Distinguishing radiation fibrosis from tumour recurrence after stereotactic ablative radiotherapy (SABR) for lung cancer: a quantitative analysis of CT density changes

Predicting Radiation-Induced Lung Injury in Patients With Lung Cancer: Challenges and Opportunities

Radiation-induced lung injury (RILI) is one of the main dose-limiting toxicities in radiation therapy (RT) for lung cancer. Approximately 10% to 20% of patients show signs of RILI of variable severity. The reason for the wide range of RILI severity and the mechanisms underlying its development are only partially understood. A number of clinical risk factors have been identified that can aid in clinical decision making. Technological advancements in RT and the use of strict organ-at-risk dose constraints have helped to reduce RILI. Predicting patients at risk for RILI may be further improved with a combination of cytokine assessments, γH2AX-assays in leukocytes, or epigenetic markers. A complicating factor is the lack of an objective definition of RILI. Tools such as computed tomography densitometry, fluorodeoxyglucose-positron emission tomography uptake, changes in lung function measurements, and exhaled breath analysis can be implemented to better define and quantify RILI. This can aid in the search for new biomarkers, which can be accelerated by omics techniques, single-cell RNA sequencing, mass cytometry, and advances in patient-specific in vitro cell culture models. An objective quantification of RILI combined with these novel techniques can aid in the development of biomarkers to better predict patients at risk and allow personalized treatment decisions.

What is the predictive value of RECIST criteria following stereotactic lung radiation?

PURPOSE

Response EvaluationCriteriain Solid Tumors (RECIST) is commonly used to assess response to anti-cancer therapies. However, its application after lung stereotactic ablative radiotherapy (SABR) is complicated by radiation-induced lung changes. This study assesses the frequency of progressive disease (PD) by RECIST following lung SABR and correlates this with actual treatment outcomes as determined by longitudinal follow-up.

METHODS AND MATERIALS

We reviewed patients treated with lung SABR for primary lung tumors or oligometastases between 2010 and 2015. Patients were treated with SABR doses of 54-60 Gy in 3-8 fractions. All follow-up scans were assessed and the treated lesion was serially measured over time, with the maximum diameter on axial CT slices used for RECIST calculations. Lesions demonstrating PD by RECIST criteria were identified and subsequently followed for long-term outcomes. The final 'gold-standard' assessment of response was based on size changes after PD and, as available, positron emission tomography scan and/or biopsy.

RESULTS

Eighty-eight lesions met inclusion criteria. Seventy-five were lung primaries and thirteen were lung metastases. Median follow-up was 52 months (interquartile range: 33-68). Two-thirds (66 %, 58/88) of treated lesions met RECIST criteria for PD; however, local recurrence was only confirmed in 16 % (9/58) of cases. Most lesions that triggered PD by RECIST (47/58, 81 %) were ultimately found not to represent recurrence, while a minority (2/58, 3 %) had an uncertain response. The positive predictive value [PPV] of a RECIST defined PD event was 0.16. If PD was triggered within 12-months post-treatment, PPV was 0.08, compared to 0.21 for lesions triggering PD after 12-months.

CONCLUSION

Using RECIST criteria, two-thirds of patients treated with lung SABR met criteria for PD. However, only a minority had recurrence, leading to a poor PPV of RECIST. This highlights the limitations of RECIST in this setting and provides context for physicians when interpreting post-lung SABR imaging.

