Prognostic Impact of Radiological Consolidation Tumor Ratio in Clinical Stage IA Pulmonary Ground Glass Opacities

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

AIM

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).

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

The presence of a GGO component is strongly associated with a favourable prognosis in grade 3 INPA.

Mediastinal lymph node dissection versus spared mediastinal lymph node dissection in stage IA non-small cell lung cancer presented as ground glass nodules: study protocol of a phase III, randomised, multicentre trial (MELDSIG) in China

INTRODUCTION

Radical surgery including mediastinal lymph node dissection is the standard treatment for early-stage non-small cell lung cancer (NSCLC). About 50% lung nodules are pure ground glass or part-solid nodules, which are predominantly clinical stage IA NSCLC. Non-solid nodules rarely develop mediastinal lymph node metastasis.

METHOD AND ANALYSIS

A phase III study was started in China to evaluate the non-inferiority in overall survival of spared mediastinal lymph node dissection compared with mediastinal lymph node dissection in stage IA NSCLC. A total of 1362 patients will be enrolled from 4 institutions in 2-3 years. The second endpoints are relapse-free survival and perioperative data, including duration of hospitalisation, duration of chest tube placement, operation time, blood loss.

ETHICS AND DISSEMINATION

This protocol has been reviewed and approved by the Clinical Research Review Board of Tianjin Medical University Cancer Institute and Hospital. The findings will be disseminated in peer-reviewed publications.

TRIAL REGISTRATION NUMBER

NCT04631770.

Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma

OBJECTIVE

To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT).

METHODS

Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram.

RESULTS

The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P =  0.029 OR =  4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR =  1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model.

CONCLUSION

The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules

Background

The use of an artificial intelligence (AI)-based diagnostic system can significantly aid in analyzing the histogram of pulmonary nodules. The aim of our study was to evaluate the value of computed tomography (CT) histogram indicators analyzed by AI in predicting the tumor invasiveness of ground-glass nodules (GGNs) and to determine the added value of contrast-enhanced CT (CECT) compared with nonenhanced CT (NECT) in this prediction.

Methods

This study enrolled patients with persistent GGNs who underwent preoperative NECT and CECT scanning. AI-based histogram analysis was performed for pathologically confirmed GGNs, which was followed by screening invasiveness-related factors via univariable analysis. Multivariable logistic models were developed based on candidate CT histogram indicators measured on either NECT or CECT. Receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the models' performance.

Results

A total of 116 patients comprising 121 GGNs were included and divided into the precancerous lesion and adenocarcinoma groups based on invasiveness. In the AI-based histogram analysis, the mean CT value [NECT: odds ratio (OR) =1.009; 95% confidence interval (CI): 1.004-1.013; P<0.001] and solid component volume (NECT: OR =1.005; 95% CI: 1.000-1.010; P=0.032) were associated with the adenocarcinoma and used for multivariable logistic modeling. The area under ROC curve (AUC) and PR curve (AUPR) were not significantly different between the NECT model (AUC =0.765, 95% CI: 0.679-0.837; AUPR =0.907, 95% CI: 0.825-0.953) and the optimal CECT model (delayed phase: AUC =0.772, 95% CI: 0.687-0.843; AUPR =0.895, 95% CI: 0.812-0.944). No significantly different metrics were observed between the NECT and CECT models (precision: 0.707 vs. 0.742; P=0.616).

Conclusions

The AI diagnostic system can help in the diagnosis of GGNs. The system displayed decent performance in GGN detection and alert to malignancy. Mean CT value and solid component volume were independent predictors of tumor invasiveness. CECT provided no additional improvement in diagnostic performance as compared with NECT.

Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation

BACKGROUND AND PURPOSE

Tumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning.

