mm), respectively. Median follow-up was 42.5 months. There was a progressive increase in LR with diminishing margin (0.1-cm decrements) from 1.5 cm to 0.5 cm. The difference in the rate of LR between “over” (n = 143) and “under” (n = 51) was most significant at 0.5 cm (8 of 51 [15.7%] vs 6 of 143 [4.2%]; P = .01) but did not reach α adjusted for multiple comparisons. On Cox regression for LR-free survival, margin ≤0.5 cm (P = .19) and solid component percentage (P = .14) trended to significance. Combining these using a ratio of margin distance–to–solid component size, a ratio of ≤1 showed a significantly higher rate of LR (7 [14.3%] vs 2 [2.0%], P = .009). Treatment of LRs provided at least intermediate-term survival in 87% of recurrences (median postrecurrence follow-up was 44 months). ConclusionsDuring sublobar resection of subsolid lung adenocarcinomas, a margin distance–to–solid component size ratio of >1.0 appears to be a more reliable factor than margin distance alone to minimize local recurrence. Local recurrence, however, may not impact survival in patients with subsolid adenocarcinomas if timely treatment is administered.

Shape-Sensing Robotic-Assisted Bronchoscopy versus Computed Tomography-Guided Transthoracic Biopsy for the Evaluation of Subsolid Pulmonary Nodules

Introduction: Lung cancer remains the leading cause of cancer death worldwide. Subsolid nodules (SSN), including ground-glass nodules (GGNs) and part-solid nodules (PSNs), are slow-growing but have a higher risk for malignancy. Therefore, timely diagnosis is imperative. Shape-sensing robotic-assisted bronchoscopy (ssRAB) has emerged as reliable diagnostic procedure, but data on SSN and how ssRAB compares to other diagnostic interventions such as CT-guided transthoracic biopsy (CTTB) are scarce. In this study, we compared diagnostic yield of ssRAB versus CTTB for evaluating SSN. Methods: A retrospective study of consecutive patients who underwent either ssRAB or CTTB for evaluating GGN and PSN with a solid component less than 6 mm from February 2020 to April 2023 at Mayo Clinic Florida and Rochester. Clinicodemographic information, nodule characteristics, diagnostic yield, and complications were compared between ssRAB and CTTB. Results: A total of 66 nodules from 65 patients were evaluated: 37 PSN and 29 GGN. Median size of PSN solid component was 5 mm (IQR: 4.5, 6). Patients were divided into two groups: 27 in the ssRAB group and 38 in the CTTB group. Diagnostic yield was 85.7% for ssRAB and 89.5% for CTTB (p = 0.646). Sensitivity for malignancy was similar between ssRAB and CTTB (86.4% vs. 88.5%; p = 0.828), with no statistical difference. Complications were more frequent in CTTB with no significant difference (8 vs. 2; p = 0.135). Conclusion: Diagnostic yield for SSN was similarly high for ssRAB and CTTB, with ssRAB presenting less complications and allowing mediastinal staging within the same procedure.

Multiparametric evaluation of mediastinal lymph node metastases in clinical T0-T1c stage non-small-cell lung cancers.

This study aimed to determine the predictive factors of lymph node metastases in clinical T0-T1c stage non-small-cell lung cancers, so as to help making surgical strategy. From January 2016 to December 2017, patients with clinical T0-T1c stage non-small-cell lung cancers were retrospectively reviewed. We elucidated the lymph node metastatic incidence and distribution according to the primary tumour radiographic findings and maximal standard uptake values, and extracted the associated clinicopathological factors. Univariable and multivariable logistic regressions were used to identify independent predictive parameters for lymph node metastases. The performance of predictive model was evaluated using receiver operating characteristic analysis. A total of 517 patients were included. Seventy-two patients had lymph node metastases. Among patients with pure ground-glass nodule and solid component size ≤10 mm, none had any lymph node metastasis. Multivariable logistic regression analysis demonstrated that age, carcinoembryonic antigen level, solid component size, consolidation-tumour ratio and tumour maximal standard uptake values were independent predictors of lymph nodal metastases. Receiver operating characteristic analyses indicated that the area under the curve of predictive model in evaluating lymph node metastases was 0.838 (95% CI 0.791-0.886). Younger age, elevated carcinoembryonic antigen level, larger solid component size, higher consolidation-tumour ratio and tumour maximal standard uptake values were associated with lymph node involvement. Employing such a predictive model in the future may affect the surgical option of lymph node excision for patients in cT1 stage non-small-cell lung cancer.

