Differential Relapse Patterns for Non-small Cell Lung Cancer Subtypes Adenocarcinoma and Squamous Cell Carcinoma: Implications for Radiation Oncology

Effect of radiotherapy on local control and overall survival in spinal metastasis of non-small-cell lung cancer after surgery and systemic therapy

Aims

Radiotherapy is a well-known local treatment for spinal metastases. However, in the presence of postoperative systemic therapy, the efficacy of radiotherapy on local control (LC) and overall survival (OS) in patients with spinal metastases remains unknown. This study aimed to evaluate the clinical outcomes of post-surgical radiotherapy for spinal metastatic non-small-cell lung cancer (NSCLC) patients, and to identify factors correlated with LC and OS.

Methods

A retrospective, single-centre review was conducted of patients with spinal metastases from NSCLC who underwent surgery followed by systemic therapy at our institution from January 2018 to September 2022. Kaplan-Meier analysis and log-rank tests were used to compare the LC and OS between groups. Associated factors for LC and OS were assessed using Cox proportional hazards regression analysis.

Results

Overall, 123 patients with 127 spinal metastases from NSCLC who underwent decompression surgery followed by postoperative systemic therapy were included. A total of 43 lesions were treated with stereotactic body radiotherapy (SBRT) after surgery and 84 lesions were not. Survival rate at one, two, and three years was 83.4%, 58.9%, and 48.2%, respectively, and LC rate was 87.8%, 78.8%, and 78.8%, respectively. Histological type was the only significant associated factor for both LC (p = 0.007) and OS (p < 0.001). Treatment with targeted therapy was significantly associated with longer survival (p = 0.039). The risk factors associated with worse survival were abnormal laboratory data (p = 0.021), lesions located in the thoracic spine (p = 0.047), and lumbar spine (p = 0.044). This study also revealed that postoperative radiotherapy had little effect in improving OS or LC.

Conclusion

Tumour histological type was significantly associated with the prognosis in spinal NSCLC metastasis patients. In the presence of post-surgical systemic therapy, radiotherapy appeared to be less effective in improving LC, OS, or quality of life in spinal NSCLC metastasis patients.

Differences of genomic alterations and heavy metals in non-small cell lung cancer with different histological subtypes

PURPOSE

This study aimed to explore the correlations among heavy metals concentration, histologic subtypes and molecular characteristics in patients with non-small cell lung cancer (NSCLC).

METHODS

In this study, an NGS panel of 82 tumor-associated genes was used to identify genomic alternations in 180 newly diagnosed patients with NSCLC. The concentrations of 18 heavy metals in the serum samples were detected by inductively coupled plasma emission spectrometry (ICP-MS).

RESULTS

A total of 243 somatic mutations of 25 mutant genes were identified in 115 of 148 patients with LUAD and 45 somatic mutations of 15 mutant genes were found in 24 of 32 patients with LUSC. The genomic alternations, somatic interactions, traditional serum biomarkers, and heavy metals were markedly different between patients with LUAD and LUSC. Moreover, patients with LUSC were significantly positively correlated with Ba, but not LUAD. Lastly, patients with EGFR mutations presented significant negative correlations with Cd and Sr, whereas patients with TP53 mutations showed a significant positive correlation with Pb.

CONCLUSION

The genomic alternations, somatic interactions, traditional serum biomarkers, and heavy metals were different between patients with LUAC and LUSC, and heavy metals (e.g., Ba, Pb, and Cd) may contribute to the tumorigenesis of NSCLC with different histological and molecular subtypes.

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.

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung

Objective

Based on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC).

Methods

A total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility.

Results

A total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility.

Conclusion

The combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.

Distinct Prognostic Factors of Ground Glass Opacity and Pure-Solid Lesion in Pathological Stage I Invasive Lung Adenocarcinoma

BACKGROUND

Ground glass opacity (GGO) is associated with favorable survival in lung cancer. However, the relevant evidence of the difference in prognostic factors between GGO and pure-solid nodules for pathological stage I invasive adenocarcinoma (IAC) is limited. We aimed to identify the impact of GGO on survival and find prognostic factor for part-GGO and pure-solid patients.

METHODS

Between December 2007 and August 2018, patients with pathological stage I IAC were retrospectively reviewed and categorized into the pure-GGO, part-GGO, and pure-solid groups. Survival curves were analyzed by the Kaplan-Meier method and compared by log-rank tests. Least absolute shrinkage and selection operator and Cox regression models were used to obtained prognostic factors for disease-free survival (DFS) and overall survival (OS).