PATIENTS AND METHODS

Two datasets of 380 HCC patients who underwent LT were enrolled. Residual convolutional neural networks were used to identify six histological structures of HCC. The individual risk score of each structure and DPS were derived by a modified DeepSurv network. Cox regression analysis and Concordance index were used to evaluate the prognostic significance. The cellular exploration of prognostic immune biomarkers was performed by quantitative and spatial proximity analysis according to three panels of 7-color immunofluorescence.

RESULTS

The overall classification accuracy of HCC tissue was 97%. At the structural level, immune cells were the most significant tissue category for predicting post-LT recurrence (HR 1.907, 95% CI 1.490-2.440). The C-indices of DPS achieved 0.827 and 0.794 in the training and validation cohorts, respectively. Multivariate analysis for recurrence-free survival (RFS) showed that DPS (HR 4.795, 95% CI 3.017-7.619) was an independent risk factor. Patients in the high-risk subgroup had a shorter RFS, larger tumor diameter and a lower proportion of clear tumor borders. At the cellular level, a higher infiltration of intratumoral NK cells was negatively correlated with recurrence risk.

CONCLUSIONS

This study established an effective DPS. Immune cells were the most significant histological structure related to HCC recurrence. DPS performed well in post-LT recurrence prediction and the identification of clinicopathological features.

A Nomogram Based on Consolidation Tumor Ratio Combined with Solid or Micropapillary Patterns for Postoperative Recurrence in Pathological Stage IA Lung Adenocarcinoma

BACKGROUND

Patients with pathological stage IA lung adenocarcinoma (LUAD) are at risk of relapse. The value of the TNM staging system is limited in predicting recurrence. Our study aimed to develop a precise recurrence prediction model for stage IA LUAD.

MATERIALS AND METHODS

Patients with pathological stage IA LUAD who received surgical treatment at Zhongshan Hospital Fudan University were retrospectively analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for recurrence-free survival (RFS). The predictive performance of the model was assessed using calibration plots and the concordance index (C-index).

RESULTS

The multivariate Cox regression analysis revealed that CTR (0.75 < CTR ≤ 1; HR = 9.882, 95% CI: 2.036-47.959, p = 0.004) and solid/micropapillary-predominance (SMPP; >5% and the most dominant) (HR = 4.743, 95% CI: 1.506-14.933, p = 0.008) were independent prognostic factors of RFS. These risk factors were used to construct a nomogram to predict postoperative recurrence in these patients. The C-index of the nomogram for predicting RFS was higher than that of the eighth T-stage system (0.873 for the nomogram and 0.643 for the eighth T stage). The nomogram also achieved good predictive performance for RFS with a well-fitted calibration curve.

CONCLUSIONS

We developed and validated a nomogram based on CTR and SMP patterns for predicting postoperative recurrence in pathological stage IA LUAD. This model is simple to operate and has better predictive performance than the eighth T stage system, making it suitable for selecting further adjuvant treatment and follow-up.

Prognostic value of consolidation-to-tumor ratio on computed tomography in NSCLC: a meta-analysis

BACKGROUND

Although several studies have confirmed the prognostic value of the consolidation to tumor ratio (CTR) in non-small cell lung cancer (NSCLC), there still remains controversial about it.

METHODS

We systematically searched the PubMed, Embase, and Web of Science databases from inception to April, 2022 for eligible studies that reported the correlation between CTR and prognosis in NSCLC. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were extracted and pooled to assess the overall effects. Heterogeneity was estimated by I² statistics. Subgroup analysis based on the cut-off value of CTR, country, source of HR and histology type was conducted to detect the sources of heterogeneity. Statistical analyses were performed using STATA version 12.0.

RESULTS

A total of 29 studies published between 2001 and 2022 with 10,347 patients were enrolled. The pooled results demonstrated that elevated CTR was associated with poorer overall survival (HR = 1.88, 95% CI 1.42-2.50, P < 0.01) and disease-free survival (DFS)/recurrence-free survival (RFS)/progression-free survival (PFS) (HR = 1.42, 95% CI 1.27-1.59, P < 0.01) in NSCLC. According to subgroup analysis by the cut-off value of CTR and histology type, both lung adenocarcinoma and NSCLC patients who had a higher CTR showed worse survival. Subgroup analysis stratified by country revealed that CTR was a prognostic factor for OS and DFS/RFS/PFS in Chinese, Japanese, and Turkish patients.