Clinicopathological and computed tomography features associated with recurrence-free survival of patients with small-sized peripheral invasive lung adenocarcinoma after sublobectomy.

Sublobar resection is gradually becoming a standard treatment for small-sized (≤2 cm) peripheral non-small cell lung cancer (NSCLC), with lung adenocarcinoma (LADC) being the most frequent histologic subtype. However, the prognostic predictors for preoperatively determining whether sublobectomy is feasible for patients with early LADC have not yet been well identified. Therefore, this study aimed to investigate the clinicopathological and computed tomography (CT) features associated with the recurrence-free survival (RFS) of patients with small-sized invasive LADC (SILADC) after sublobar resection. This retrospective cohort study analyzed 107 patients with SILADC who underwent preoperative chest CT scan and sublobar resection from December 2012 to March 2019. The Kaplan-Meier survival was used to analyze the relationship between clinicopathological characteristics, preoperative chest CT findings, and RFS. The Cox proportional hazards regression was used to identify independent prognostic factors of poor RFS. For clinicopathological characteristics, RFS was shorter in patients aged ≥70 years, smokers, and those with micropapillary/solid-predominant adenocarcinomas (all P values <0.05). For preoperative CT features, RFS was shorter in patients with tumor size ≥1.4 cm, solid component size ≥1.1 cm, proportion of solid component ≥72%, solid density, spiculation, vascular convergence sign, peripheral fibrosis, and type II pleural tag (all P values <0.05). Multivariate analysis showed proportion of solid component ≥72% [hazard ratio (HR): 5.920; P=0.006; 95% confidence interval (CI): 1.686-20.794], spiculation (HR: 5.026; P=0.001; 95% CI: 2.008-12.581), and type II pleural tag (HR: 4.638; P=0.002; 95% CI: 1.773-12.136) were independent risk factors for poor prognosis in patients with SILADC after sub-lobectomy. Clinicopathological and CT characteristics are helpful for predicting the RFS of patients with SILADC after sublobar resection and can be used as an auxiliary tool for thoracic surgeons to choose the best surgical mode.

Role of magnetic resonance imaging to differentiate between borderline and malignant serous epithelial ovarian tumors.

We aimed to differentiate serous borderline ovarian tumors (SBOT) from serous epithelial ovarian carcinomas (SEOC) using morphological and functional MRI findings, to improve the patient management. We retrospectively investigated 24 ovarian lesions diagnosed with SBOT and 64 ovarian lesions diagnosed with SEOC. Additional to the demographic and morphological findings T2W signal intensity ratio, mean apparent diffusion coefficient (ADCmean) and total apparent diffusion coefficient (ADCtotal) values were analyzed and compared between two groups. Bilaterality, pelvic free fluid presence, serum CA-125 level (U/mL), presence of pelvic peritoneal implant were in favor of SEOC. Lower maximum size of solid component and solid size to maximum size ratio, dominantly cystic and solid-cystic appearance, exophytic growth pattern, presence of papiller projection and papillary architecture and internal branching pattern, higher T2W signal intensity ratio, ADCmean and ADCtotal values were in favor of SBOT. Our study revealed that morphological and functional imaging findings were valuable in differentiating BSOT from SEOC.

Predictors of residual simultaneous multiple ground-glass nodule progression after dominant lesion resection.

