Pulmonary imaging after stereotactic radiotherapy-does RECIST still apply?

Distinguishing recurrence from radiation-induced lung injury at the time of RECIST progressive disease on post-SABR CT scans using radiomics

Stereotactic ablative radiotherapy (SABR) is a highly effective treatment for patients with early-stage lung cancer who are inoperable. However, SABR causes benign radiation-induced lung injury (RILI) which appears as lesion growth on follow-up CT scans. This triggers the standard definition of progressive disease, yet cancer recurrence is not usually present, and distinguishing RILI from recurrence when a lesion appears to grow in size is critical but challenging. In this study, we developed a tool to do this using scans with apparent lesion growth after SABR from 68 patients. We performed bootstrapped experiments using radiomics and explored the use of multiple regions of interest (ROIs). The best model had an area under the receiver operating characteristic curve of 0.66 and used a sphere with a diameter equal to the lesion's longest axial measurement as the ROI. We also investigated the effect of using inter-feature and volume correlation filters and found that the former was detrimental to performance and that the latter had no effect. We also found that the radiomics features ranked as highly important by the model were significantly correlated with outcomes. These findings represent a key step in developing a tool that can help determine who would benefit from follow-up invasive interventions when a SABR-treated lesion increases in size, which could help provide better treatment for patients.

Imaging of cell death in malignancy: Targeting pathways or phenotypes?

Cell death is fundamental in health and disease and resisting cell death is a hallmark of cancer. Treatment of malignancy aims to cause cancer cell death, however current clinical imaging of treatment response does not specifically image cancer cell death but assesses this indirectly either by changes in tumor size (using x-ray computed tomography) or metabolic activity (using 2-[18F]fluoro-2-deoxy-glucose positron emission tomography). The ability to directly image tumor cell death soon after commencement of therapy would enable personalised response adapted approaches to cancer treatment that is presently not possible with current imaging, which is in many circumstances neither sufficiently accurate nor timely. Several cell death pathways have now been identified and characterised that present multiple potential targets for imaging cell death including externalisation of phosphatidylserine and phosphatidylethanolamine, caspase activation and La autoantigen redistribution. However, targeting one specific cell death pathway carries the risk of not detecting cell death by other pathways and it is now understood that cancer treatment induces cell death by different and sometimes multiple pathways. An alternative approach is targeting the cell death phenotype that is "agnostic" of the death pathway. Cell death phenotypes that have been targeted for cell death imaging include loss of plasma membrane integrity and dissipation of the mitochondrial membrane potential. Targeting the cell death phenotype may have the advantage of being a more sensitive and generalisable approach to cancer cell death imaging. This review describes and summarises the approaches and radiopharmaceuticals investigated for imaging cell death by targeting cell death pathways or cell death phenotype.

Radiomic analysis for early differentiation of lung cancer recurrence from fibrosis in patients treated with lung stereotactic ablative radiotherapy

Objective. The development of radiation-induced fibrosis after stereotactic ablative radiotherapy (SABR) can obscure follow-up images and delay detection of a local recurrence in early-stage lung cancer patients. The objective of this study was to develop a radiomics model for computer-assisted detection of local recurrence and fibrosis for an earlier timepoint (<1 year) after the SABR treatment.Approach. This retrospective clinical study included CT images (n= 107) of 66 patients treated with SABR. A z-score normalization technique was used for radiomic feature standardization across scanner protocols. The training set for the radiomics model consisted of CT images (66 patients; 22 recurrences and 44 fibrosis) obtained at 24 months (median) follow-up. The test set included CT-images of 41 patients acquired at 5-12 months follow-up. Combinations of four widely used machine learning techniques (support vector machines, gradient boosting, random forests (RF), and logistic regression) and feature selection methods (Relief feature scoring, maximum relevance minimum redundancy, mutual information maximization, forward feature selection, and LASSO) were investigated. Pyradiomics was used to extract 106 radiomic features from the CT-images for feature selection and classification.Main results. An RF + LASSO model scored the highest in terms of AUC (0.87) and obtained a sensitivity of 75% and a specificity of 88% in identifying a local recurrence in the test set. In the training set, 86% accuracy was achieved using five-fold cross-validation. Delong's test indicated that AUC achieved by the RF+LASSO is significantly better than 11 other machine learning models presented here. The top three radiomic features: interquartile range (first order), Cluster Prominence (GLCM), and Autocorrelation (GLCM), were revealed as differentiating a recurrence from fibrosis with this model.Significance. The radiomics model selected, out of multiple machine learning and feature selection algorithms, was able to differentiate a recurrence from fibrosis in earlier follow-up CT-images with a high specificity rate and satisfactory sensitivity performance.