CONCLUSIONS

In NSCLC patients with high CTR, the prognosis was worse than that with low CTR, indicating that CTR may be a prognostic factor.

Lymph Node Metastases in Surgically Resected Solitary Ground-Glass Opacities: A Two-Center Retrospective Cohort Study and Pooled Literature Analysis

BACKGROUND

An increasing body of evidence supports the noninferiority of sublobar resection compared with lobectomy in terms of survival for patients with early-stage lung cancer with ground-glass opacities (GGOs). However, few studies have focused on the incidence of lymph node (LN) metastases in these patients. We aimed to analyze N1 and N2 lymph node involvement in patients with non-small cell lung cancer (NSCLC) with GGO components stratified with different consolidation tumor ratio (CTR).

PATIENTS AND METHODS

We performed two-center studies by retrospectively reviewing a total of 864 patients with NSCLC with semisolid or pure GGO manifestation (diameter ≤ 3 cm). Clinicopathologic features and outcomes were analyzed. We also reviewed 35 studies to characterize the patient with NSCLC population with the GGO manifestation.

RESULTS

In both cohorts, there was no LN involvement for pure GGO NSCLC, while solid predominant GGO exhibited a relatively high LN involvement rate. On the basis of a pooled literature analysis, the incidence of pathologic mediastinal LN was 0% and 3.8% for pure and semisolid GGOs, respectively. GGO NSCLCs with CTR ≤ 0.5 also had rare LN involvement (0.1%).

CONCLUSIONS

From two cohorts and pooled literature analysis, LN involvement was not observed in patients with pure GGO, and very few patients with semisolid GGO NSCLC with CTR ≤ 0.5 had LN involvement, revealing that it may be unnecessary to perform lymphadenectomy for pure GGOs, while mediastinal lymph node sampling (MLNS) is enough for semisolid GGOs with CTR ≤ 0.5. For the patients with GGO CTR > 0.5, mediastinal lymphadenectomy (MLD) or MLNS should be considered.

Concurrent lung adenocarcinoma hidden among multiple shadows of COVID-19 pneumonia: A rare and instructive case report

A 40-year-old man was admitted with a diagnosis of COVID-19 pneumonia. Although most of multiple ground-glass opacities and consolidations on computed tomography improved, a round ground-glass opacity with consolidation remained unchanged and was suspected to be a part-solid nodule of lung adenocarcinoma. Pathologic diagnosis of resected tumor was papillary adenocarcinoma.

The efficacy and safety of wedge resection for peripheral stage IA lung adenocarcinoma: a real-world study based on a single center

Background

The effectiveness of segmentectomy for stage IA lung adenocarcinoma (IA-LUAD) has been well-documented. However, the efficacy and safety of wedge resection for peripheral IA-LUAD remains controversial. This study evaluated the feasibility of wedge resection in patients with peripheral IA-LUAD.

Methods

Patients with peripheral IA-LUAD who underwent wedge resection by video-assisted thoracoscopic surgery (VATS) at Shanghai Pulmonary Hospital were reviewed. Cox proportional hazards modeling was performed to identify predictors of recurrence. Receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cutoffs of identified predictors.

Results

A total of 186 patients (female/male, 115/71; mean age, 59.9 years) were included. Mean maximum dimension of consolidation component (MCD) was 5.6 mm, consolidation-to-tumor ratio (CTR) was 37%, and mean computed tomography value of tumor (CTVt) was -285.4 HU. With a median follow-up of 67 months (interquartile range, 52-72 months), the 5-year recurrence rate was 4.84%. Ten patients occurred recurrence postoperatively. No recurrence was observed adjacent to the surgical margin. Increasing MCD, CTR, and CTVt were associated with a higher risk of recurrence, with corresponding hazard ratios (HRs) of 1.212 [95% confidence interval (CI): 1.120-1.311], 1.054 (95% CI: 1.018-1.092), and 1.012 (95% CI: 1.004-1.019) with optimal cutoffs for predicting recurrence of 10 mm, 60%, and -220 HU, respectively. When a tumor had characteristics under these respective cutoffs, no recurrence was observed.

