Predicting Outcomes From Radical Radiotherapy for Non-small Cell Lung Cancer: A Systematic Review of the Existing Literature

Predictors of Early Hospice or Death in Patients With Inoperable Lung Cancer Treated With Curative Intent

INTRODUCTION

Treatment for inoperable stage II to III non-small cell lung cancer (NSCLC) involves chemo-radiotherapy (CRT). However, some patients transition to hospice or die early during their treatment course. We present a model to prognosticate early poor outcomes in NSCLC patients treated with curative-intent CRT.

METHODS AND MATERIALS

Across a statewide consortium, data was prospectively collected on stage II to III NSCLC patients who received CRT between 2012 and 2019. Early poor outcomes included hospice enrollment or death within 3 months of completing CRT. Logistic regression models were used to assess predictors in prognostic models. LASSO regression with multiple imputation were used to build a final multivariate model, accounting for missing covariates.

RESULTS

Of the 2267 included patients, 128 experienced early poor outcomes. Mean age was 71 years and 59% received concurrent chemotherapy. The best predictive model, created parsimoniously from statistically significant univariate predictors, included age, ECOG, planning target volume (PTV), mean heart dose, pretreatment lack of energy, and cough. The estimated area under the ROC curve for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%.

CONCLUSIONS

This multivariate model identified a combination of clinical variables and patient reported factors that may identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at higher risk for early poor outcomes.

Therapeutic advances in non-small cell lung cancer: Focus on clinical development of targeted therapy and immunotherapy

Lung cancer still contributes to nearly one-quarter cancer-related deaths in the past decades, despite the rapid development of targeted therapy and immunotherapy in non-small cell lung cancer (NSCLC). The development and availability of comprehensive genomic profiling make the classification of NSCLC more precise and personalized. Most treatment decisions of advanced-stage NSCLC have been made based on the genetic features and PD-L1 expression of patients. For the past 2 years, more than 10 therapeutic strategies have been approved as first-line treatment for certain subgroups of NSCLC. However, some major challenges remain, including drug resistance and low rate of overall survival. Therefore, we discuss and review the therapeutic strategies of NSCLC, and focus on the development of targeted therapy and immunotherapy in advanced-stage NSCLC. Based on the latest guidelines, we provide an updated summary on the standard treatment for NSCLC. At last, we discussed several potential therapies for NSCLC. The development of new drugs and combination therapies both provide promising therapeutic effects on NSCLC.

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.

In Silico Ventilation Within the Dose-Volume is Predictive of Lung Function Post-radiation Therapy in Patients with Lung Cancer

Lung cancer is a leading cause of death worldwide. Radiation therapy (RT) is one method to treat this disease. A common side effect of RT for lung cancer is radiation-induced lung damage (RILD) which leads to loss of lung function. RILD often compounds pre-existing smoking-related regional lung function impairment. It is difficult to predict patient outcomes due to large variability in individual response to RT. In this study, the capability of image-based modelling of regional ventilation in lung cancer patients to predict lung function post-RT was investigated. Twenty-five patient-based models were created using CT images to define the airway geometry, size and location of tumour, and distribution of emphysema. Simulated ventilation within the 20 Gy isodose volume showed a statistically significant negative correlation with the change in forced expiratory volume in 1 s 12-months post-RT (p = 0.001, R = - 0.61). Patients with higher simulated ventilation within the 20 Gy isodose volume had a greater loss in lung function post-RT and vice versa. This relationship was only evident with the combined impact of tumour and emphysema, with the location of the emphysema relative to the dose-volume being important. Our results suggest that model-based ventilation measures can be used in the prediction of patient lung function post-RT.

Markers Useful in Monitoring Radiation-Induced Lung Injury in Lung Cancer Patients: A Review

In 2018, lung cancer was the most common cancer and the most common cause of cancer death, accounting for a 1.76 million deaths. Radiotherapy (RT) is a widely used and effective non-surgical cancer treatment that induces remission in, and even cures, patients with lung cancer. However, RT faces some restrictions linked to the radioresistance and treatment toxicity, manifesting in radiation-induced lung injury (RILI). About 30–40% of lung cancer patients will develop RILI, which next to the local recurrence and distant metastasis is a substantial challenge to the successful management of lung cancer treatment. These data indicate an urgent need of looking for novel, precise biomarkers of individual response and risk of side effects in the course of RT. The aim of this review was to summarize both preclinical and clinical approaches in RILI monitoring that could be brought into clinical practice. Next to transforming growth factor-β1 (TGFβ1) that was reported as one of the most important growth factors expressed in the tissues after ionizing radiation (IR), there is a group of novel, potential biomarkers—microRNAs—that may be used as predictive biomarkers in therapy response and disease prognosis.