Integration of artificial intelligence in lung cancer: Rise of the machine

The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

The Management of Oligometastases in Non-small Cell Lung Cancer - is Stereotactic Ablative Radiotherapy now Standard of Care?

Oligometastatic non-small cell lung cancer encompasses a number of distinct clinical scenarios with a pattern of limited tumour burden on imaging. Delivering local ablative therapy to individual metastatic lesions may assist in disease modification and contribute to improved outcomes. We review the published randomised clinical trials that support the implementation of stereotactic ablative radiotherapy as a standard of care in certain oligometastatic non-small cell lung cancer clinical scenarios, and highlight the current knowledge gaps and areas of ongoing research.

Tumor Volume Regression during and after Radiochemotherapy: A Macroscopic Description

Tumor volume regression during and after chemo and radio therapy is a useful information for clinical decisions. Indeed, a quantitative, patient oriented, description of the response to treatment can guide towards the modification of the scheduled doses or the evaluation of the best time for surgery. We propose a macroscopic algorithm which permits to follow quantitatively the time evolution of the tumor volume during and after radiochemotherapy. The method, initially validated with different cell-lines implanted in mice, is then successfully applied to the available data for partially responding and complete recovery patients.

Image-Guided Robotic Radiosurgery for the Treatment of Lung Metastases of Renal Cell Carcinoma-A Retrospective, Single Center Analysis

Simple Summary

Patients with metastatic renal cell carcinoma are difficult to treat despite many new systemic therapy options. Patients often present with pulmonary metastases. Local treatment of those metastases is traditionally performed surgically. In this study, robotic radiosurgery is tested in 50 patients with pulmonary metastases and is demonstrated to be a safe and highly effective treatment option in this patient group. Future research might focus on the combination of robotic radiosurgery with systemic treatment.

Abstract

Pulmonary metastases are the most frequent site of metastases in renal cell carcinoma (RCC). Metastases directed treatment remains an important treatment option despite advances in systemic therapies. However, the safety and efficacy of robotic radiosurgery (RRS) for the treatment of lung metastases of RCC remains unclear. Patients with metastatic RCC and lung metastases treated by RRS were retrospectively analyzed for overall survival (OS), progression-free survival (PFS), local recurrence free survival (LRFS) and adverse events. The Kaplan–Meier method was used for survival analysis and the common terminology criteria for adverse events (CTCAE; Version 5.0) classification for assessment of adverse events. A total of 50 patients were included in this study. Median age was 64 (range 45–92) years at the time of RRS. Prior to RRS, 20 patients (40.0%) had received either tyrosine kinase inhibitors or immunotherapy and 27 patients (54.0%) were treatment naïve. In our patient cohort, the median PFS was 13 months (range: 2–93). LRFS was 96.7% after two years with only one patient revealing progressive disease of the treated metastases 13 months after RRS. Median OS was 35 months (range 2–94). Adverse events were documented in six patients (12%) and were limited to grade 2. Fatigue (n = 4) and pneumonitis (n = 2) were observed within 3 months after RRS. In conclusion, RRS is safe and effective for patients with metastatic RCC and pulmonary metastases. Radiation induced pneumonitis is specific in the treatment of pulmonary lesions, but not clinically relevant and survival rates seem favorable in this highly selected patient cohort. Future directions are the implementation of RRS in multimodal treatment approaches for oligometastatic or oligoprogressive disease.

