Change in Apparent Diffusion Coefficient Is Associated With Local Failure After Stereotactic Body Radiation Therapy for Non-Small Cell Lung Cancer: A Prospective Clinical Trial

The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.

A systematic review into the radiological features predicting local recurrence after stereotactic ablative body radiotherapy (SABR) in patients with non-small cell lung cancer (NSCLC): Local recurrence features of NSCLC post-SABR

BACKGROUND AND PURPOSE

Post-treatment surveillance for local recurrence (LR) following SABR can include both fluorodeoxyglucose-positron emission tomography (FDG-PET) and computed tomography (CT). Radiation-induced lung injury (RILI) shares a similar appearance to LR after treatment making the detection of LR on imaging difficult for clinicians. We aimed to summarise radiological features of CT and FDG-PET predicting LR, and to evaluate radiomics as another tool for detecting LR.

METHODS AND MATERIALS

We searched MEDLINE, EMBASE and PubMed databases for published studies and Web of Science, Wiley Online and Science Direct databases for conference abstracts that had patient populations with NSCLC and reported post-SABR radiological features of FDG-PET or CT and radiomics from either FDG-PET or CT. Studies for inclusion were independently reviewed by two authors.

RESULTS

Across 32 relevant studies, the incidence of LR was 13% (222/1726). On CT, certain gross radiological appearances, and kinetic features of changes in size, diameter, volume or 3 consecutive rises in volume of mass-like consolidation are suggestive of LR. Particular regard should be made for the presence of any ≥3 high-risk features (HRF) on CT or the individual HRF of enlarging opacity at ≥12 month's post-SABR as being highly suspicious of LR. On FDG-PET a relative reduction of <5% of SUV_max from baseline in the first 12 months or cut-offs of SUV_max >5 and SUV_mean >3.44 after 12 months can indicate LR. There is limited evidence available to corroborate radiomic features suggestive of LR.

CONCLUSION

This research has identified common features of LR compared to RILI which may aid in early and accurate detection of LR post-SABR; further research is required to validate these findings.

Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy

PURPOSE

To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and posttreatment computed tomography (CT) scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy.

METHODS AND MATERIALS

Two cohorts of patients with ES-NSCLC uniformly treated with SBRT (a median dose of 50 Gy in 4-5 fractions) were studied. Prediction models were built on a discovery cohort of 100 patients with treatment planning CT scans, and then were applied to a separate validation cohort of 60 patients with pre- and posttreatment CT scans for evaluating their performance.

RESULTS

Prediction models achieved a c-index up to 0.734 in predicting survival outcomes of the validation cohort. The integration of the pretreatment risk of survival measures (risk-high vs risk-low) and changes (risk-increase vs risk-decrease) in risk of survival measures between the pretreatment and posttreatment scans further stratified the patients into 4 subgroups (risk: high, increase; risk: high, decrease; risk: low, increase; risk: low, decrease) with significant difference (χ² = 18.549, P = .0003, log-rank test). There was also a significant difference between the risk-increase and risk-decrease groups (χ² = 6.80, P = .0091, log-rank test). In addition, a significant difference (χ² = 7.493, P = .0062, log-rank test) was observed between the risk-high and risk-low groups obtained based on the pretreatment risk of survival measures.

CONCLUSION

The integration of risk of survival measures estimated from pre- and posttreatment CT scans can help differentiate patients with good expected survival from those who will do more poorly following SBRT. The analysis of these radiomics-based longitudinal risk measures may help identify patients with early-stage NSCLC who will benefit from adjuvant treatment after lung SBRT, such as immunotherapy.

Diffusion-weighted MRI improves response assessment after definitive radiotherapy in patients with NSCLC

Background

Computed tomography (CT) is the standard procedure for follow-up of non-small-cell lung cancer (NSCLC) after radiochemotherapy. CT has difficulties differentiating between tumor, atelectasis and radiation induced lung toxicity (RILT). Diffusion-weighted imaging (DWI) may enable a more accurate detection of vital tumor tissue. The aim of this study was to determine the diagnostic value of MRI versus CT in the follow-up of NSCLC.

