Tumor heterogeneity, hypoxia, and immune markers in surgically resected non-small-cell lung cancer

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?

Objectives: Radiomics and machine learning are innovative approaches to improve the clinical management of NSCLC. However, there is less information about the additive value of FDG PET-based radiomics compared with clinical and imaging variables. Methods: This retrospective study included 320 NSCLC patients who underwent PET/CT with FDG at initial staging. VOIs were placed on primary tumors only. We included a total of 94 variables, including 87 textural features extracted from PET studies, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. We used the least absolute shrinkage and selection operator (LASSO) regression to select variables with the highest predictive value. Although several radiomics variables are available, the added value of these predictors compared with clinical and imaging variables is still under evaluation. Three hundred and twenty NSCLC patients were included in this retrospective study and underwent 18F-FDG PET/CT at initial staging. In this study, we evaluated 94 variables, including 87 textural features, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. Image-based predictors were extracted from a volume of interest (VOI) positioned on the primary tumor. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to reduce the number of variables and select only those with the highest predictive value. The predictive model implemented with the variables selected using the LASSO analysis was compared with a reference model using only a tumor stage and SUVmax. Results: NGTDM coarseness, SUVmax, and TNM stage survived the LASSO analysis and were used for the radiomic model. The AUCs obtained from the reference and radiomic models were 80.82 (95%CI, 69.01-92.63) and 81.02 (95%CI, 69.07-92.97), respectively (p = 0.98). The median OS in the reference model was 17.0 months in high-risk patients (95%CI, 11-21) and 113 months in low-risk patients (HR 7.47, p < 0.001). In the radiomic model, the median OS was 16.5 months (95%CI, 11-20) and 113 months in high- and low-risk groups, respectively (HR 9.64, p < 0.001). Conclusions: Our results indicate that a radiomic model composed using the tumor stage, SUVmax, and a selected radiomic feature (NGTDM_Coarseness) predicts survival in NSCLC patients similarly to a reference model composed only by the tumor stage and SUVmax. Replication of these preliminary results is necessary.

Predictive value of infiltrating tumor border configuration of rectal cancer on MRI

BACKGROUND

Infiltrating tumor border configuration (iTBC) is assessed by postoperative pathological examination, thus, is not helpful for preoperative treatment strategies. The study aimed to detect iTBC by magnetic resonance imaging (MRI) and evaluate its predictive value.

MATERIALS AND METHODS

A total of 153 patients with rectal cancer were retrospectively analyzed. Clinicopathological and MRI data mainly including tumor border configuration (TBC) on MRI, MRI-detected extramural vascular invasion (MEMVI), tumor length, tumor growth pattern, maximal extramural depth, pathology-proven lymph node metastasis (PLN) and pathology-proven extramural vascular invasion (PEMVI) were analyzed. The correlation of MRI factors with PEMVI and PLN was analyzed by univariate and multivariate logistic regression analyses. The nomograms were established based on multivariate logistic regression analysis and were confirmed by Bootstrap self-sampling. The receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to evaluate the diagnostic efficiency.

RESULTS

Fifty cases of PEMVI and 48 cases of PLN were found. Forty cases of PEMVI and 34 cases of PLN in 62 cases of iTBC were also found. iTBC, MEMVI and maximal extramural depth were significantly associated with PEMVI and PLN (P < 0.05). iTBC (odds ratio = 3.84 and 3.02) and MEMVI (odds ratio = 7.27 and 3.22) were independent risk factors for PEMVI and PLN. The C-indices of the two nomograms for predicting PEMVI and PLN were 0.863 and 0.752, respectively. The calibration curves and ROC curves of the two nomograms showed that the correlation between the predicted and the actual incidence of PEMVI and PLN was good. The AUCs of iTBC for predicting PEMVI and PLN were 0.793 (95% CI: 0.714-0.872) and 0.721 (95% CI: 0.632-0.810), respectively. The DeLong test showed that the predictive efficiency of the nomogram in predicting PEMVI was better than that of iTBC (P = 0.0009) and MEMVI (P = 0.0095).

CONCLUSION

iTBC and MEMVI are risk factors for PEMVI and pelvic lymph node metastasis. The nomograms based on iTBC show a good performance in predicting PEMVI and pelvic lymph node metastasis, possessing a certain clinical reference value.

TRIAL REGISTRATION

This study was approved by the Ethics Committee of Beijing Friendship Hospital, and individual consent was waived for this retrospective analysis.

