Clinical T1aN0M0 lung cancer: differences in clinicopathological patterns and oncological outcomes based on the findings on high-resolution computed tomography

A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules

BACKGROUND

Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.

METHODS

The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).

RESULTS

A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.

CONCLUSION

In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

Recent Advancements in Minimally Invasive Surgery for Early Stage Non-Small Cell Lung Cancer: A Narrative Review

Introduction: Lung cancer remains a global health concern, with non-small cell lung cancer (NSCLC) comprising the majority of cases. Early detection of lung cancer has led to an increased number of cases identified in the earlier stages of NSCLC. This required the revaluation of the NSCLC treatment approaches for early stage NSCLC. Methods: We conducted a comprehensive search using multiple databases to identify relevant studies on treatment modalities for early stage NSCLC. Inclusion criteria prioritized, but were not limited to, clinical trials and meta-analyses on surgical approaches to early stage NSCLC conducted from 2021 onwards. Discussion: Minimally invasive approaches, such as VATS and RATS, along with lung resection techniques, including sublobar resection, have emerged as treatments for early stage NSCLC. Ground-glass opacities (GGOs) have shown prognostic significance, especially when analyzing the consolidation/tumor ratio (CTR). There have also been updates on managing GGOs, including the non-surgical approaches, the extent of lung resection indicated, and the level of lymphadenectomy required. Conclusions: The management of early stage NSCLC requires a further assessment of treatment strategies. This includes understanding the required extent of surgical resection, interpreting the significance of GGOs (specifically GGOs with a high CTR), and evaluating the efficacy of alternative therapies. Customized treatment involving surgical and non-surgical interventions is essential for advancing patient care.

A Self-supervised Learning-Based Fine-Grained Classification Model for Distinguishing Malignant From Benign Subcentimeter Solid Pulmonary Nodules

RATIONALE AND OBJECTIVES

Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images.

MATERIALS AND METHODS

This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model's efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined.

RESULTS

Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model's performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model's performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860.

CONCLUSION

This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.

Distinct impacts of radiological appearance on lymph node metastasis and prognosis based on solid size in clinical T1 non-small cell lung cancer

BACKGROUND

Solid nodules (SN) had more aggressive features and a poorer prognosis than part-solid nodules (PSN). This study aimed to evaluate the specific impacts of nodule radiological appearance (SN vs. PSN) on lymph node metastasis and prognosis based on solid size in cT1 non-small cell lung cancer (NSCLC).

METHODS

Patients with cT1 NSCLC who underwent anatomical resection between 2010 and 2019 were retrospectively screened. Univariable and multivariable logistic regression analyses were adopted to evaluate the associations between nodule radiological appearance and lymph node metastasis. The log-rank test and Cox regression analyses were applied for prognostic evaluation. The cumulative recurrence risk was evaluated by the competing risk model.

RESULTS

There were 958 and 665 NSCLC patients with PSN and SN. Compared to the PSN group, the SN arm had a higher overall lymph node metastasis rate (21.7% vs. 2.7%, P < 0.001), including nodal metastasis at N1 stations (17.7% vs. 2.1%), N2 stations (14.0% vs. 1.6%), and skip nodal metastasis (3.9% vs. 0.6%). However, for cT1a NSCLC, no significant difference existed between SN and PSN (0 vs. 0.4%, P = 1). In addition, the impacts of nodule radiological appearance on lymph node metastasis varied between nodal stations. Solid NSCLC had an inferior prognosis than part-solid patients (5-year disease-free survival: 79.3% vs. 96.2%, P < 0.001). The survival inferiority only existed for cT1b and cT1c NSCLC, but not for cT1a. Strikingly, even for patients with nodal involvement, SN still had a poorer disease-free survival (P = 0.048) and a higher cumulative incidence of recurrence (P < 0.001) than PSN. Specifically, SN had a higher recurrence risk than PSN at each site. Nevertheless, the distribution of recurrences between SN and PSN was similar, except that N2 lymph node recurrences were more frequent in solid NSCLC (28.21% vs. 7.69%, P = 0.041).

CONCLUSION

SN had higher risks of lymph node metastasis and poorer prognosis than PSN for cT1b and cT1c NSCLC, but not for cT1a. SN exhibited a greater proportion of N2 lymph node recurrence than PSN. SN and PSN needed distinct strategies for nodal evaluation and postoperative follow-up.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

BACKGROUND

We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs).