This study aimed to identify the predictive factors for the progression of residual simultaneous multifocal ground-glass nodules (SMGGNs) after resection of the dominant lesion. Patients (n = 3420) with primary lung cancer who underwent lung resections at our hospital between 2006 and 2016 were screened, and the data from 82 patients who had residual SMGGNs after undergoing surgery for the dominant lesion (pathologically stage 0-IIA) were retrospectively analyzed. Clinicopathological factors that predicted the growth of residual second dominant GGNs were identified. Median total tumor and solid component sizes of the residual second dominant GGNs were 1.3cm (interquartile range [IQR]: 0.6-2.0) and 0cm (IQR: 0-0.7), respectively. During a median follow-up period of 54months (IQR: 37-78months), 35 (43%) lesions progressed. Logistic regression analysis revealed that age younger than 70 (OR: 10.54, 95% CI: 1.71-65.11), a dominant lesion with pure solid appearance (reference: GGN, OR: 18.16, 95% CI: 1.66-198.60), a second dominant GGN total size larger than 1.0cm (OR: 12.27, 95% CI: 1.85-81.17), and a second dominant GGN solid component size larger than 0.5cm (OR: 17.59, 95% CI: 3.58-86.47) were significant predictive factors for the progression of residual GGNs (all p values < 0.03). Based on an analysis of growth patterns, rapid growth was higher in second dominant GGNs with a part-solid appearance. If the resected dominant lesion or the residual second dominant GGN exhibits high-risk factors, the second dominant GGN should be meticulously observed.

Differentiation Between Heterogeneous GGN and Part-Solid Nodule Using 2 D Grayscale Histogram Analysis of Thin-Section CT Image

Introduction/BackgroundTo evaluate cases of surgically resected pulmonary adenocarcinoma (Ad) with heterogenous ground-glass nodules (HGGNs) or part-solid nodules (PSNs) and to clarify the differences between them, and between invasive adenocarcinoma (IVA) and minimally invasive adenocarcinoma (MIA) + adenocarcinoma in situ (AIS) using grayscale histogram analysis of thin-section computed tomography (TSCT). Materials and Methods241 patients with pulmonary Ad were retrospectively classified into HGGNs and PSNs on TSCT by three thoracic radiologists. Sixty HGGNs were classified into 17 IVAs, 26 MIAs, and 17 AISs. 181 PSNs were classified into 114 IVAs, 55 MIAs, and 12 AISs. ResultsWe found significant differences in area (P = 0.0024), relative size of solid component (P <0.0001), circumference (P <0.0001), mean CT value (P <0.0001), standard deviation of the CT value (P <0.0001), maximum CT value (P <0.0001), skewness (P <0.0001), kurtosis (P <0.0001), and entropy (P <0.0001) between HGGNs and PSNs. In HGGNs, we found significant differences in relative size of solid component (P <0.0001), mean CT value (P = 0.0005), standard deviation of CT value (P = 0.0071), maximum CT value (P = 0.0237), and skewness (P = 0.0027) between IVAs and MIA+AIS lesions. In PSNs, we found significant differences in area (P = 0.0029), relative size of solid component (P = 0.0003), circumference (P = 0.0004), mean CT value (P = 0.0011), skewness (P = 0.0009), and entropy (P = 0.0002) between IVAs and the MIA+AIS lesions. ConclusionQuantitative evaluations using grayscale histogram analysis can clearly distinguish between HGGNs and PSNs, and may be useful for estimating the pathology of such lesions.

Tumor-associated prognostic factors extractable from chest CT scans in patients with lung cancer.

Accurately predicting the prognosis of patients with lung cancer before or at the time of treatment would offer clinicians an opportunity to tailor management plans more precisely to individual patients. Considering that chest computed tomography (CT) scans are universally acquired in patients with lung cancer for clinical staging or response evaluation, fully extracting and utilizing the prognostic information embedded in this modality would be a reasonable approach. Herein, we review tumor-related prognostic factors that are extractable from CT scans, including the tumor dimensions, presence of ground-glass opacity (GGO), margin characteristics, tumor location, and deep learning-based features. Tumor dimensions include diameter and volume, which are among the most potent prognostic factors in lung cancer. In lung adenocarcinomas, the solid component size on CT scans as well as the total tumor size is associated with the prognosis. The areas of GGO indicate the lepidic component and are associated with better postoperative survival in early-stage lung adenocarcinomas. As for the margin characteristics, which represent the CT manifestation of fibrotic stroma or desmoplasia, tumor spiculation should be evaluated. The tumor location in the central lung is associated with occult nodal metastasis and is a worse prognostic factor per se. Last but not least, deep learning analysis enables prognostic feature extraction beyond the human eyes.