Predictive value of lesion morphology in rectal cancer based on MRI before surgery

OBJECTIVE

To explore the relationship of MRI morphology of primary rectal cancer with extramural vascular invasion (EMVI), metastasis and local recurrence.

MATERIALS AND METHODS

This retrospective study included 153 patients with rectal cancer. Imaging factors and histopathological index including nodular projection (NP), cord sign (CS) at primary tumor margin, irregular nodules (IN) of mesorectum, MRI-detected peritoneal reflection invasion (PRI), range of rectal wall invasion (RRWI), patterns and length of tumor growth, maximal extramural depth (EMD), histologically confirmed local node involvement (hLN), MRI T stage, MRI N stage, MRI-detected extramural vascular invasion (mEMVI) and histologically confirmed extramural vascular invasion (hEMVI) were evaluated. Determining the relationship between imaging factors and hEMVI, synchronous metastasis and local recurrence by univariate analysis and multivariable logistic regression, and a nomogram validated internally via Bootstrap self-sampling was constructed based on the latter.

RESULTS

Thirty-eight cases of hEMVI, fourteen cases of synchronous metastasis and ten cases of local recurrence were observed among 52 NP cases. There were 50 cases of mEMVI with moderate consistency with hEMVI (Kappa = 0.614). NP, CS, EMD and mEMVI showed statistically significant differences in the negative and positive groups of hEMVI, synchronous metastasis, and local recurrence. Compared to patients with local mass growth, the rectal tumor with circular infiltration had been found to be at higher risk of synchronous metastasis and local recurrence (P < 0.05). NP and IN remained as significant predictors for hEMVI, and mEMVI was a predictor for synchronous metastasis, while PRI and mEMVI were predictors for local recurrences. The nomogram for predicting hEMVI demonstrated a C-index of 0.868, sensitivity of 86.0%, specificity of 79.6%, and accuracy of 81.7%.

CONCLUSION

NP, CS, IN, large EMD, mEMVI, and circular infiltration are significantly associated with several adverse prognostic indicators. The nomogram based on NP has good predictive performance for preoperative EMVI. mEMVI is a risk factor for synchronous metastasis. PRI and mEMVI are risk factors for local recurrence.

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

Tumor infiltrating lymphocytes and radiological picture of the tumor

Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.

Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study

The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.

Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma

Background

High levels of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of ¹⁸F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.

Results

Using an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In ¹⁸F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan–Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower ¹⁸F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P = .001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P = .027). A high TLR (P = .01) score of ¹⁸F-FDG PET parameters and a high T follicular helper cell (TFH) score (P = .005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P = .024 and .047).

Conclusions

A high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00908-9.

Hypoxia in Lung Cancer Management: A Translational Approach

Simple Summary

Hypoxia is a common feature of lung cancers. Nonetheless, no guidelines have been established to integrate hypoxia-associated biomarkers in patient management. Here, we discuss the current knowledge and provide translational novel considerations regarding its clinical detection and targeting to improve the outcome of patients with non-small-cell lung carcinoma of all stages.

Abstract

Lung cancer represents the first cause of death by cancer worldwide and remains a challenging public health issue. Hypoxia, as a relevant biomarker, has raised high expectations for clinical practice. Here, we review clinical and pathological features related to hypoxic lung tumours. Secondly, we expound on the main current techniques to evaluate hypoxic status in NSCLC focusing on positive emission tomography. We present existing alternative experimental approaches such as the examination of circulating markers and highlight the interest in non-invasive markers. Finally, we evaluate the relevance of investigating hypoxia in lung cancer management as a companion biomarker at various lung cancer stages. Hypoxia could support the identification of patients with higher risks of NSCLC. Moreover, the presence of hypoxia in treated tumours could help clinicians predict a worse prognosis for patients with resected NSCLC and may help identify patients who would benefit potentially from adjuvant therapies. Globally, the large quantity of translational data incites experimental and clinical studies to implement the characterisation of hypoxia in clinical NSCLC management.

Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Purpose

To investigate the impact of staging on differences in glucose metabolic heterogeneity between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) by ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) textural analysis and to develop a stage-specific PET radiomic prediction model to distinguish lung ADC from SCC.

Patients and Methods

Patients who were histologically diagnosed with lung ADC or SCC and underwent pretreatment ¹⁸F-FDG PET/CT scans were retrospectively identified. Radiomic features were extracted from a semiautomatically outlined tumor region in the Chang-Gung Image Texture Analysis (CGITA) software package. The differences in radiomic parameters between lung ADC and SCC were compared stage-by-stage in 253 consecutive NSCLC patients with stages I to III disease. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. A radiomic signature for each stage was subsequently constructed and evaluated. Then, an individual nomogram incorporating the radiomic signature and clinical risk factors was established and evaluated. The performance of the constructed models was assessed by receiver operating characteristic (ROC) curve analysis, and the nomogram was further validated by calibration curve analysis.