RESULTS

The number of patients with pure-GGO, part-GGO, and pure-solid was 134, 540, and 396, respectively. Part-GGO patients with consolidation-tumor-ratio (CTR) > 0.75 had similar outcome to those with pure-solid nodules. In part-GGO patients, CTR was negatively associated with OS (P = 0.007) and solid tumor size (STS) was negatively associated with DFS (P < 0.001). Visceral pleural invasion (VPI) was negatively associated with OS (P = 0.040) and DFS (P = 0.002). Sublobectomy was negatively associated with OS (P = 0.008) and DFS (P = 0.005), while extended N1 stations examination was associated with improved DFS (P = 0.005) in pure-solid patients.

CONCLUSIONS

Though GGO component is a positively prognostic factors of patients with pathological stage I IAC, a small proportion of GGO components is not associated with favorable survival. VPI, STS and CTR are the significant predictors for part-GGO patients. Sublobectomy, especially wedge resection should be used cautiously in pure-solid patients.

SETD2 regulates gene transcription patterns and is associated with radiosensitivity in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has high morbidity and mortality worldwide, and its prognosis remains unsatisfactory. Identification of epigenetic biomarkers associated with radiosensitivity is beneficial for precision medicine in LUAD patients. SETD2 is important in repairing DNA double-strand breaks and maintaining chromatin integrity. Our studies established a comprehensive analysis pipeline, which identified SETD2 as a radiosensitivity signature. Multi-omics analysis revealed enhanced chromatin accessibility and gene transcription by SETD2. In both LUAD bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq), we found that SETD2-associated positive transcription patterns were associated with DNA damage responses. SETD2 knockdown significantly upregulated tumor cell apoptosis, attenuated proliferation and migration of LUAD tumor cells, and enhanced radiosensitivity in vitro. Moreover, SETD2 was a favorably prognostic factor whose effects were antagonized by the m6A-related genes RBM15 and YTHDF3 in LUAD. In brief, SETD2 was a promising epigenetic biomarker in LUAD patients.

MRI-based radiomics analysis in differentiating solid non-small-cell from small-cell lung carcinoma: a pilot study

AIM

To investigate the ability of a T2-weighted (W) magnetic resonance imaging (MRI)-based radiomics signature to differentiate solid non-small-cell lung carcinoma (NSCLC) from small-cell lung carcinoma (SCLC).

MATERIALS AND METHODS

The present retrospective study enrolled 152 eligible patients (NSCLC = 125, SCLC = 27). All patients underwent MRI using a 3 T scanner and radiomics features were extracted from T2W MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression model was used to identify the optimal radiomics features for the construction of a radiomics model to differentiate solid NSCLC from SCLC. Threefold cross validation repeated 10 times was used for model training and evaluation. The conventional MRI morphology features of the lesions were also evaluated. The performance of the conventional MRI morphological features, and the radiomics signature model and nomogram model (combining radiomics signature with conventional MRI morphological features) was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Five optimal features were chosen to build a radiomics signature. There was no significant difference in age, gender, and the largest diameter. The radiomics signature and conventional MRI morphological features (only pleural indentation and lymph node enlargement) were independent predictive factors for differentiating solid NSCLC from SCLC. The area under the ROC curves (AUCs) for MRI morphological features, and the radiomics model, and nomogram model was 0.69, 0.85, and 0.90 (ROC), respectively.

CONCLUSIONS

The T2W MRI-based radiomics signature is a potential non-invasive approach for distinguishing solid NSCLC from SCLC.

Radiomics Nomogram for Predicting Locoregional Failure in Locally Advanced Non-small Cell Lung Cancer Treated with Definitive Chemoradiotherapy

RATIONALE AND OBJECTIVES

To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting locoregional failure (LRF) in patients with locally advanced non-small cell lung cancer (NSCLC) treated with definitive chemoradiotherapy (CRT).

MATERIALS AND METHODS

A total of 141 patients with locally advanced NSCLC treated with definitive CRT from January 2014 to December 2017 were included and divided into testing cohort (n = 100) and validation (n = 41) cohort. Radiomics features were extracted from pretreatment contrast enhanced CT. The least absolute shrinkage and selection operator logistic regression was processed to select predictive features from the testing cohort and constructed a radiomics signature. Clinical characteristics and the radiomics signature were analyzed using univariable and multivariate Cox regression. The radiomics nomogram was established with the radiomics signature and independent clinical factors. Harrell's C-index, calibration curves and decision curves were used to assess the performance of the radiomics nomogram.