METHODS

A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC).

RESULTS

Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value.

CONCLUSIONS

The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs.

RELEVANCE STATEMENT

The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work.

KEY POINTS

• We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.

Invasiveness identification in pure ground-glass nodules: exploring the generalizability of radiomics based on external validation and stress testing

PURPOSE

This study aimed to apply external validation and stress tests to evaluate the generalizability of radiomics models built using various machine-learning methods for identifying the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs).

METHODS

This retrospective study enrolled 495 patients (514 pGGNs) confirmed as lung adenocarcinomas by postoperative pathology from three centers. All nodules were included in the primary cohort (randomly divided into training and test cohorts), two external validation cohorts, and two stress test cohorts. Six machine-learning radiomics models were constructed in the training cohort using the optimal features. Performance of radiomics models and clinical models were compared in primary cohort and external validation cohorts. The stress tests included stratified performance evaluation and shifted performance evaluation and contrastive evaluation under three single-condition modification settings. The predictive performance was validated by area under curve (AUC) of receiver operating characteristic (ROC).

RESULTS

Of the six radiomics models, the best logistic regression (LR) model was able to maintain high differential diagnostic capability (AUC: 0.849 ± 0.049) and good stability (relative standard deviation, 5.719%), but it showed poorer performance (AUC = 0.835) than the clinical model (AUC = 0.862) in the external validation cohort E1. The stress tests suggested LR model had no significant difference in performance between subgroups after stratification and had good consistency in the predictions before and after the three transformations (Kappa = 0.960, 0.840, and 0.933, respectively; p < 0.05, all).

CONCLUSION

The rigorous testing procedure facilitates the selection of high-performance radiomics models with good clinical generalizability.

Advances in diagnosis and prediction for aggression of pure solid T1 lung cancer

A growing number of early-stage lung cancers presenting as malignant pulmonary nodules have been diagnosed because of the increased adoption of low-dose spiral computed tomography. But pure solid T1 lung cancer with ≤3 cm in the greatest dimension is not always at an early stage, despite its small size. This type of cancer can be highly aggressive and is associated with pathological involvement, metastasis, postoperative relapse, and even death. However, it is easily misdiagnosed or delay diagnosed in clinics and thus poses a serious threat to human health. The percentage of nodal or extrathoracic metastases has been reported to be >20% in T1 lung cancer. As such, understanding and identifying the aggressive characteristics of pure solid T1 lung cancer is crucial for prevention, diagnosis, and therapeutic strategies, and beneficial to improving the prognosis. With the widespread of lung cancer screening, these highly invasive pure solid T1 lung cancer will become the main advanced lung cancer in future. However, there is limited information regarding precision medicine on how to identify these "early-stage" aggressive lung cancers. To provide clinicians with new insights into early recognition and intervention of the highly invasive pure solid T1 lung cancer, this review summarizes its clinical characteristics, imaging, pathology, gene alterations, immune microenvironment, multi-omics, and current techniques for diagnosis and prediction.

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.

A nomogram combining thoracic CT and tumor markers to predict the malignant grade of pulmonary nodules ≤3 cm in diameter

Background

With the popularity of computed tomography (CT) of the thorax, the rate of diagnosis for patients with early-stage lung cancer has increased. However, distinguishing high-risk pulmonary nodules (HRPNs) from low-risk pulmonary nodules (LRPNs) before surgery remains challenging.

Methods

A retrospective analysis was performed on 1064 patients with pulmonary nodules (PNs) admitted to the Qilu Hospital of Shandong University from April to December 2021. Randomization of all eligible patients to either the training or validation cohort was performed in a 3:1 ratio. Eighty-three PNs patients who visited Qianfoshan Hospital in the Shandong Province from January through April of 2022 were included as an external validation. Univariable and multivariable logistic regression (forward stepwise regression) were used to identify independent risk factors, and a predictive model and dynamic web nomogram were constructed by integrating these risk factors.

Results

A total of 895 patients were included, with an incidence of HRPNs of 47.3% (423/895). Logistic regression analysis identified four independent risk factors: the size, consolidation tumor ratio, CT value of PNs, and carcinoembryonic antigen levels in blood. The area under the ROC curves was 0.895, 0.936, and 0.812 for the training, internal validation, and external validation cohorts, respectively. The Hosmer-Lemeshow test demonstrated excellent calibration capability, and the fit of the calibration curve was good. DCA has shown the nomogram to be clinically useful.