Conclusions

Wedge resection can be considered to be a safe and efficacious management strategy for patients with peripheral IA-LUAD, especially for MCD less than 10 mm, CTR less than 60% and CTVt less than -220 HU.

Preoperative prognostic prediction for stage I lung adenocarcinomas: Impact of the computed tomography features associated with the new histological grading system

Objectives

This study aimed to identify the computed tomography (CT) features associated with the new International Association for the Study of Lung Cancer (IASLC) three-tiered grading system to improve the preoperative prediction of disease-free survival of stage I lung adenocarcinoma patients.

Methods

The study included 379 patients. Ordinal logistic regression analysis was used to identify the independent predictors of IASLC grades. The first multivariate Cox regression model (Model 1) was based on the significant factors from the univariate analysis. The second multivariate model (Model 2) excluded the histologic grade and based only on preoperative factors.

Results

Larger consolidation tumor ratio (OR=2.15, P<.001), whole tumor size (OR=1.74, P=.002), and higher CT value (OR=3.77, P=.001) were independent predictors of higher IASLC grade. Sixty patients experienced recurrences after 70.4 months of follow-up. Model 1 consisted of age (HR:1.05, P=.003), clinical T stage (HR:2.32, P<.001), histologic grade (HR:4.31, P<.001), and burrs sign (HR:5.96, P<.001). Model 2 consisted of age (HR,1.04; P=.015), clinical T stage (HR:2.49, P<.001), consolidation tumor ratio (HR:2.49, P=.016), whole tumor size (HR:2.81, P=.022), and the burrs sign (HR:4.55, P=.002). Model 1 had the best prognostic predictive performance, followed by Model 2, clinical T stage, and histologic grade.

Conclusion

CTR (cut-off values of <25% and ≥75%) and whole tumor size (cut-off value of 17 mm) could stratify patients into different prognosis and be used as preoperative surrogates for the IASLC grading system. Integrating these CT features with clinical T staging can improve the preoperative prognostic prediction for stage I lung adenocarcinoma patients.

Tumor size, but not consolidation-to-tumor ratio, is an independent prognostic factor for part-solid clinical T1 non-small cell lung cancer

BACKGROUND

Tumor size and consolidation-to-tumor ratio (CTR) are crucial for non-small cell lung cancer (NSCLC) prognosis. However, the optimal CTR cutoff remains unclear. Whether tumor size and CTR are independent prognostic factors for part-solid NSCLC is under debate. Here, we aimed to evaluate the prognostic impacts of CTR and tumor size on NSCLC, especially on part-solid NSCLC.

METHODS

We reviewed 1366 clinical T1 NSCLC patients who underwent surgical treatment. Log-rank test and Cox regression analyses were adopted for prognostic evaluation. The "surv_cutpoint" function was used to identify the optimal CTR and tumor size cutoff values.

RESULTS

There were 416, 510, and 440 subjects with pure ground-glass opacity (pGGO), part-solid, and pure solid nodules. The 5-year overall survival (disease-free survival) for patients with pGGO, part-solid, and pure solid nodules were 99.5% (99.5%), 97.3% (95.8%), and 90.4% (78.9%), respectively. Multivariate Cox regression analysis indicated that CTR was an independent prognostic factor for the whole patients, and the optimal CTR cutoff was 0.99. However, for part-solid NSCLC, CTR was not independently associated with survival, even if categorized by the optimal cutoffs. The predicted optimal cutoffs of total tumor size and solid component size were 2.4 and 1.4 cm for part-solid NSCLC. Total tumor size (HR = 6.21, 95% CI: 1.58-24.34, p = 0.009) and solid component size (HR = 2.27, 95% CI: 1.04-5.92, p = 0.045) grouped by the cutoffs were significantly associated with part-solid NSCLC prognosis.