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.

“UTILITY OF IOTA ADNEX MODEL IN PRE-OPERATIVE EVALUATION OF ADNEXAL MASSES WITH HPE CO-RELATION IN FEMALES BETWEEN 30-70 YEARS IN TERTIARY CARE CENTRE OF CENTRAL INDIA”

1. Objectives: Ÿ To prove that there is a role of IOTA ADNEX model in preoperative evaluation of adnexal masses. 2. Conclusion And Results: Ÿ In our study, out of 65 patients, most of the patients were ≤50 years of age. Age was statistically signicant with HPE (p0.032). Ÿ We found that, most of the patients had family history in Malignant group compared to Benign group but this was not statistically signicant (p-0.951). Ÿ Our study showed that, a greater number of patients had more than 10 locules in Malignant group compared to Benign group though it was not statistically signicant (p-0.87). Ÿ Higher number of patients had Ascites in Malignant group compared to Benign group which was statistically signicant (p&lt;0.001). Ÿ More number of patients had Post Acoustic Shadowing in Malignant group compared to Benign group which was statistically signicant (p&lt;0.001). Ÿ It was found that, the mean CA 125 U/mL was higher in Malignant group compared to Benign group it was statistically signicant (p&lt;0.001). Ÿ The mean Lesion Diameter (mm) was more in Malignant group compared to Benign group but this was not statistically signicant (p-0.701). Ÿ We showed that, the mean Solid Component Size (mm) was more in Malignant group compared to Benign group which was statistically signicant (p&lt;0.001). Ÿ The mean number of Papillary Projections was more in Malignant group compared to Benign group which was statistically signicant (p-0.005). Ÿ We observed that, majority of lesions which were categorized as malignant by IOTA ADNEX model turned out to be malignant and benign lesions turned out to be benign on HPE, it was statistically signicant (p-0.0001). 3. Inference: IOTA ADNEX MODEL proved to be a useful tool in early detection of adnexal lesions and differentiating them into beningn and malignant groups. The discriminating performance of ovarian tumors with the IOTA-ADNEX model has been better than other existing models. In 2014, ADNEX model was developed. The ADNEX model helps in differentiation of benign from malignant by using 9 predictors, 3 of them are clinical (age, CA 125 and type of centre) and rest 6 are sonographic variables (maximal diameter of lesion, proportion of solid tissue, more than 10 cyst locules, no. of papillary projections, acoustic shadow and ascites. This study aimed to test reliability of these risks prediction models to improve the performance of pelvic ultrasound and discriminate between benign and malignant lesions. Association of results of IOTA ADNEX model with HPE was statistically signicant (p&lt;0.0001)

Performance of IOTA Simple Rules Risks, ADNEX Model, Subjective Assessment Compared to CA125 and HE4 with ROMA Algorithm in Discriminating between Benign, Borderline and Stage I Malignant Adnexal Lesions.

Borderline ovarian tumors (BOTs) and early clinical stage malignant adnexal masses can make sonographic diagnosis challenging, while the clinical utility of tumor markers, e.g., CA125 and HE4, or the ROMA algorithm, remains controversial in such cases. To compare the IOTA group Simple Rules Risk (SRR), the ADNEX model and the subjective assessment (SA) with serum CA125, HE4 and the ROMA algorithm in the preoperative discrimination between benign tumors, BOTs and stage I malignant ovarian lesions (MOLs). A multicenter retrospective study was conducted with lesions classified prospectively using subjective assessment and tumor markers with the ROMA. The SRR assessment and ADNEX risk estimation were applied retrospectively. The sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-) were calculated for all tests. In total, 108 patients (the median age: 48 yrs, 44 postmenopausal) with 62 (79.6%) benign masses, 26 (24.1%) BOTs and 20 (18.5%) stage I MOLs were included. When comparing benign masses with combined BOTs and stage I MOLs, SA correctly identified 76% of benign masses, 69% of BOTs and 80% of stage I MOLs. Significant differences were found for the presence and size of the largest solid component (p = 0.0006), the number of papillary projections (p = 0.01), papillation contour (p = 0.008) and IOTA color score (p = 0.0009). The SRR and ADNEX models were characterized by the highest sensitivity (80% and 70%, respectively), whereas the highest specificity was found for SA (94%). The corresponding likelihood ratios were as follows: LR+ = 3.59 and LR- = 0.43 for the ADNEX; LR+ = 6.40 and LR- = 0.63 for SA and LR+ = 1.85 with LR- = 0.35 for the SRR. The sensitivity and specificity of the ROMA test were 50% and 85%, respectively, with LR+ = 3.44 and LR- = 0.58. Of all the tests, the ADNEX model had the highest diagnostic accuracy of 76%. This study demonstrates the limited value of diagnostics based on CA125 and HE4 serum tumor markers and the ROMA algorithm as independent modalities for the detection of BOTs and early stage adnexal malignant tumors in women. SA and IOTA methods based on ultrasound examination may present superior value over tumor marker assessment.