Metabolic Alterations in Sputum and Exhaled Breath Condensate of Early Stage Non-Small Cell Lung Cancer Patients After Surgical Resection: A Pilot Study

Every year, close to two million people world-wide are diagnosed with and die of lung cancer. Most patients present with advanced-stage cancer with limited curative options and poor prognosis. Diagnosis of lung cancer at an early stage provides the best chance for a cure. Low- dose CT screening of the chest in the high-risk population is the current standard of care for early detection of lung cancer. However, CT screening is invasive due to radiation exposure and carries the risk of unnecessary biopsies in non-cancerous tumors. In this pilot study, we present metabolic alterations observed in sputum and breath condensate of the same population of early- stage non-small cell lung cancer (NSCLC) patients cancer before and after surgical resection (SR), which could serve as noninvasive diagnostic tool. Exhaled breath condensate (EBC) (n=35) and sputum (n=15) were collected from early-stage non-small cell lung cancer (NSCLC) patients before and after SR. Median number of days for EBC and sputum collection before and after SR were 7 and 42; and 7 and 36 respectively Nuclear magnetic resonance (NMR) and liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) were used to analyze the metabolic profile of the collected samples. A total of 26 metabolites with significant alteration post SR were identified, of which 14 (54%) were lipids and 12 constituted nine different chemical metabolite classes. Eighteen metabolites (69%) were significantly upregulated and 8 (31%) were downregulated. Median fold change for all the up- and downregulated metabolites (LC-QTOF-MS) were 10 and 8, respectively. Median fold change (MFC) in concentration of all the up- and downregulated metabolites (NMR) were 0.04 and 0.27, respectively. Furthermore, glucose (median fold change, 0.01, p=0.037), adenosine monophosphate (13 log fold, p=0.0037) and N1, N12- diacetylspermine (8 log fold p=0.011) sputum levels were significantly increased post-SR. These identified sputa and EBC indices of altered metabolism could serve as basis for further exploration of biomarkers for early detection of lung cancer, treatment response, and targets for drug discovery. Validation of these promising results by larger clinical studies is warranted.

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.

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.

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

PURPOSE

To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT.

MATERIALS AND METHODS

One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann-Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves.

RESULTS

Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001).

CONCLUSIONS

Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Simple Summary

Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential.

Abstract

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

Assessment of Clinical Stage IA Lung Adenocarcinoma with pN1/N2 Metastasis Using CT Quantitative Texture Analysis

OBJECTIVE

To explore the application of texture analysis basing on computed tomography (CT) images in predicting lymph-node metastasis in patients with clinical stage IA lung adenocarcinoma.

METHODS

In total, 256 patients with clinical stage IA lung adenocarcinoma who had underwentgone preoperative CT examinations were enrolled. A total of 25 texture features using MaZda (version 4.6) software and conventional radiological features were extracted from raw CT data sets. Based on surgical results, patients were stratified into lymph node metastasis-positive and -negative groups. Independent-sample t-tests and Mann-Whitney U tests were used to compare continuous variables between the groups. Continuity-correction and χ2 tests were used for categorical variable comparison. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of lymph-node metastasis.

RESULTS

In total, 256 clinical stage IA lung adenocarcinoma cases were proved by pathology: 39 (15.23%) cases with lymph-node metastasis (14 N1a, seven N1b, six N2a1, ten N2a2, and two N2b) and 217 (84.77%) cases without lymph-node metastasis. Univariate and multivariate logistic regression analyses demonstrated that total volume (OR 3.777, p=0.015), average CT value of whole tumor (OR 16.271, p<0.001), three texture parameters (mean OR 8.473, p<0.001; skewness OR 6.393, p=0.001; and entropy OR 0.343, p=0.049) were independent factors associated with lymph-node status. As such, early-stage lung adenocarcinoma with higher total volume (>4.05 cm3), average CT value of whole tumor (>-70 HU), mean (>133.79), entropy (>1.98), and lower skewness (≤0.02) pointed to positive lymph-node metastasis.

CONCLUSION

Texture parameters were independent factors associated with lymph-node status in clinical stage IA lung adenocarcinoma.

Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images

Identification of histologic grading of urothelial carcinoma still depends on histopathologic examination. As an emerging and promising imaging technology, radiomic texture analysis is a noninvasive technique and has been studied to differentiate various tumors. This study explored the value of computed tomography (CT) texture analysis for the differentiation of low-grade urothelial carcinoma (LGUC), high-grade urothelial carcinoma (HGUC), and their invasive properties.Radiologic data were analyzed retrospectively for 94 patients with pathologically proven urothelial carcinomas from November 2016 to April 2019. Pathologic examination demonstrated that tumors were: high grade in 43 cases, and low grade in 51 cases; and nonmuscle invasive (NMI) in 37 cases, and muscle invasive (MI) in 37 cases. Maximum tumor diameters on CT scan were manually outlined as regions of interest and 78 texture features were extracted automatically. Three-phasic CT images were used to measure texture parameters, which were compared with postoperative pathologic grading and invasive results. The independent sample t test or Mann-Whitney U test was used to compare differences in parameters. Receiver-operating characteristic curves for statistically significant parameters were used to confirm efficacy.Of the 78 features extracted from each phase of CT images, 26 (33%), 20 (26%), and 22 (28%) texture parameters were significant (P < .05) for differentiating LGUC from HGUC, while 19 (24%), 16 (21%), and 30 (38%) were significant (P < .05) for differentiating NMI from MI urothelial carcinoma. Highest areas the under curve for differentiating grading and invasive properties were obtained by variance (0.761, P < .001) and correlation (0.798, P < .001) on venous-phase CT images.Texture analysis has the potential to distinguish LGUC and HGUC, or NMI from MI urothelial carcinoma, before surgery.

Correlation of normal lung density changes with dose after stereotactic body radiotherapy (SBRT) for early stage lung cancer

BACKGROUND AND PURPOSE

To investigate the correlation between normal lung CT density changes with dose accuracy and outcome after stereotactic body radiation therapy (SBRT) for patients with early stage non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

Thirty-one patients (with a total of 33 lesions) with non-small cell lung cancer were selected out of 270 patients treated with SBRT at a single institution between 2003 and 2009. Out of these 31 patients, 10 patients had developed radiation pneumonitis (RP). Dose distributions originally planned using a 1-D pencil beam-based dose algorithm were retrospectively recomputed using different algorithms. Prescription dose was 48 Gy in 4 fractions in most patients. Planning CT images were rigidly registered to follow-up CT datasets at 3-9 months after treatment. Corresponding dose distributions were mapped from planning to follow-up CT images. Hounsfield Unit (HU) changes in lung density in individual, 5 Gy, dose bins from 5 to 45 Gy were assessed in the peri-tumoral region. Correlations between HU changes in various normal lung regions, dose indices (V20, MLD, generalized equivalent uniform dose (gEUD)), and RP grade were investigated.

RESULTS

Strong positive correlation was found between HU changes in the peri-tumoral region and RP grade (Spearman's r = 0.760; p < 0.001). Positive correlation was also observed between RP and HU changes in the region covered by V20 for all algorithms (Spearman's r ≥ 0.738; p < 0.001). Additionally, V20, MLD, and gEUD were significantly correlated with RP grade (p < 0.01). MLD in the peri-tumoral region computed with model-based algorithms was 5-7% lower than the PB-based methods.

CONCLUSION

Changes of lung density in the peri-tumoral lung and in the region covered by V20 were strongly associated with RP grade. Relative to model-based methods, PB algorithms over-estimated mean peri-tumoral dose and showed displacement of the high-dose region, which correlated with HU changes on follow-up CT scans.

[Prognosis factors after lung stereotactic body radiotherapy for non-small cell lung carcinoma]

Lung cancer is the fourth most common cancer in France with a prevalence of 30,000 new cases per year. Lobectomy surgery with dissection is the gold standard treatment for T1-T2 localized non-small cell lung carcinoma. A segmentectomy may be proposed to operable patients but fragile from a respiratory point of view. For inoperable patients or patients with unsatisfactory pulmonary function tests, local treatment with stereotactic radiotherapy may be proposed to achieve local control rates ranging from 85 to 95% at 3-5 years. Several studies have examined prognostic factors after stereotaxic pulmonary radiotherapy. We conducted a general review of the literature to identify factors affecting local control.