Local ablative therapies in oligometastatic NSCLC-upfront or outback?-a narrative review

Patients with oligometastatic (OM) non-small cell lung cancer (NSCLC) have favorable outcomes compared to patients presenting with diffuse metastatic disease. Recent randomized trials have demonstrated safety and efficacy signals for local ablative therapies with radiotherapy, surgery, or radiofrequency ablation for OM-NSCLC patients alongside systemic therapies. However, it remains unclear whether local ablative therapy (LAT) should be offered either upfront preceding systemic therapies or following initial systemic therapies as local consolidative therapy (LCT). Establishing optimal timing of RT and systemic therapy combinations is essential to maximize efficacy while maintaining safety. Most published randomized trial evidence surrounding the benefits of LAT and systemic therapies were generated from OM-NSCLC patients receiving cytotoxic chemotherapy agents. With increasing use of novel agents such as targeted therapies (i.e., tyrosine kinase inhibitors) and immune checkpoint inhibitors in management of metastatic NSCLC patients, LAT timing may need to be modulated based on the use of specific agents. This narrative review will discuss the current evidence on either upfront LAT or LCT for OM-NSCLC based on published trials and cohort studies. We briefly explored the possible biological mechanisms of the potential clinical advantages of either approach. This review also summarized the ongoing trials incorporating both upfront LAT and LCT, and considerations for future LAT strategies.

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

PURPOSE

The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution.

MATERIALS AND METHODS

Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (Rad_CT ), radiomics CT and BED (Rad_CT,BED ), deep learning (DL) CT (DL_CT ), and DL CT and BED (DL_CT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5.

RESULTS

Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a Rad_CT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75-0.85)) and AUC of 0.773 (0.688-0.846), and a DL_CT,BED model also using three variables with an accuracy of 0.798 (0.649-0.829) and AUC of 0.812 (0.755-0.867).

CONCLUSION

According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Evaluation of tumor response after stereotactic body radiation therapy for lung cancer: Role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography

BACKGROUND

Early identification of patients who fail to lung stereotactic body radiation therapy (SBRT) is vital as they can benefit from salvage therapy. Main guidelines recommend computed tomography (CT) to assess response and use of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT only when a local recurrence is suspected in CT. The pattern of radiation-induced lung injury caused by SBRT is different from changes seen after conventional radiation therapy in terms of extent, time of manifestation, and morphologic characteristics, and knowing this is crucial for proper monitoring of the tumor response. In certain cases, it may be difficult to differentiate response from progression or recurrence on CT and, in addition, some changes in CT take a long time to evolve before they are considered suspicious, making early diagnosis difficult. Metabolic changes often precede morphological changes, so 18F-FDG PET/CT quantitative and qualitative metabolic criteria can be useful in assessing early response and detecting relapses. However, the optimal practice for follow-up remains unclear and there is an active search for imaging markers for recurrent disease, including CT texture analysis, biomarker assays, new PET/CT isotopes, and magnetic resonance imaging.

AIM

The aim of the study was to review the radiological changes that are objectified after pulmonary SBRT and the metabolic changes in 1F-FDG PET/CT, to assess the usefulness of following up patients with 18F-FDG PET/CT.

RELEVANCE FOR PATIENTS

At present, the evaluation of response and diagnosis of relapse after SBRT are difficult and the incorporation of routine 18F-FDG PET/CT may have value in early diagnosis of relapse when the patient may still benefit from rescue treatment.