CONCLUSIONS

CTR was an independent prognostic factor for the whole NSCLC, but not for the part-solid NSCLC. Tumor size was still meaningful for part-solid NSCLC.

Adaptation criterion for segmentectomy in small-sized early stage non-small cell lung cancer

BACKGROUND

Although the utility of segmentectomy for early-stage non-small cell lung cancer (NSCLC) has been reported, the adaptation criterion for segmentectomy is unclear.

METHODS

In total, 171 NSCLC patients who underwent segmentectomy or lobectomy with a consolidation tumor diameter on computed tomography of ≤20 mm were analyzed.

RESULTS

Consolidation diameter (p = 0.01), consolidation to tumor ratio (CTR) (p < 0.01), maximum standardized uptake value (SUV_max ) (p < 0.01), and segmentectomy (p = 0.01) were significantly different upon univariate analysis among patients stratified by recurrence. Positive correlations were observed between the consolidation diameter on CT and CEA (correlation coefficient; r = 0.19, p = 0.01), SUV_max (r = 0.48, p < 0.01), and CTR (r = 0.83, p < 0.01). Because there was a significant correlation among the consolidation diameter of tumors on CT, CTR, and SUV_max in this study, we integrated these factors into one. Consolidation, CTR, and SUV_max (hazard ratio [HR]: 3.77, 95% confidence interval [CI]: 1.35-11.29, p = 0.01) and segmentectomy (HR: 0.24, 95% CI: 0.03-0.90, p = 0.03) were risk factors for recurrence in a multivariate analysis. There was a significant difference between the segmentectomy and lobectomy groups (5-year relapse-free survival [RFS] 96.5% vs. 80.7%, p = 0.02).

CONCLUSIONS

Consolidation tumor diameter on CT, CTR, and SUV_max is a risk factor for recurrence. These results suggest that for patients with small-sized early stage NSCLC, this combined factor is important for determining the indication for segmentectomy.

Invasive area to tumor ratio is a significant prognostic factor for non-small cell lung cancer

BACKGROUND

Although T factor is defined as the size of invasive area rather than total tumor size in the eighth edition of the TNM classification, whether the pathological invasive area to tumor ratio (ITR) is a prognostic factor has not yet been evaluated.

METHODS

In total, 432 lung adenocarcinoma patients were analyzed, among which 266 patients with pathological stage IA were used to perform a subanalysis.

RESULTS

Smoking status (odds ratio [OR]: 0.43, p = 0.01), neutrophil-to-lymphocyte ratio (NLR) (OR: 1.97, p = 0.03), maximum standardized uptake value (SUV_max ) (OR: 3.62, p < 0.01), and ITR (OR: 6.76, p < 0.01) were significantly different in univariate analysis. Smoking status (OR: 0.34, p < 0.01), SUV_max (OR: 3.05, p < 0.01), and ITR (OR: 5.44, p < 0.01) were risk factors for recurrence in multivariate analysis. In patients with pathological stage IA disease, smoking status (OR: 0.34, p = 0.03), NLR (OR: 2.30, p = 0.04), SUV_max (OR: 3.63, p < 0.01), pathological invasive area (OR: 4.00, p < 0.01), and ITR (OR: 6.03, p < 0.01) were significantly different in univariate analysis. Smoking status (OR: 0.27, p = 0.02), SUV_max (OR: 3.93, p < 0.01), and ITR (OR: 4.38, p < 0.01) were significant risk factors for recurrence in multivariate analysis.