Methods

Twelve patients with NSCLC stages I-III scheduled for radiochemotherapy were enrolled in this prospective study. CT with i.v. contrast agent and non enhanced MRI were performed before and 3, 6 and 12 months after treatment. Standardized ROIs were used to determine the apparent diffusion weighted coefficient (ADC) within the tumor. Tumor size was assessed by the longest longitudinal diameter (LD) and tumor volume on DWI and CT. RILT was assessed on a 4-point-score in breath-triggered T2-TSE and CT.

Results

There was no significant difference regarding LD and tumor volume between MRI and CT (p ≥ 0.6221, respectively p ≥ 0.25). Evaluation of RILT showed a very high correlation between MRI and CT at 3 (r = 0.8750) and 12 months (r = 0.903). Assessment of the ADC values suggested that patients with a good tumor response have higher ADC values than non-responders.

Conclusions

DWI is equivalent to CT for tumor volume determination in patients with NSCLC during follow up. The extent of RILT can be reliably determined by MRI. DWI could become a beneficial method to assess tumor response more accurately. ADC values may be useful as a prognostic marker.

Apparent diffusion coefficient for the prediction of tumor response to neoadjuvant chemo-radiotherapy in locally advanced rectal cancer

Background

Patients with locally advanced rectal cancer generally have different response rates to preoperative neoadjuvant chemo-radiotherapy. This study investigated the value of the apparent diffusion coefficient (ADC) as a predictor to forecast the response to neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer.

Methods

Ninety-one locally advanced rectal cancer patients who underwent neoadjuvant chemo-radiotherapy between 2015 and 2018 were enrolled. Diffusion-weighted magnetic resonance imaging was performed before treatment and within 4 weeks after the completion of neoadjuvant chemo-radiotherapy. Mean ADC values of regions of interest were evaluated by two radiologists. The tumor response was evaluated according to RESCIST 1.1. The cut-off value for the mean ADC and increasing percentage (ΔADC%) after neoadjuvant chemo-radiotherapy was calculated using the receiver operating characteristic curve. The response rate of pre-ADC and ΔADC% above/below the cut-off values was determined using the chi-square test, respectively. Primary tumor progression-free survival (PFS) was analyzed using the Kaplan–Meier method, based on the pre-ADC and ΔADC% cut-off values.

Results

The cut-off value of mean pre-ADC and ΔADC% was 0.94 × 10^–3 mm²/s (80.36% sensitivity, 74.29% specificity) and 26.0% (73.21% sensitivity, 77.14% specificity), respectively. Lower mean pre-ADC values were related to a better response rate (83.3% vs 29.7%, P < 0.001) and PFS (26.12 vs 17.70 months, P = 0.004). ΔADC% above the cut-off value was also related to a better response rate (83.7% vs 35.7%, P < 0.001) and PFS (26.93 vs 15.65 months, P = 0.034).

Conclusions

The mean ADC pre-treatment value and ΔADC% were potential predictors for the tumor response in locally advanced rectal cancer patients treated with neoadjuvant chemo-radiotherapy.

Mean apparent diffusion coefficient in a single slice may predict tumor response to whole-brain radiation therapy in non-small-cell lung cancer patients with brain metastases

OBJECTIVES

This study aimed to access the performance of apparent diffusion coefficient (ADC) as a predictor for treatment response to whole-brain radiotherapy (WBRT) in patients with brain metastases (BMs) from non-small-cell lung cancer (NSCLC).

METHODS

A retrospective analysis was conducted of 102 NSCLC patients with BMs who underwent WBRT between 2012 and 2016. Diffusion-weighted MRI were performed pre-WBRT and within 12 weeks after WBRT started. Mean single-plane ADC value of ROIs was evaluated by two radiologists blinded to results of each other. The treatment response rate, intracranial progression-free survival (PFS), and overall survival (OS) were analyzed based on the ADC value and ΔADC respectively. At last, we used COX and logistic regression to do the multivariate analysis.