SABR-BRIDGE: Stereotactic ABlative Radiotherapy Before Resection to AvoId Delay for Early-Stage LunG Cancer or OligomEts During the COVID-19 Pandemic

Surgical resection is the standard-of-care approach for early-stage non-small cell lung cancer (NSCLC). Surgery is also considered an acceptable standard infit patients with oligometastatic lesions in the lungs. The COVID-19 pandemic has led to worldwide issues with access to operating room time, with patients and physicians facing uncertainty as to when surgical resection will be available, with likely delays of months. Further compounding this are concerns about increased risks of respiratory complications with lung cancer surgery during active phases of the pandemic. In this setting, many thoracic oncology teams are embracing a paradigm where stereotactic ablative radiotherapy (SABR) is used as a bridge, to provide radical-intent treatment based on a combination of immediate SABR followed by planned surgery in 3–6 months. This pragmatic approach to treatment has been named SABR-BRIDGE (Stereotactic ABlative Radiotherapy Before Resection to avoId Delay for early-stage lunG cancer or oligomEts). This term has also been applied to the pragmatic study of the outcomes of this approach. In this paper, we discuss the standards of care in treatment of early-stage (NSCLC) and pulmonary oligometastases, the impetus for the SABR-BRIDGE approach, and the controversies surrounding assessment of pathological response to neo-adjuvant radiation therapy.

Timing of fluorodeoxyglucose positron emission tomography maximum standardized uptake value for diagnosis of local recurrence of non-small cell lung cancer after stereotactic body radiation therapy

Introduction

After treatment with stereotactic body radiation therapy (SBRT), local recurrence of non‐small cell cancer (NSCLC) can be difficult to differentiate from radiation‐induced changes. Maximum standardized uptake value (SUVmax), measured with 18‐F‐Fluorodeoxyglucose positron emission tomography (FDG‐PET), can have false positives due to acute radiation inflammation. The primary study objective was to determine the utility of SUVmax > 5 to identify local recurrence later than 9 months after SBRT.

Method

A retrospective review was performed of FDG‐PET scans for suspicious CT findings after SBRT treatment of stage 1 NSCLC. SUVmax was measured including surrounding opacification. Outcome measures were local recurrence, progression free survival, and overall survival. Receiver operator curve analysis, sensitivity, specificity, and Kaplan‐Meier analysis were performed.

Results

Of 118 patients treated, 42 patients had eligible FDG‐PET scans. They received SBRT (48‐60Gy in 3‐8 fractions) for 49 NSCLC and had 101 follow‐up PET scans. The median time to first PET scan was 9.3 months, and the median follow‐up period was 22.4 months. Local recurrence was diagnosed in 12 patients, at a median of 16 months. Due to selection bias, the included patients had poorer outcomes than the entire cohort, with progression free survival (PFS) at 1, 2, and 3 years of 82.7%, 57.8%, and 45.8%; and overall survival of 97.9%, 79.9%, and 59.1%, respectively. Thirty FDG‐PET scans were performed within 9 months, of which 17% were false positives. A total of 71 FDG‐PET scans were performed beyond 9 months, and the median SUVmax was significantly higher for patients with local recurrence (7.48 vs. 2.14, P < .0001). SUVmax > 5 has a sensitivity of 91% (95% CI 62%‐99.8%) and 100% (89.1%‐100%).

Conclusion

For local recurrence of NSCLC, SUVmax > 5 on FDG‐PET scan has good sensitivity and specificity after 6 months, but is highest beyond 9 months after SBRT.

Four-year follow-up outcomes after stereotactic body radiation therapy for central early-stage non-small cell lung cancer

Background

Previous research has shown that stereotactic body radiation therapy (SBRT) can achieve a high level of tumor control in patients with early-stage non-small cell lung cancer (NSCLC). However, to date, such studies have mainly focused on peripheral early-stage patients. This study aimed to assess the clinical outcomes and toxicity of patients with central lung cancer treated with SBRT in our institution.