Results

The performance of the radiomic signature for distinguishing lung ADC and SCC in both the training and validation cohorts was good, with AUCs of 0.883, 0.854, and 0.895 in the training cohort and 0.932, 0.944, and 0.886 in the validation cohort for stages I, II, and III NSCLC, respectively. The radiomic-clinical nomogram integrating radiomic features with independent clinical predictors exhibited more favorable discriminative performance, with AUCs of 0.982, 0.963, and 0.979 in the training cohort and 0.989, 0.984, and 0.978 in the validation cohort for stages I, II, and III, respectively.

Conclusion

Differences in PET radiomic features between lung ADC and SCC varied in different stages. Stage-specific PET radiomic prediction models provided more favorable performance for discriminating the histological subtype of NSCLC.

A Role for FDG PET Radiomics in Personalized Medicine?

Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.

Fit-For-Purpose PD-L1 Biomarker Testing For Patient Selection in Immuno-Oncology: Guidelines For Clinical Laboratories From the Canadian Association of Pathologists-Association Canadienne Des Pathologistes (CAP-ACP)

Since 2014, programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) checkpoint inhibitors have been approved by various regulatory agencies for the treatment of multiple cancers including melanoma, lung cancer, urothelial carcinoma, renal cell carcinoma, head and neck cancer, classical Hodgkin lymphoma, colorectal cancer, gastroesophageal cancer, hepatocellular cancer, and other solid tumors. Of these approved drug/disease combinations, a subset also has regulatory agency-approved, commercially available companion/complementary diagnostic assays that were clinically validated using data from their corresponding clinical trials. The objective of this document is to provide evidence-based guidance to assist clinical laboratories in establishing fit-for-purpose PD-L1 biomarker assays that can accurately identify patients with specific tumor types who may respond to specific approved immuno-oncology therapies targeting the PD-1/PD-L1 checkpoint. These recommendations are issued as 38 Guideline Statements that address (i) assay development for surgical pathology and cytopathology specimens, (ii) reporting elements, and (iii) quality assurance (including validation/verification, internal quality assurance, and external quality assurance). The intent of this work is to provide recommendations that are relevant to any tumor type, are universally applicable and can be implemented by any clinical immunohistochemistry laboratory performing predictive PD-L1 immunohistochemistry testing.

18F-FDG maximum standard uptake value predicts PD-L1 expression on tumor cells or tumor-infiltrating immune cells in non-small cell lung cancer

OBJECTIVES

Programmed cell death-ligand 1 (PD-L1) is expressed on tumor cells (TC) and tumor-infiltrating immune cells (IC). We conducted a retrospective study to investigate the relationship between PD-L1 expression on TC/IC and ¹⁸F-FDG uptake in patients with surgically resected non-small cell lung cancer (NSCLC).

METHODS

Total 362 NSCLC patients (297 adenocarcinoma and 65 squamous cell carcinoma) who underwent preoperative ¹⁸F-FDG-PET/CT imaging were analyzed retrospectively. Immunohistochemistry analysis was performed for PD-L1 expression on TC and IC in NSCLC specimens with 28-8 antibody. The cut-off value of 5% for defining PD-L1 positivity was determined according to previous trials. The association between PD-L1 expression and clinicopathological variables were analyzed, including age, gender, smoking status, tumor diameter, lymph node metastasis, stage and the maximum standardized uptake value (SUVmax).

RESULTS

PD-L1 positive expression was 50.8% (184/362) in NSCLC patients. Its positive expression on TC and IC were 24.3% (88/362) and 42.5% (154/362), respectively. SUVmax was significantly higher in patients with PD-L1 positive expression on TC or IC than that with negative. Multivariate analysis demonstrated that PD-L1 expression were correlated with SUVmax. The best cut-off value of SUVmax for PD-L1 expression on TC/IC was 8.5 [area under the curve (AUC) = 0.607, 95% CI 0.549-0.665, P = 0.001, sensitivity 50.5% and specificity 71.4%] determined by ROC curve.

CONCLUSION

High SUVmax is linked to PD-L1 expression on TC and IC in our patients with surgically resected non-small cell lung cancer. ¹⁸F-FDG-PET/CT imaging may be used to predict the PD-L1 expression on TC and IC in NSCLC patients.