RESULTS

There was good inter-observer agreement of mean ADC value pre-WBRT, post-WBRT, and ΔADC between the 2 radiologists (Pearson correlation 0.915 [pre-WBRT], 0.950 [post-WBRT], 0.937 [ΔADC], p < 0.001, for each one). High mean ADC value were related with better response rate (72.2% vs 37.5%, p = 0.001) and iPFS (7.6 vs 6.4 months, p = 0.031). High ΔADC were related with better response rate (73.6% vs 36.7%, p < 0.001). Multivariate analysis shows that histopathology, BMs number, high ADC value pre-WBRT, and high ΔADC post-WBRT were related to better treatment response of WBRT, and KPS, BMs number, and low ADC value pre-WBRT increased the risk of developing intracranial relapse.

CONCLUSIONS

The mean single-plane ADC value pre-WBRT and ΔADC post-WBRT were potential predictor for intracranial tumor response to WBRT in NSCLC patients with brain metastases.

KEY POINTS

• ADC value is a potential predictor of intracranial treatment response to WBRT in NSCLC patients with brain metastases. • Higher mean ADC value pre-WBRT and ΔADC post-WBRT of brain metastases were related to better intracranial tumor response. • Prediction of response before WBRT using ADC value can help oncologists to make better therapy plans and avoid missing opportunities for rescue therapy.

Effect of radiotherapy on T cell and PD-1 / PD-L1 blocking therapy in tumor microenvironment

Cancer is a worldwide problem that threatens human health. Radiotherapy plays an important role in a variety of cancer treatment methods. The administration of radiotherapy can alter the differentiation pathways and functions of T cells, which in turn improves the immune response of T cells. Radiotherapy can also induce up-regulation of PD-L1 expression, which means that it has great potential for enhancing the therapeutic effect of anti-PD-1/PD-L1 inhibitors and reducing the risk of drug resistance toward them. At present, the combination of radiotherapy and anti-PD-1/PD-L1 inhibitors has shown significant therapeutic effects in clinical tumor research. This review focuses on the mechanism of radiotherapy on T cells reported in recent years, as well as related research progress in the application of PD-1/PD-L1 blockers. It will provide a theoretical basis for the rational clinical application of radiotherapy combined with PD-1/PD-L1 inhibitors.

SBRT combined with PD-1/PD-L1 inhibitors in NSCLC treatment: a focus on the mechanisms, advances, and future challenges

Immune checkpoint inhibitors targeting programmed cell death 1 (PD-1), programmed cell death ligand-1 (PD-L1), and others have shown potent clinical efficacy and have revolutionized the treatment protocols of a broad spectrum of tumor types, especially non–small-cell lung cancer (NSCLC). Despite the substantial optimism of treatment with PD-1/PD-L1 inhibitors, there is still a large proportion of patients with advanced NSCLC who are resistant to the inhibitors. Preclinical and clinical trials have demonstrated that radiotherapy can induce a systemic antitumor immune response and have a great potential to sensitize refractory “cold” tumors to immunotherapy. Stereotactic body radiation therapy (SBRT), as a novel radiotherapy modality that delivers higher doses to smaller target lesions, has shown favorable antitumor effects with significantly improved local and distant control as well as better survival benefits in various solid tumors. Notably, research has revealed that SBRT is superior to conventional radiotherapy, possibly because of its more powerful immune activation effects. Thus, PD-1/PD-L1 inhibitors combined with SBRT instead of conventional radiotherapy might be more promising to fight against NSCLC, further achieving more favorable survival outcomes. In this review, we focus on the underlying mechanisms and recent advances of SBRT combined with PD-1/PD-L1 inhibitors with an emphasis on some future challenges and directions that warrant further investigation.

Radiomics for liver tumours

Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.