Methods

A total of 31 consecutive central early-stage NSCLC patients who were treated with SBRT using the biologically effective dose (BED; α/β =10) 100–119 Gy in 4–10 fractions between April, 2013, and August, 2016, were reviewed. The RTOG 0813 trial standard was used to define whether the NSCLC was centrally located. All patients received four-dimensional computed tomography (4D CT) simulation. Intensity modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D CRT) techniques were used in treatment planning. The dose to the planning target volume (PTV) was prescribed to the 95% isodose line. Mainly dosimetric parameters, clinical outcomes, and toxicity were analyzed.

Results

The 31 patients enrolled in the study had a median follow-up time of 47.7 months, and the median tumor diameter was 2.2 cm (range, 1.3–5.0 cm). A total of 15 patients (48.4%) developed disease recurrence. The incidences of local, regional, and distant disease recurrence at 3 years were 11.7%, 9.7%, and 30.7%, respectively; at 5 years, they were 21.5%, 15.0%, and 35.0%, respectively. The 3- and 5-year progression-free survival (PFS) rates were 55.1% and 40.5%, respectively; the corresponding overall survival (OS) rates were 85.3% and 68.4%, respectively. Toxicities of grade 3 or higher were observed in 6.5% of the patients. None of the patients experienced grade 4 or 5 acute adverse events; however, 2 patients possibly died of treatment-related late toxicity.

Conclusions

SBRT with a BED 100 Gy in 4–10 fractions is effective and acceptable for treating patients with central early-stage NSCLC. Further studies are warranted.

Radiological assessment after stereotactic body radiation of lung tumours

The increasing use of stereotactic body radiation therapy for lung tumours comes along with new post-therapeutic imaging findings that should be known by physicians involved in patient follow-up. Radiation-induced lung injury is much more frequent than after conventional radiation therapy, it can also be delayed and has a different radiological presentation. Radiation-induced lung injury after stereotactic body radiation therapy involves the lung parenchyma surrounding the target tumour and appears as a dynamic process continuing for years after completion of the treatment. Thus, the radiological pattern and the severity of radiation-induced lung injury are prone to changes during follow-up, which can make it difficult to differentiate from local recurrence. Contrary to radiation-induced lung injury, local recurrence after stereotactic body radiation therapy is rare. Other complications mainly depend on tumour location and include airway complications, rib fractures and organizing pneumonia. The aim of this article is to provide a wide overview of radiological changes occurring after SBRT for lung tumours. Awareness of changes following stereotactic body radiation therapy should help avoiding unnecessary interventions for pseudo tumoral presentations.

Safety and Efficacy of a Five-Fraction Stereotactic Body Radiotherapy Schedule for Centrally Located Non-Small-Cell Lung Cancer: NRG Oncology/RTOG 0813 Trial

PURPOSE

Patients with centrally located early-stage non-small-cell lung cancer (NSCLC) are at a higher risk of toxicity from high-dose ablative radiotherapy. NRG Oncology/RTOG 0813 was a phase I/II study designed to determine the maximum tolerated dose (MTD), efficacy, and toxicity of stereotactic body radiotherapy (SBRT) for centrally located NSCLC.

MATERIALS AND METHODS

Medically inoperable patients with biopsy-proven, positron emission tomography-staged T1 to 2 (≤ 5 cm) N0M0 centrally located NSCLC were accrued into a dose-escalating, five-fraction SBRT schedule that ranged from 10 to 12 Gy/fraction (fx) delivered over 1.5 to 2 weeks. Dose-limiting toxicity (DLT) was defined as any treatment-related grade 3 or worse predefined toxicity that occurred within the first year. MTD was defined as the SBRT dose at which the probability of DLT was closest to 20% without exceeding it.