CONCLUSIONS

SUV_max and ITR are risk factors for recurrence. These results suggest that SUV_max is important for deciding the indication for limited resection or adjuvant chemotherapy, and ITR is an adaptation criterion for adjuvant chemotherapy for early-stage lung adenocarcinoma patients.

[Research Progress in the Effect of Consolidation Tumor Ratio on the Diagnosis and Treatment of Early-stage Peripheral Lung Cancer]

Consolidation tumor ratio (CTR) is a hot issue in lung cancer imaging studies in recent years. It is defined as the proportion of the maximum consolidation diameter divided by the maximum tumor diameter in the lung window scanned by high resolution computed tomography (HRCT). Many studies have also confirmed that it can be used as an indicator to identify whether a lung tumor is benign or malignant at the early stage, the main basis on which to decide whether sublobectomy can be performed, and is an independent factor for the recurrence and prognosis of early-stage lung cancer. Especially after tumor size and CTR results of JCOG0804 and JCOG0802 trials in Japan were published, a breakthrough in the treatment method upends the conventional surgical approach, which benefits patients with early-stage lung cancer. But insufficient research data on CTR leads to the fact that an evaluation system to measure CTR is yet to be built. This paper discusses the research progress in CTR prediction of benign or malignancy of pulmonary nodules, how to choose a surgical approach, lymph node dissection, spread through air spaces (STAS) and other hot issues. It also investigates the possible indicators to predict efficacy based on CTR, summarizes and analyzes the development trend of surgical methods to treat early-stage peripheral lung cancer and challenges, to provide new ideas for clinical application.
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A long waiting time from diagnosis to treatment decreases the survival of non-small cell lung cancer patients with stage IA1: A retrospective study

Objective

The prognostic effect of delayed treatment on stage IA1 non-small cell lung cancer (NSCLC) patients is still unclear. This study aimed to explore the association between the waiting time before treatment and the prognosis in stage IA1 NSCLC patients.

Methods

Eligible patients diagnosed with pathological stage IA1 NSCLC were included in this study. The clinical endpoints were overall survival (OS) and cancer-specific survival (CSS). The Kaplan-Meier method, the Log-rank test, univariable, and multivariable Cox regression analyses were used in this study. Propensity score matching was used to reduce the bias of data distribution.

Results

There were eligible 957 patients in the study. The length of waiting time before treatment stratified the survival in patients [&lt;3 months vs. ≥3-months, unadjusted hazard ratio (HR) = 0.481, P = 0.007; &lt;2 months vs. ≥2-months, unadjusted HR = 0.564, P = 0.006; &lt;1 month vs. ≥1-month, unadjusted HR = 0.537, P = 0.001]. The 5-year CSS rates were 95.0% and 77.0% in patients of waiting time within 3 months and over 3 months, respectively. After adjusting for other confounders, the waiting time was identified as an independent prognostic factor.

Conclusions

A long waiting time before treatment may decrease the survival of stage IA1 NSCLC patients. We propose that the waiting time for those patients preferably is less than one month and should not exceed two months.

Construction of Pulmonary Nodule CT Radiomics Random Forest Model Based on Artificial Intelligence Software for STAS Evaluation of Stage IA Lung Adenocarcinoma

Objective

Spread through air space (STAS) is an invasive characterization of lung adenocarcinoma and is regarded as a risk factor for poor prognosis. The aim of this study is to develop a random forest model for preoperative prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma.

Methods

92 patients with stage IA lung adenocarcinoma, who underwent computed tomography (CT) scan and surgical resection, were retrospectively reviewed. Each pulmonary nodule was automatically segmented by artificial intelligence (AI) software, and its CT-based radiomics were extracted. All patients were pathologically classified into STAS-negative and STAS-positive cohorts; then, clinical pathological and CT-based radiomics were compared between the two cohorts. Finally, a prediction model for evaluating STAS status in stage IA lung adenocarcinoma was established by a random forest model.