RESULTS

The radiomics signature, which consisted of eight selected features, was an independent factor of LRF. The clinical predictors of LRF were the histologic type and clinical stage. The radiomics nomogram combined with the radiomics signature and clinical prognostic factors showed good performance with C-indexes of 0.796 (95% confidence interval [CI]: 0.709-0.883) and 0.756 (95% CI: 0.674-0.838) in the testing and validation cohorts respectively. Additionally, the combined nomogram resulted in better performance (p < 0.001) for the estimation of LRF than the nomograms with the radiomics signature (C-index: 0.776; 95% CI: 0.686-0.866) or clinical predictors (C-index: 0.641; 95% CI: 0.542-0.740) alone.

CONCLUSION

The radiomics nomogram provided the best performance for LRF prediction in patients with locally advanced NSCLC, which may help optimize individual treatments.

Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review

Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.

A Combined RNA Signature Predicts Recurrence Risk of Stage I-IIIA Lung Squamous Cell Carcinoma

Objective

Recurrence remains the main cause of the poor prognosis in stage I-IIIA lung squamous cell carcinoma (LUSC) after surgical resection. In the present study, we aimed to identify the long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) related to the recurrence of stage I-IIIA LUSC. Moreover, we constructed a risk assessment model to predict the recurrence of LUSC patients.

Methods

RNA sequencing data (including miRNAs, lncRNAs, and mRNAs) and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed lncRNAs, miRNAs, and mRNAs were identified using the “DESeq2” package of the R language. Univariate Cox proportional hazards regression analysis and Kaplan-Meier curve were used to identify recurrence-related genes. Stepwise multivariate Cox regression analysis was carried out to establish a risk model for predicting recurrence in the training cohort. Moreover, Kaplan-Meier curves and receiver operating characteristic (ROC) curves were adopted to examine the predictive performance of the signature in the training cohort, validation cohort, and entire cohort.

Results

Based on the TCGA database, we analyzed the differentially expressed genes (DEGs) among 27 patients with recurrent stage I-IIIA LUSC and 134 patients with non-recurrent stage I-IIIA LUSC, and identified 431 lncRNAs, 36 miRNAs, and 746 mRNAs with different expression levels. Out of these DEGs, the optimal combination of DEGs was finally determined, and a nine-joint RNA molecular signature was constructed for clinical prediction of recurrence, including LINC02683, AC244517.5, LINC02418, LINC01322, AC011468.3, hsa-mir-6825, AC020637.1, AC027117.2, and SERPINB12. The ROC curve proved that the model had good predictive performance in predicting recurrence. The area under the curve (AUC) of the prognostic model for recurrence-free survival (RFS) was 0.989 at 3 years and 0.958 at 5 years (in the training set). The combined RNA signature also revealed good predictive performance in predicting the recurrence in the validation cohort and entire cohort.

Conclusions

In the present study, we constructed a nine-joint RNA molecular signature for recurrence prediction of stage I-IIIA LUSC. Collectively, our findings provided new and valuable clinical evidence for predicting the recurrence and targeted treatment of stage I-IIIA LUSC.

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

PURPOSE

To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC).

METHODS

A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots.

RESULTS

In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900-0.964), 0.901 (0.840-0.957), respectively) (p < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions.

CONCLUSION

This study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

Advanced pneumonic type of lung adenocarcinoma: survival predictors and treatment efficacy of the tumor

PURPOSE

To retrospectively explore the survival predictors and treatment efficacy of advanced pneumonic-type lung adenocarcinoma (P-ADC).

METHODS

Retrospective analysis of clinical data and survival follow-up was undertaken on 41 patients with advanced P-ADC from January 1, 2009, to April 30, 2019. Analysis on tumor biomarkers such as carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and the cytokeratin-19-fragment (Cyfra21-1) were undertaken. The patients in this study were divided into three groups based on usage of tyrosine kinase inhibitor (TKI): TKI therapy group (including combination with chemotherapy), non-TKI therapy group (chemotherapy alone), and palliative care group.

RESULTS

More than half of the patients had higher levels of tumor biomarkers and the incidence of NSE was highest (81.8%), followed by CEA (74.4%) and Cyfra21-1 (74.1%). All patients had abnormal findings on chest computed tomography and with adenocarcinoma pathology. The overall survival (OS) time was 10.4 months in TKI group, 8.8 months in the non-TKI group, and 2.1 months in the palliative care group. Patients with higher level of serum Cyfra21-1 had insignificantly shorter survival time compared to those with normal Cyfra21-1 (p = 0.067). TKI therapy and non-TKI therapy provided a better prognosis prediction compared to palliative care. TKI therapy improved prognosis compared to non-TKI therapy. The comprehensive based TKI therapy provided improved OS vs the non-TKI therapy.

CONCLUSION

TKI-based therapy could improve the prognosis and OS for advanced P-ADC. This study recommends the analysis of EGFR mutations for all patients with advanced P-ADC.