An investigative expansion of a competing risk model for first failure site in locally advanced non-small cell lung cancer

Introduction: We hypothesized that gross tumor volume (GTV) of primary tumor (GTV_T) and nodal volumes (GTV_N) were predictors of first failure site in non-small cell lung cancer (NSCLC). We aimed at also comparing the prognostic model's complexity to its ability to generate absolute risk predictions with emphasis on variables available at the time of diagnosis. Materials and methods: Three hundred and forty-two patients treated with definitive chemoradiotherapy (CRT) for adenocarcinoma (AC) or squamous cell carcinoma (SCC) in 2009-2017 were analyzed. Clinical data, standardized uptake values on FDG-PET/CT, GTV_T and GTV_N were analyzed using multivariate competing risk models. Results: One hundred and thirty-seven patients had SCC. As first site of failure 49 had locoregional failure (LRF), 40 had distant metastasis (DM) and 24 died with no evidence of disease (NED). In 205 patients with AC, 34 had LRF, 118 had DM as first failure site and 17 died with NED. Performance status predicted LRF (p = .02) and UICC stage risk of DM (p = .05 for stage 3, p < .001 for stage 4). Adding histopathology changed predictions with much reduced risk of LRF in AC compared to SCC (HR = 0.5, 95% CI: (0.3-0.75), p = .001). Conversely, AC had a higher rate of DM than SCC (HR = 2.1, 95% CI: (1.5-3.0], p < .001). Addition of FDG metrics and tumor/nodal volume data predicted DM risk (p = .001), but with smaller impact on absolute risk compared to histopathology. Separation of GTV in nodal and tumor lesions did not improve risk predictions. Conclusions: We quantified the effect of adding volumetric and quantitative imaging to competing risk models of first failure site, but did not find tumor volume components to be important. Histopathology remains the simplest and most important factor in prognosticating failure patterns in NSCLC.

Combining Radiotherapy and Immunotherapy in Lung Cancer: Can We Expect Limitations Due to Altered Normal Tissue Toxicity?

In recent decades, technical advances in surgery and radiotherapy, as well as breakthroughs in the knowledge on cancer biology, have helped to substantially improve the standard of cancer care with respect to overall response rates, progression-free survival, and the quality of life of cancer patients. In this context, immunotherapy is thought to have revolutionized the standard of care for cancer patients in the long term. For example, immunotherapy approaches such as immune checkpoint blockade are currently increasingly being used in cancer treatment, either alone or in combination with chemotherapy or radiotherapy, and there is hope from the first clinical trials that the appropriate integration of immunotherapy into standard care will raise the success rates of cancer therapy to a new level. Nevertheless, successful cancer therapy remains a major challenge, particularly in tumors with either pronounced resistance to chemotherapy and radiation treatment, a high risk of normal tissue complications, or both, as in lung cancer. Chemotherapy, radiotherapy and immunotherapy have the capacity to evoke adverse effects in normal tissues when administered alone. However, therapy concepts are usually highly complex, and it is still not clear if combining immunotherapy with radio(chemo)therapy will increase the risk of normal tissue complications, in particular since normal tissue toxicity induced by chemotherapy and radiotherapy can involve immunologic processes. Unfortunately, no reliable biomarkers are available so far that are suited to predict the unique normal tissue sensitivity of a given patient to a given treatment. Consequently, clinical trials combining radiotherapy and immunotherapy are attracting major attention, not only regarding efficacy, but also with regard to safety. In the present review, we summarize the current knowledge of radiation-induced and immunotherapy-induced effects in tumor and normal tissue of the lung, and discuss the potential limitations of combined radio-immunotherapy in lung cancer with a focus on the suspected risk for enhanced acute and chronic normal tissue toxicity.