Impact of Corticosteroid Administration on Outcomes Following Stereotactic Ablative Radiotherapy for Non-small-cell Lung Cancer

INTRODUCTION

Radiotherapy produces immune-promoting effects, which may be blunted by the delivery of corticosteroids (CS). We thus aimed to evaluate the impact of CS use on recurrence and survival outcomes of patients with early stage non-small-cell lung cancer treated with stereotactic ablative radiotherapy (SABR).

MATERIALS AND METHODS

A prospectively registered database of patients with stage I to II (T1-3N0M0) stage non-small-cell lung cancer treated with SABR from 2004 to 2015 was queried. Concurrent CS administration was defined as receipt of CS within 2 days of the SABR course. Statistics included Kaplan-Meier survival analysis, Cox proportional hazards modeling, and cumulative incidence analysis utilizing death as a competing risk.

RESULTS

Of 912 patients, 87 (9.5%) received CS with their SABR course. The most common agent was prednisone (64.4%). Indications for CS use were chronic obstructive pulmonary disease in 53 cases (60.9%), chemotherapy in 7 (8.0%), arthritis in 7 (8.0%), chronic pain in 4 (4.6%), transplant-related in 3 (3.4%), and "others" in 13 (14.9%; pneumonia, asthma, anemia, etc.). The median follow-up time was 59.3 months. Compared with patients who did not receive CS, receipt of CS was associated with poorer overall survival (P = .004). However, CS administration was not associated with worse time to progression (P = .766) or any recurrence when using death as a competing risk (local P = .119, regional P = .449, distant P = .847, and any recurrence P = .708). Toxicity rates were not statistically different between cohorts.

CONCLUSIONS

These data do not suggest increased recurrence rates when patients undergoing SABR are administered corticosteroids. However, owing to limitations of retrospective analyses, individualized judgment is still recommended.

Organs at Risk Considerations for Thoracic Stereotactic Body Radiation Therapy: What Is Safe for Lung Parenchyma?

PURPOSE

Stereotactic body radiation therapy (SBRT) has become the standard of care for inoperable early-stage non-small cell lung cancer and is often used for recurrent lung cancer and pulmonary metastases. Radiation-induced lung toxicity (RILT), including radiation pneumonitis and pulmonary fibrosis, is a major concern for which it is important to understand dosimetric and clinical predictors.

METHODS AND MATERIALS

This study was undertaken through the American Association of Physicists in Medicine's Working Group on Biological Effects of Stereotactic Body Radiotherapy. Data from studies of lung SBRT published through the summer of 2016 that provided detailed information about RILT were analyzed.

RESULTS

Ninety-seven studies were ultimately considered. Definitions of the risk organ and complication endpoints as well as dose-volume information presented varied among studies. The risk of RILT, including radiation pneumonitis and pulmonary fibrosis, was reported to be associated with the size and location of the tumor. Patients with interstitial lung disease appear to be especially susceptible to severe RILT. A variety of dosimetric parameters were reported to be associated with RILT. There was no apparent threshold "tolerance dose-volume" level. However, most studies noted safe treatment with a rate of symptomatic RILT of <10% to 15% after lung SBRT with a mean lung dose (MLD) of the combined lungs ≤8 Gy in 3 to 5 fractions and the percent of total lung volume receiving more than 20 Gy (V₂₀) <10% to 15%.

CONCLUSIONS

To allow more rigorous analysis of this complication, future studies should standardize reporting by including standardized endpoint and volume definitions and providing dose-volume information for all patients, with and without RILT.

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy.

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.

Quantitative texture analysis on pre-treatment computed tomography predicts local recurrence in stage I non-small cell lung cancer following stereotactic radiation therapy

Background

The prediction of local recurrence (LR) of stage I non-small cell lung cancer (NSCLC) after definitive stereotactic body radiotherapy (SBRT) remains elusive. The purpose of this study was to assess whether quantitative imaging features on pre-treatment computed tomography (CT) can predict LR beyond 18 (¹⁸F) fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT maximum standard uptake value (SUV_max).