Independent expression of circulating and tissue levels of PD-L1: correlation of clusters with tumor metabolism and outcome in patients with non-small cell lung cancer

PURPOSE

To evaluate the clinical-pathological and prognostic significance of the circulating PD-L1 level in patients with surgically treated NSCLC, by combining data for PD-L1 expression with other immune-related markers and tumor metabolism.

METHODS

Overall, 40 patients with resected NSCLC (stage Ia-IIIa) who had preoperative blood storage and underwent staging PET/CT were enrolled for the study. In all cases, we determined plasma levels of PD-L1 (pg/ml), immune-reactive areas (IRA %) covered by CD3, CD68, CD20, CD8, PD-1, and PD-L1 in the tumor specimen, and metabolic parameters on PET, i.e., SUV_max, SUV_peak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Variables were statistically analyzed to establish their association with disease-free survival (DFS).

RESULTS

The circulating levels of PD-L1 in the bloodstream could be determined in 38/40 (95%) samples. The mean and median expression levels were 34.86 pg/ml and 24.83 pg/ml, respectively. We did not find any statistically significant correlation between circulating PD-L1 and tissue expression of PD-L1/PD-1. Some mild degree of positive correlation was determined between tissue PD-L1 and SUV_max (ρ = 0.390; p = 0.0148). Hierarchical clustering combining circulating, tissue, and metabolic parameters identified clusters with high metabolic tumor burden or high expression of plasma PD-L1 levels (Z score ≥ 2) as having a poor DFS (p = 0.033). The multivariate analysis detected stage and metabolism (i.e., SUV_max and SUV_peak) as independent prognostic factors for DFS.

CONCLUSION

Plasma levels of PD-L1 are independent of the expression of PD-1/PD-L1 in NSCLC tumor tissue and, when combined with other clinical-pathological parameters, allow for the identification of clusters with different outcomes.

Opportunistic dose amplification for proton and carbon ion therapy via capture of internally generated thermal neutrons

This paper presents Neutron Capture Enhanced Particle Therapy (NCEPT), a method for enhancing the radiation dose delivered to a tumour relative to surrounding healthy tissues during proton and carbon ion therapy by capturing thermal neutrons produced inside the treatment volume during irradiation. NCEPT utilises extant and in-development boron-10 and gadolinium-157-based drugs from the related field of neutron capture therapy. Using Monte Carlo simulations, we demonstrate that a typical proton or carbon ion therapy treatment plan generates an approximately uniform thermal neutron field within the target volume, centred around the beam path. The tissue concentrations of neutron capture agents required to obtain an arbitrary 10% increase in biological effective dose are estimated for realistic treatment plans, and compared to concentrations previously reported in the literature. We conclude that the proposed method is theoretically feasible, and can provide a worthwhile improvement in the dose delivered to the tumour relative to healthy tissue with readily achievable concentrations of neutron capture enhancement drugs.

MiR-873 inhibition enhances gefitinib resistance in non-small cell lung cancer cells by targeting glioma-associated oncogene homolog 1

BACKGROUND

The five-year survival rate of non-small cell lung cancer (NSCLC) patients is very low. MiR-873 is involved in the growth, metastasis, and differentiation of tumors. Herein, we determined the target gene and influence of miR-873 in NSCLC.

METHODS

MiRanda and Targetscan websites were used to predict the target gene of miR-873 in NSCLC. Luciferase activity was examined using a dual luciferase reporter gene assay kit. The viability, tube formation, and proliferation of cells were analyzed by cell counting kit-8, angiogenic analysis, and flow cytometry, respectively. The levels of miR-873 and GLI1 were evaluated using quantitative real-time PCR and Western blot assays.

RESULTS

Low levels of GLI1 and high levels of miR-873 were observed in an NSCLC cell line (PC9) highly sensitive to EGFR-tyrosine kinase inhibitors. There was a negative correlation between miR-873 and GLI1 expression in PC9 and PC9/GR cells. The inhibition of miR-873 enhanced GLI1 levels. MiR-873 expression was inhibited by gefitinib. Gefitinib markedly reduced the viability, tube formation, and cell number in PC9 cells. However, suppression of miR-873 enhanced the resistance and knockdown of GLI1 enhanced the sensitivity of PC9 cells to gefitinib.

CONCLUSIONS

GLI1 is a target gene of miR-873 in NSCLC. The inhibition of miR-873 increased gefitinib resistance of NSCLC cells via the upregulation of GLI1. These results indicate that miR-873-GLI1 signaling is involved in gefitinib resistance in NSCLC.