Conclusion

The nomogram performed well in predicting the likelihood of HRPNs. In addition, it identified HRPNs in patients with PNs, achieved accurate treatment with HRPNs, and is expected to promote their rapid recovery.

Lung Segmentectomy in NSCLC Surgery

Current guidelines recommend surgery for early-stage non-small cell lung cancer (NSCLC). The standard treatment for patients with cT1N0 NSCLC has been lobectomy with lymph-node dissection, with sublobar resection used only in patients with inadequate cardio-respiratory reserve, with poor performance status, or who are elderly. In 1995, the Lung Cancer Study Group published the results of a randomized, prospective trial demonstrating the superiority of lobectomy compared with sublobar resection. From then on, wedge resection and segmentectomy were reserved exclusively for patients with poor functional reserve who could not tolerate lobectomy. Therefore, the exact role of segmentectomy has been controversial over the past 20 years. Recently, the randomized controlled trial JCOG0802/WJOG4607L demonstrated that segmentectomy was superior to lobectomy in patients with stage IA NSCLC (<2 cm and CTR < 0.5) in terms of both overall-survival and post-operative lung function. Based on these results, segmentectomy should be considered the standard surgical procedure for this patient group. In 2023, the randomized phase III CALGB 140503 (Alliance) trial demonstrated the efficacy and non-inferiority of sublobar resection, including wedge resection, for clinical stage IA NSCLC with tumor diameter of < 2 cm. This article is a narrative review of the current role of segmentectomy in lung cancer treatment and summarizes the most relevant studies in this context.

Application of bilateral simultaneous sequential single-incision video-assisted thoracic surgery in multiple nodules both lungs: a single-center experience of 10 cases

Objective

To discuss the application of bilateral simultaneous sequential single-incision video-assisted thoracic surgery in multiple nodules in both lungs.

Methods

A retrospective analysis of 10 patients in Zhengzhou People’s Hospital who underwent single-incision thoracoscopic surgery to treat multiple nodules in both lungs at the same time from September 2019 to January 2021, and analyze the perioperative indicators (general condition, smoking history, family history, follow-up time of pulmonary nodules, size, location, height and weight, pulmonary function, intraoperative blood loss, operation time, color and volume of drainage fluid, catheterization time, perioperative complications, length of stay, pathology, patient satisfaction, etc.).

Results

All 10 patients used single-incision thoracoscopy to complete bilateral simultaneous sequential operations, aged 32 to 70 years, 8 female patients, 2 male patients, preoperative follow-up time ranging from 1 day to 2 years, a total of 23 lung nodules were removed except for the benign lesions in one nodule in the 2 patients, the other nodules were tumorous lesions (91.3%). The average total hospital stay was 10.5 days (8–14 days), and the average operation time was 194.5 min (145–292 min). The blood loss ranged from 10 to 280 ml, all patients had no serious complications during the perioperative period, and they recovered well and were discharged smoothly, and the satisfaction reached 100%.

Conclusion

Single-incision bilateral simultaneous sequential thoracoscopy have certain advantages in the treatment of patients with multiple nodules in both lungs, conforms to the concept of rapid recovery, and is a feasible choice in the shared decision making of doctors and patients.

Outcome of Resected Lung Cancers with Subcentimeter Solid Component on Computed Tomography

Background Although the opportunity to treat subcentimeter lung cancers has increased, the optimal surgical methods remain unclear. We performed a retrospective study to examine the clinical outcome of subcentimeter lung cancers.

Patients and Methods In total, 118 patients who underwent curative resection for subcentimeter lung cancer from January 2005 to December 2013 were analyzed. Multivariate Cox proportional hazards models were used to calculate the hazard ratio to identify independent predictors of recurrence-free survival (RFS) and overall survival (OS).

Results Anatomical resections were performed for 64 patients (59 lobectomies and 5 segmentectomies) and wedge resections for 54 patients. Recurrence developed in six patients who had consolidation-predominant tumors (consolidation/tumor [C/T] ratio of >0.5) and underwent wedge resections. The first recurrence patterns were regional recurrences in three patients, both regional and distant in one, and distant in two. Seventeen patients died of other causes. The multivariate analysis revealed that the C/T ratio was the independent predictor of RFS (p = 0.008) and OS (p = 0.011).