RESULTS

One hundred twenty patients were accrued between February 2009 and September 2013. Patients were elderly, there were slightly more females, and the majority had a performance status of 0 to 1. Most cancers were T1 (65%) and squamous cell (45%). Organs closest to planning target volume/most at risk were the main bronchus and large vessels. Median follow-up was 37.9 months. Five patients experienced DLTs; MTD was 12.0 Gy/fx, which had a probability of a DLT of 7.2% (95% CI, 2.8% to 14.5%). Two-year rates for the 71 evaluable patients in the 11.5 and 12.0 Gy/fx cohorts were local control, 89.4% (90% CI, 81.6% to 97.4%) and 87.9% (90% CI, 78.8% to 97.0%); overall survival, 67.9% (95% CI, 50.4% to 80.3%) and 72.7% (95% CI, 54.1% to 84.8%); and progression-free survival, 52.2% (95% CI, 35.3% to 66.6%) and 54.5% (95% CI, 36.3% to 69.6%), respectively.

CONCLUSION

The MTD for this study was 12.0 Gy/fx; it was associated with 7.2% DLTs and high rates of tumor control. Outcomes in this medically inoperable group of mostly elderly patients with comorbidities were comparable with that of patients with peripheral early-stage tumors.

Image-based management of empiric lung stereotactic body radiotherapy (SBRT) without biopsy: Predictors from a 10-year single institution experience

Background

There is emerging reliance on clinical imaging for the diagnosis, prognosis, and treatment evaluation of early stage non‐small cell lung cancer (NSCLC) in patients deemed too high risk for biopsy. We report our experience of clinically diagnosed NSCLC treated empirically with stereotactic body radiotherapy (SBRT) to validate the imaging parameters used for management in this high‐risk population.

Methods

We reviewed 101 empiric lung SBRT cases and profiled imaging specifics of computed tomography and positron emission tomography for diagnosis and follow‐up. Secondarily, we identified potential correlates of disease progression with Cox regression multivariate analysis.

Results

Fifty‐seven men and 43 women aged 45–94 (median 76) were treated with a median dose of 48 Gy in four fractions. The median nodule diameter was 1.6 cm (0.6–4.5 cm) and most were spiculated (n = 58), right‐sided (n = 63), and in the upper lobe (n = 68). Median follow‐up and survival rates were 14 and 28 months, respectively. Local control at three years was 94%. Freedom from any progression at one and three years was 85% and 69%, respectively. Toxicity ≥ grade 3 included two grade 3 dyspneas. A pre‐treatment standard uptake value > 4.1 was the only significant predictor of disease progression.

Conclusion

This study illustrates the instrumental role of modern clinical imaging for the effective management of presumed early stage NSCLC treated with empiric lung SBRT. As lung SBRT without tissue confirmation becomes more common, hopefully these assertions can be prospectively validated.

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature.

Treatment outcomes and patterns of radiologic appearance after hypofractionated image-guided radiotherapy delivered with helical tomotherapy (HHT) for lung tumours

OBJECTIVE

To evaluate treatment outcomes and patterns of CT lung injury after hypofractionated image-guided radiotherapy delivered with helical tomotherapy (HHT) in a series of inoperable lung lesions.

METHODS

68 patients who were medically inoperable (69 lesions) without evidence of viable extrathoracic disease were included. Dose prescription was driven by tumour location (hilar/pericentral vs peripheral) and/or target volume. 52% of the lesions received a biological equivalent dose (BED10) ≥100 Gy. Assessment of tumour response was based on the Response Evaluation Criteria in Solid Tumours 1.1 criteria coupled with fluorine-18 fludeoxyglucose/positron emission tomography-CT. Toxicity monitoring was focused on treatment-related pulmonary adverse events according to the Common Terminology Criteria for Adverse Events v. 4.0. Acute and late events were classified as radiation pneumonitis (RP) and radiation fibrosis (RF), respectively. Survival curves were calculated using the Kaplan-Meier method. Univariate and multivariate analyses of survival were performed using the Cox proportional hazards model.