A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator?

There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.

Ground glass opacity resection extent assessment trial (GREAT): A study protocol of multi-institutional, prospective, open-label, randomized phase III trial of minimally invasive segmentectomy versus lobectomy for ground glass opacity (GGO)-containing early-stage invasive lung adenocarcinoma.

With widely use of computed tomography (CT) screening, an increasing number of early-stage lung cancers appearing as ground glass opacity (GGO) have been detected. Therefore, attempts have been made to investigate the feasibility of segmentectomy instead of lobectomy for those patients with GGO. However, the two recently released phase III trials failed to distinguish between GGO-containing lesions from pure solid nodules in the inclusion criteria, and the surgical methods did not distinguish between minimally invasive surgery and open thoracotomy. In addition, total lesion size≤ 2cm was taken as the inclusion criterion, instead of the solid part size recommended in the eighth edition of Union for International Cancer Control/International Association for the Study of Lung Cancer/American Joint Committee on Cancer (UICC/IASLC/AJCC) staging system. Hence, this present trial aims to figure out whether minimally invasive segmentectomy shows superiority in perioperative outcomes and non-inferiority in oncological prognosis over minimally invasive lobectomy among patients with GGO-containing clinical stage T1a-T1b lung invasive adenocarcinoma (IADC). Sample sizes are 1024 patients, who will be randomized into minimally invasive segmentectomy and lobectomy groups . Patients will be collected from 19 hospitals in China. Patients with peripheral mixed ground glass opacity (mGGO) with 0.5cm<total lesion size ≤ 3cm and 0.5cm<solid component size ≤ 2cm in lung window on CT scan are enrolled. The primary endpoint is 5-year recurrence-free survival (RFS). The secondary endpoints are 5-year overall survival (OS), perioperative outcomes and pulmonary function preservation. Kaplan-Meier curves are plotted to compare the survival outcomes between the two arms. Subgroup analyses are also performed to investigate the benefit of segmentectomy among different clinical variables. If the primary endpoint shows at least non-inferiority in 5-year RFS of segmentectomy to lobectomy, minimally invasive segmentectomy can be recommended as an alternative to minimally invasive lobectomy. If second endpoints show non-inferior 5-year OS along with better perioperative outcomes and/or better pulmonary function preservation of segmentectomy compared with lobectomy after the primary endpoint has reached, minimally invasive segmentectomy may become a preferred procedure for patients with GGO-containing clinical stage T1a-T1b IADCs. Chinese Clinical Trial Registry. Trial registration number: ChiCTR2000037065. https://www.chictr.org.cn/, identifier ChiCTR2000037065.

Establishment and validation a prediction model for discrimination of invasive adenocarcinomas for patients with peripheral pulmonary subsolid nodules.