Conclusion Patients with subcentimeter lung cancer rarely developed recurrence. The C/T ratio was the independent prognostic factor, and all relapsed patients received wedge resections. Even for subcentimeter lung cancers, we should select the extent of pulmonary resection after thoroughly considering whether wedge resection (less invasiveness) is a reasonable alternative to anatomical resection (superior oncologic efficacy) considering the C/T ratio of the lesion.

Prognostic significance of postoperative longitudinal change of serum carcinoembryonic antigen level in patients with stage I lung adenocarcinoma completely resected by single-port video-assisted thoracic surgery: a retrospective study

Background

Patients with stage I lung adenocarcinoma (LUAD) have varying postoperative prognosis. This study aimed to investigate the prognostic significance of postoperative longitudinal change of serum carcinoembryonic antigen (CEA) level in patients with stage I LUAD.

Methods

The study cohort comprised 241 patients with stage I LUAD completely resected with single-port video-assisted thoracic surgery (VATS). The patients were categorized into 4 groups according to the postoperative longitudinal change of serum CEA levels measured in the third and sixth months after surgery: the NN group (continuously normal), HN group (increase first and then decrease), NH group (decrease first and then increase), and HH group (continuously high). Recurrence-free survival (RFS) was analyzed by the Kaplan-Meier method and compared by log-rank test. A nomogram was developed to predict recurrence in the stage I LUAD patients.

Results

In univariate analysis, differentiation (P<0.001), visceral pleural invasion (VPI) (P=0.025), tumor diameter (P<0.001), tumor-node-metastasis (TNM) stage (P=0.008), preoperative CEA levels (≥10.0 vs. <10.0 ng/mL, P<0.001), and postoperative CEA grouping (NH/HH vs. NN/HN, P<0.001) were significant prognostic factors for stage I LUAD patients. Multivariate analysis showed that tumor diameter (P=0.009) and postoperative CEA grouping (P<0.001) were considered to be independent prognostic factors of postoperative recurrence of stage I LUAD. Tumor diameter (≥20 mm) and postoperative CEA (NH/HH vs. NN/HN) were associated with worse RFS. Receiver operating characteristic (ROC) curve analysis showed that postoperative CEA (NH/HH vs. NN/HN) have high sensitivity (64.7%) and specificity (83.2%) for early prediction of postoperative recurrence of stage I LUAD. The area under curve (AUC) value was 0.745. The nomogram based on multivariate Cox regression had a concordance index (value of 0.789). The calibration plot showed that the predicted probabilities closely matched the observed probabilities.

Conclusions

Longitudinal change in serum CEA level after surgery was found to be an independent unfavorable prognostic factor in completely resected stage I LUAD patients. The NH group and HH group were significantly associated with worse RFS. A nomogram was established to predict the postoperative recurrence of patients with stage I LUAD.

Prognostic value of ground glass opacity on computed tomography in pathological stage I pulmonary adenocarcinoma: A meta-analysis

BACKGROUND

The clinical role of ground glass opacity (GGO) on computed tomography (CT) in stage I pulmonary adenocarcinoma patients currently remains unclear.

AIM

To explore the prognostic value of GGO on CT in lung adenocarcinoma patients who were pathologically diagnosed with tumor-node-metastasis stage I.

METHODS

A comprehensive and systematic search was conducted through the PubMed, EMBASE and Web of Science databases up to April 3, 2021. The hazard ratio (HR) and corresponding 95% confidence interval (CI) were combined to assess the association between the presence of GGO and prognosis, representing overall survival and disease-free survival. Subgroup analysis based on the ratio of GGO was also conducted. STATA 12.0 software was used for statistical analysis.

RESULTS

A total of 12 studies involving 4467 patients were included. The pooled results indicated that the GGO predicted favorable overall survival (HR = 0.44, 95%CI: 0.34-0.59, P < 0.001) and disease-free survival (HR = 0.35, 95%CI: 0.18-0.70, P = 0.003). Subgroup analysis based on the ratio of GGO further demonstrated that the proportion of GGO was a good prognostic indicator in pathological stage I pulmonary adenocarcinoma patients, and patients with a higher ratio of GGO showed better prognosis than patients with a lower GGO ratio did.

CONCLUSION

This meta-analysis manifested that the presence of GGO on CT predicted favorable prognosis in tumor-node-metastasis stage I lung adenocarcinoma. Patients with a higher GGO ratio were more likely to have a better prognosis than patients with a lower GGO ratio.