Results

Among 92 patients with stage IA lung adenocarcinoma, STAS positive was identified in 19 patients. The random forest classification model identified predictive features, including CT maximum value, consolidation to tumor ratio (CTR), 3D diameter, CT mean value, entropy, and CT minimum value. The misclassification rate of the random forest model is only 7.69%.

Conclusion

The risk factors of STAS in stage IA lung adenocarcinoma can be effectively identified based on the random forest model, and the hierarchical management of characteristic risk can be effectively realized. A random forest model for predicting STAS in IA lung adenocarcinoma is simple and practical.

Exploring pathological signatures for predicting the recurrence of early-stage hepatocellular carcinoma based on deep learning

Background

Postoperative recurrence impedes the curability of early-stage hepatocellular carcinoma (E-HCC). We aimed to establish a novel recurrence-related pathological prognosticator with artificial intelligence, and investigate the relationship between pathological features and the local immunological microenvironment.

Methods

A total of 576 whole-slide images (WSIs) were collected from 547 patients with E-HCC in the Zhongshan cohort, which was randomly divided into a training cohort and a validation cohort. The external validation cohort comprised 147 Tumor Node Metastasis (TNM) stage I patients from The Cancer Genome Atlas (TCGA) database. Six types of HCC tissues were identified by a weakly supervised convolutional neural network. A recurrence-related histological score (HS) was constructed and validated. The correlation between immune microenvironment and HS was evaluated through extensive immunohistochemical data.

Results

The overall classification accuracy of HCC tissues was 94.17%. The C-indexes of HS in the training, validation and TCGA cohorts were 0.804, 0.739 and 0.708, respectively. Multivariate analysis showed that the HS (HR= 4.05, 95% CI: 3.40-4.84) was an independent predictor for recurrence-free survival. Patients in HS high-risk group had elevated preoperative alpha-fetoprotein levels, poorer tumor differentiation and a higher proportion of microvascular invasion. The immunohistochemistry data linked the HS to local immune cell infiltration. HS was positively correlated with the expression level of peritumoral CD14+ cells (p= 0.013), and negatively with the intratumoral CD8+ cells (p&lt; 0.001).

Conclusions

The study established a novel histological score that predicted short-term and long-term recurrence for E-HCCs using deep learning, which could facilitate clinical decision making in recurrence prediction and management.

Ground Glass Opacity and Adjuvant Chemotherapy in Pathological Stage IB-IIA Lung Adenocarcinoma

Background

The prognostic value of ground glass opacity (GGO) in stage IA non-small cell lung cancer (NSCLC) has been widely recognized. However, studies investigating its value in the related stage IB-IIA lung adenocarcinoma (LUAD) remains lacking. The impact of adjuvant chemotherapy (ACT) on pathological stage IB-IIA LUAD is also controversial.

Materials and Methods

We retrospectively reviewed the clinical records of 501 patients with pathological stage IB-IIA LUAD at the Sun Yat-sen University Cancer Center from January 2008 to June 2018. We calculated and compared survival curves using the Kaplan-Meier test and log-rank test. Cox regression models were performed to determine independent prognostic factors of disease-free survival (DFS) and overall survival (OS). We established nomograms to predict the OS and DFS of LUAD patients. Calibration and receiver operator characteristic curves were conducted to assess the predictive performance of two nomograms. Based on the nomogram, we identified candidate patients that may most benefit from ACT after surgery.

Results

The number of patients with pure solid, part GGO, and pure GGO nodules was 240, 242, and 19, respectively, and 125 patients who received ACT. Patients with consolidation-to-tumor ratio (CTR) <0.75 had longer OS (P = 0.026) and DFS (P = 0.003). Pathological tumor size and at least 10 lymph nodes (LNs) resection were independent prognostic factors of both OS and DFS. CTR <0.75 was positively associated with DFS. The C-index of nomograms predicting individual OS and DFS was 0.660 and 0.634, respectively. Based on the nomogram for OS, ACT was found to be a positive prognostic indicator of OS (P = 0.031, HR = 0.5141, 95% CI 0.281-0.942) in patients with nomogram total points ≥5.