RESULTS

After a median follow-up of 12 months (range, 3-31 months), no instances of ≥Grade 4 RP was documented, and clinically severe (Grade 3) RP occurred in 5.8% of the patients. 2 (3%) patients developed a late severe (≥Grade 3) symptomatic RF. No specific pattern of CT lung injury was demonstrated, in both acute and late settings. Median overall survival (OS) and progression-free survival (PFS) for the entire population were 30.8 and 14.1 months, respectively. At multivariate analysis (MVA), BED10 ≥ 100 Gy and KPS ≥ 90 emerged as significant prognostic factors for OS (p = 0.01 and p = 0.001, respectively), and BED10 ≥ 100 Gy for PFS (p = 0.02).

CONCLUSION

Our findings show that HHT adjusted for tumour location and/or target volume is an effective treatment with an acceptable toxicity profile in patients who are medically inoperable with lung tumours and is not associated with a specific pattern of lung injury. Therefore, it can represent a viable option when conventional stereotactic ablative radiotherapy facilities are not available. Advances in knowledge: The present study is among the largest series addressing the role of HHT for inoperable lung tumours. This technique is safe and effective and is not associated with a specific pattern of lung injury, at least at early and average time points.

Texture analysis of medical images for radiotherapy applications

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

The Effect of Biologically Effective Dose and Radiation Treatment Schedule on Overall Survival in Stage I Non-Small Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy

PURPOSE

To determine the effect of biologically effective dose (BED₁₀) and radiation treatment schedule on overall survival (OS) in patients with early-stage non-small cell lung cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT).

METHODS AND MATERIALS

Using data from 65 treatment centers in the United States, we retrospectively reviewed the records of T1-2 N0 NSCLC patients undergoing SBRT alone from 2006 to 2014. Biologically relevant covariates, including dose per fraction, number of fractions, and time between fractions, were used to quantify BED₁₀ and radiation treatment schedule. The linear-quadratic equation was used to calculate BED₁₀ and to generate a dichotomous dose variable of <105 Gy versus ≥105 Gy BED₁₀. The primary outcome was OS. We used the Kaplan-Meier method, the log-rank test, and Cox proportional hazards regression with propensity score matching to determine whether prescription BED₁₀ was associated with OS.

RESULTS

We identified 747 patients who met inclusion criteria. The median BED₁₀ was 132 Gy, and 59 (7.7%) had consecutive-day fractions. Median follow-up was 41 months, and 452 patients (60.5%) had died by the conclusion of the study. The 581 patients receiving ≥105 Gy BED₁₀ had a median survival of 28 months, whereas the 166 patients receiving <105 Gy BED₁₀ had a median survival of 22 months (log-rank, P=.01). Radiation treatment schedule was not a significant predictor of OS on univariable analysis. After adjusting for T stage, sex, tumor histology, and Eastern Cooperative Oncology Group performance status, BED₁₀ ≥105 Gy versus <105 Gy remained significantly associated with improved OS (hazard ratio 0.78, 95% confidence interval 0.62-0.98, P=.03). Propensity score matching on imbalanced variables within high- and low-dose cohorts confirmed a survival benefit with higher prescription dose.

CONCLUSIONS

We found that dose escalation to 105 Gy BED₁₀ and beyond may improve survival in NSCLC patients treated with SBRT.

Tyrosine Kinase Inhibitors for Non-Small Cell Lung Cancer and Eye Metastasis: Disease Relapse or a New Entity?

Lung cancer is still diagnosed during the advanced stage of the disease and most patients do not have the opportunity for surgical treatment, despite the new diagnostic equipment that has been made available in recent years, such as the radial and linear endobronchial ultrasound (EBUS) and electromagnetic fiberoptic bronchoscopy. However, novel targeted therapies with second generation tyrosine kinase inhibitors and immunotherapy are available. In this commentary, we will focus on eye metastasis after initiation of tyrosine kinase inhibitors due to epidermal growth factor mutation of lung cancer adenocarcinoma.