The optimal management of patients with subsolid pulmonary nodules is of growing clinical concern. This study sought to develop and validate a more precise predictive model to evaluate the pathological invasiveness of patients with lung peripheral subsolid nodules (SSNs). The data of 1,140 patients with peripheral SSNs who underwent surgical resection at Shanghai Renji Hospital from January 2014 to December 2018 were retrospectively analyzed. The patients were randomly assigned to a training and validation cohort (at a ratio of 2 to 1). Clinical parameters and imaging features were collected to estimate the independent predictors of pathological invasiveness of SSNs. A nomogram model was developed and applied to the validation cohort. The predictive performance of the nomogram model was evaluated by a calibration curve analysis, an area under the receiver operating characteristic curve (AUC) analysis, and a decision curve analysis (DCA), which was also compared with other diagnostic models. In the multivariate analysis, the nodule diameter (P<0.001), solid component size (P<0.001), mean CT attenuation (P=0.001), spiculation (P<0.001), and pleura indentation (P=0.011) were identified as independent predictors of the pathological invasiveness of SSNs. A nomogram model based on the results of the multivariate analysis was developed and showed a robust discrimination in the validation cohort, with an AUC of [0.890; 95% confidence interval (CI), 0873-0.907], which was higher than another two reported models. The calibration curve showed optimal agreement between the pathological invasive probability as predicted by the nomogram and the actual probability. We developed and validated a nomogram model to evaluate the risk of the pathological invasiveness for patients with lung SSNs. The AUC of this nomogram model was higher than another two reported models. Our nomogram model may help clinicians to make individualized treatment more precisely.

Oncologic outcomes of segmentectomy for stage IA radiological solid-predominant lung cancer >2 cm in maximum tumour size.

We aimed to compare the outcomes of segmentectomy with those of lobectomy in clinical-stage IA radiological solid-predominant non-small-cell lung cancer (NSCLC) >2 cm in maximum tumour size. A retrospective review was performed for radiological solid-predominant NSCLC >2-3 cm in maximum tumour size with a ground-glass opacity component on thin-section computed tomography. Multivariable or propensity score-matched analyses were performed to control for confounders for survival. Overall survival (OS) was analysed using a Kaplan-Meier estimation. Of the 215 eligible cases, segmentectomy and lobectomy were performed in 46 and 169 patients. Multivariable analysis revealed that standardized uptake value (hazard ratio: 1.148, 95% confidence interval: 1.032-1.276, P = 0.011) was an independently significant prognosticators of OS, while the operative mode was not associated (hazard ratio: 0.635, 95% confidence interval: 0.132-3.049, P = 0.570). The 5 y-OS was excellent and did not differ significantly between segmentectomy and lobectomy (95.5% vs 90.2%; P = 0.697), which was also shown in the propensity score analysis (96.8% vs 94.0%; P = 0.406), with a median follow-up time of 5.2 years. Locoregional recurrence was found in 2 (4.3%) segmentectomy and 13 (7.7%) lobectomy (P = 0.443). In the subgroup analysis stratified by solid component size, the 5 y-OS was similar between segmentectomy and lobectomy in the c-T1b and c-T1c groups, respectively [c-T1b (n = 163): 94.1% vs 91.8%; P = 0.887 and c-T1c (n = 52): 100% vs 84.9%; P = 0.197]. Segmentectomy showed similar oncological results compared to lobectomy in solid-predominant NSCLC with a ground-glass opacity component >2-3 cm in maximum tumour size. More prospective randomized trials are needed to adequately expand the indication of anatomic segmentectomy for early-stage NSCLC.

A Multicenter Study of Complex Segmentectomy Versus Wedge Resection in Clinical Stage 0-IA Non-Small Cell Lung Cancer