Conclusion

CTR <0.75 is associated with a better DFS in patients with stage IB-IIA LUAD. Nomograms developed by integrating pathological tumor size, at least 10 LNs resection, and CTR ≥0.75 for predicting individual OS and DFS displayed a good predictive capacity and clinical value, which were also proved to be a useful tool for selecting patients most benefiting from ACT.

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating the gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction. In this study, 5,198 differentially expressed genes (DEGs) and 270 DNA methylation-driven genes were selected to obtain 146 differentially expressed DNA methylation-driven genes (DEMDGs). Two clusters were distinguished by consensus clustering using 146 DEMDGs. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Cluster-based immune status analysis and functional enrichment analysis of 106 DEGs provide new insights for the development of RCC. To predict the prognosis of patients with RCC, a prognostic model based on eight DEMDGs was constructed. The patients were divided into high-risk groups and low-risk groups based on their risk scores. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:3496) were built to validate the predictive accuracy of the prognostic model. Gene set enrichment analysis was performed to annotate the signaling pathways in which the genes are enriched. The correlation of the risk score with clinical features, immune status, and drug susceptibility was also evaluated. These results suggested that the prognostic model might be a promising prognostic tool for RCC and might facilitate the management of patients with RCC.

[Pattern of Recurrence and Metastasis after Radical Resection of Non-small Cell Lung Cancer]

The rate of recurrence and metastasis of non-small cell lung cancer after radical resection is still very high. The risk factors for recurrence and metastasis have been extensively studied, but the dynamic pattern of postoperative recurrence hazard over time is relatively lacking. The dynamic recurrence hazard rate curve is applied to describe the rate of recurrence at any point time among the "at-risk" patients. In this article, by reviewing the previous literature, the characteristics of the dynamic recurrence and metastasis pattern after radical resection of non-small cell lung cancer and the clinical factors affecting the recurrence and metastasis pattern are summarized, in order to screen out specific populations with high recurrence risk and give them personalized follow-up strategy and diagnosis and treatment.
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The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

Background

Visceral pleural invasion (VPI) is a clinical manifestation associated with a poor prognosis, and diagnosing it preoperatively is highly imperative for successful sublobar resection of these peripheral tumors. We evaluated the roles of computed tomography (CT) features and circulating tumor cells (CTCs) for improving VPI detection in patients with clinical T1N0M0 invasive lung adenocarcinoma.

Methods

Three hundred and ninety-one patients were reviewed retrospectively in this study, of which 234 presented with a pleural tag or pleural contact on CT images. CTCs positive for the foliate receptors were enriched and analyzed prior to surgery. Logistic regression analyses were performed to assess the association of CT features and CTCs with VPI, and the receiver operating characteristic (ROC) curve was generated to compare the predictive power of these variables.

Results

Patients mostly underwent either segmentectomies (18.9%) or lobectomies (79.0%). Only 49 of the 234 patients with pleural involvement on CT showed pathologically confirmed VPI. Multivariate logistic regression analysis revealed that CTC level ≥10.42 FU/3 mL was a significant VPI risk factor for invasive adenocarcinoma cases ≤30 mm [adjusted odds ratio (OR) =4.62, 95% confidence interval (CI): 2.05–10.44, P<0.001]. Based on CT features, subgroup analyses showed that the solid portion size was a statistically significant independent predictor of VPI for these peripheral nodules with pleural tag, while the solid portion length of the interface was an independent predictor of pleural contact. The receiver operating curve analyses showed that the combination of CTC and CT features were highly predictive of VPI [area under the curve (AUC) =0.921 for pleural contact and 0.862 for the pleural tag, respectively].

Conclusions

CTC, combined with CT features of pleural tag or pleural contact, could significantly improve VPI detection in invasive lung adenocarcinomas at clinical T1N0M0 stage prior to the patient’s surgery.