Methods

This retrospective study evaluated 36 patients with 37 stage I NSCLC who had local tumor control (LC; n=19) and (LR; n=18). Textural features were extracted on pre-treatment CT. Mann-Whitney U tests were used to compare LC and LR groups. Receiver-operating characteristic (ROC) curves were constructed and the area under the curve (AUC) calculated with LR as outcome.

Results

Gray-level correlation and sum variance were greater in the LR group, compared with the LC group (P=0.02 and P=0.04, respectively). Gray-level difference variance was lower in the LR group (P=0.004). The logistic regression model generated using gray-level correlation and difference variance features resulted in AUC (SE) 0.77 (0.08) (P=0.0007). The addition of 18F-FDG PET/CT SUV_max did not improve the AUC (P=0.75).

Conclusions

CT textural features were found to be predictors of LR of early stage NSCLC on baseline CT prior to SBRT.

The Value of CBCT-based Tumor Density and Volume Variations in Prediction of Early Response to Chemoradiation Therapy in Advanced NSCLC

The correlations between early responses and the variations in physical density and primary tumor volume (TV) according to cone-beam computed tomography (CBCT) during chemoradiotherapy for non-small cell lung cancer (NSCLC) patients were investigated. 54 patients with inoperable and locally advanced NSCLC were included in this study. The CT numbers (CTN) and TV were measured on each of the seven observation points. The changes in the mean CTN values and the variation ratios of TV during the treatment course were analysed and correlated with the clinical outcomes, as evaluated by the RECIST criteria. For patients who responded to treatment, the CTN and TV change ratio decreased by 28.44 ± 13.12 HU and 32.01% (range, 8.46-61.67%); these values were significantly higher than those in the non-responding patients, with 19.63 ± 8.67 HU and 23.20% (range, -15.57-38%) (p = 0.016, p = 0.048), respectively. The area under curve for the combination of CTN and TV was larger than either alone (AUC = 0.751, p = 0.002). The differences between response and non-response were most significant between Fraction 10 and Fraction 15 for CTN changes and between Fraction 5 and Fraction 10 for the TV regression ratio. The changes in CTN and TV obtained from CBCT images have the potential capability to predict an early response of NSCLC.

[Quantitative Imaging Assessment of Tumor Response to Chemoradiation in Lung Cancer]

精准医疗的实施要求及时准确地对治疗疗效进行评估，以便于治疗方案的调整和优化，从而进一步提高疗效，改善预后。以定量评估为基础的影像组学以其无创、直观和可重复的特点在临床疗效评估方面具有不可替代的作用。本文将综述定量影像学在肺癌放化疗疗效评估中的应用现状及其相关进展。

Early Assessment of Treatment Responses During Radiation Therapy for Lung Cancer Using Quantitative Analysis of Daily Computed Tomography

PURPOSE

To investigate early tumor and normal tissue responses during the course of radiation therapy (RT) for lung cancer using quantitative analysis of daily computed tomography (CT) scans.

METHODS AND MATERIALS

Daily diagnostic-quality CT scans acquired using CT-on-rails during CT-guided RT for 20 lung cancer patients were quantitatively analyzed. On each daily CT set, the contours of the gross tumor volume (GTV) and lungs were generated and the radiation dose delivered was reconstructed. The changes in CT image intensity (Hounsfield unit [HU]) features in the GTV and the multiple normal lung tissue shells around the GTV were extracted from the daily CT scans. The associations between the changes in the mean HUs, GTV, accumulated dose during RT delivery, and patient survival rate were analyzed.

RESULTS

During the RT course, radiation can induce substantial changes in the HU histogram features on the daily CT scans, with reductions in the GTV mean HUs (dH) observed in the range of 11 to 48 HU (median 30). The dH is statistically related to the accumulated GTV dose (R² > 0.99) and correlates weakly with the change in GTV (R² = 0.3481). Statistically significant increases in patient survival rates (P=.038) were observed for patients with a higher dH in the GTV. In the normal lung, the 4 regions proximal to the GTV showed statistically significant (P<.001) HU reductions from the first to last fraction.