BackgroundAlthough sublobar resection is widely used for lung cancer treatment, very limited data are available comparing outcomes after complex segmentectomy and wedge resection. We compared the oncological outcomes of complex segmentectomy and wedge resection for clinical stage 0-IA lung cancer via a large cohort, multicenter database using propensity score-matched analysis. Patients and MethodsWe retrospectively analyzed data from 506 clinical stage 0-IA, solid component size ≤ 2.0 cm lung cancer patients who underwent surgical resection at three institutions between 2010 and 2018. Surgical results after complex segmentectomy (n = 222) and “location-adjusted” wedge resection (n = 284) were analyzed for all patients and their propensity score-matched pairs. ResultsIn all cohort, the complex segmentectomy group tended to have a better prognosis than the wedge resection group (5 year cancer-specific survival rate, 97.4% vs. 93.7%; P = .065 and 5 year recurrence-free interval [RFI] rates, 96.9% vs. 86.1%; P = .0005). This trend was also identified in subanalyses for pure solid tumors. In 179 propensity score-matched pairs, the prognosis of patients with complex segmentectomy tended to be better than that of patients with wedge resection (5 year cancer-specific survival rates, 96.8% vs. 92.9%; 5 year RFI rates, 96.3% vs. 87.5%). Multivariable Cox regression analysis for RFI revealed that complex segmentectomy significantly reduced lung cancer recurrence compared with wedge resection (hazard ratio, 0.32; 95% confidence interval, 0.12-0.73; P = .0061). ConclusionsComplex segmentectomy can provide better oncological outcomes compared with wedge resection.

Preoperative Patient and Lesion Features Are Valuable for Predicting the Prognosis of Resectable Peripheral Non-Small Cell Lung Cancer.

The aim of the study was to investigate the preoperative factors affecting the survival of patients with resectable peripheral non-small cell lung cancer (NSCLC) to improve the management of NSCLC. One hundred ninety-nine patients with peripheral NSCLC diagnosed clinically without lymph node metastasis were enrolled. The preoperative computed tomography characteristics of the tumors were retrospectively analyzed and the preoperative clinical data were collected. The size of the solid components for lung adenocarcinomas containing ground-glass opacity (GGO) component were measured. Kaplan-Meier method with log-rank test was used to compare overall survival (OS) between groups. Univariate and multivariate cox regression analyses were used to identify prognostic factors. Survival analysis showed that the OS of the group with a tumor of 3 cm or less was longer than that of the group with a tumor greater than 3 cm, the OS of the group with GGO component was superior to that of the group without GGO component, and the OS of the group with elevated carcinoembryonic antigen (CEA) levels was inferior to that of the group with normal CEA levels. Multivariate Cox regression analysis showed that tumor size, density, and preoperative CEA level were independent factors affecting OS, with hazard ratios of 2.401, 0.457, and 1.948, respectively. The analysis of lung adenocarcinomas with GGO component demonstrated that the mean size of the solid component in the nonsurviving group was significantly larger than that in the surviving group (mean, 23 ± 6.4 vs 8.6 ± 7.0 mm). The area under the receiver operating characteristic curve of the solid component size of lung cancer containing GGO component to predict postoperative death was 0.932. Tumor size, density, and preoperative CEA level were independent prognostic factors of patients with resectable peripheral NSCLCs. Preoperative computed tomography findings can be valuable for predicting the prognosis of patients with NSCLC after surgery.

Presence of a Ground-Glass Opacity Component Is the True Prognostic Determinant in Clinical Stage I NSCLC

IntroductionRecent studies have suggested that including presence or absence of ground-glass opacity (GGO) may improve the tumor descriptor (T descriptor) classification in clinical stage I NSCLC. In this study, we analyzed prognostic implications of presence or absence of GGO, size of the solid component, and predominant histology to identify the true prognostic determinant for early-stage NSCLC. MethodsWe retrospectively examined 384 patients with clinical stage I NSCLC (solid: 242, part solid: 142) who underwent complete resection between 2009 and 2013. ResultsSurvival curves of the whole cohort revealed good separation using the current TNM classification. Nevertheless, the part-solid group had a favorable prognosis irrespective of solid component size. Conversely, patients in the solid tumor group with tumors between 3 and 4 cm had a worse prognosis than patients whose tumors were less than or equal to 3 cm. Thus, we propose the following novel T descriptor classification: IA, part-solid tumors; IB, solid tumors less than or equal to 3 cm; and IC, solid tumors between 3 and 4 cm. This novel classification system stratified patient prognosis better than the current classification. On pathologic evaluation, the part-solid group always had better prognoses than the solid group in each subgroup divided by pathologic grade. ConclusionsThese results suggest that presence of GGO is the true prognostic determinant of stage I NSCLC, irrespective of the size of the solid component. Our novel T descriptor classification system could more accurately predict prognoses of clinical stage I NSCLC cases.