CONCLUSION

Quantitative analysis of the daily CT scans indicated that the mean HUs in lung tumor and surrounding normal tissue were reduced during RT delivery. This reduction was observed in the early phase of the treatment, is patient specific, and correlated with the delivered dose. A larger HU reduction in the GTV correlated significantly with greater patient survival. The changes in daily CT features, such as the mean HU, can be used for early assessment of the radiation response during RT delivery for lung cancer.

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges

OBJECTIVE

Texture analysis involves the mathematic processing of medical images to derive sets of numeric quantities that measure heterogeneity. Studies on lung cancer have shown that texture analysis may have a role in characterizing tumors and predicting patient outcome. This article outlines the mathematic basis of and the most recent literature on texture analysis in lung cancer imaging. We also describe the challenges facing the clinical implementation of texture analysis.

CONCLUSION

Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

Imaging after radiation therapy of thoracic tumors

Radiation-induced lung disease (RILD) is frequent after therapeutic irradiation of thoracic malignancies. Many technique-, treatment-, tumor- and patient-related factors influence the degree of injury sustained by the lung after irradiation. Based on the time interval after the completion of the treatment RILD presents as early and late features characterized by inflammatory and fibrotic changes, respectively. They are usually confined to the radiation port. Though the typical pattern of RILD is easily recognized after conventional two-dimensional radiation therapy (RT), RILD may present with atypical patterns after more recent types of three- or four-dimensional RT treatment. Three atypical patterns are reported: the modified conventional, the mass-like and the scar-like patterns. Knowledge of the various features and patterns of RILD is important for correct diagnosis and appropriate treatment. RILD should be differentiated from recurrent tumoral disease, infection and radiation-induced tumors. Due to RILD, the follow-up after RT may be difficult as response evaluation criteria in solid tumours (RECIST) criteria may be unreliable to assess tumor control particularly after stereotactic ablation RT (SABR). Long-term follow-up should be based on clinical examination and morphological and/or functional investigations including CT, PET-CT, pulmonary functional tests, MRI and PET-MRI.

Pulmonary imaging after stereotactic radiotherapy-does RECIST still apply?

The use of stereotactic ablative radiotherapy (SABR) for the treatment of primary lung cancer and metastatic disease is rapidly increasing. However, the presence of benign fibrotic changes on CT imaging makes response assessment following SABR a challenge, as these changes develop with an appearance similar to tumour recurrence. Misclassification of benign fibrosis as local recurrence has resulted in unnecessary interventions, including biopsy and surgical resection. Response evaluation criteria in solid tumours (RECIST) are widely used as a universal set of guidelines to assess tumour response following treatment. However, in the context of non-spherical and irregular post-SABR fibrotic changes, the RECIST criteria can have several limitations. Positron emission tomography can also play a role in response assessment following SABR; however, false-positive results in regions of inflammatory lung post-SABR can be a major clinical issue and optimal standardized uptake values to distinguish fibrosis and recurrence have not been determined. Although validated CT high-risk features show a high sensitivity and specificity for predicting recurrence, most recurrences are not detected until more than 1-year post-treatment. Advanced quantitative radiomic analysis on CT imaging has demonstrated promise in distinguishing benign fibrotic changes from local recurrence at earlier time points, and more accurately, than physician assessment. Overall, the use of RECIST alone may prove inferior to novel metrics of assessing response.

Cone-beam computed tomography in lung stereotactic ablative radiation therapy: predictive parameters of early response

OBJECTIVE:

To analyze lung lesion volume variations by contouring on cone-beam CT (CBCT) images to evaluate the early predictive parameters of stereotactic ablative radiation therapy (SABR) treatment response.

METHODS:

The prescribed dose of SABR was varied according to the tumour site (central or peripheral) and maximum diameter of the lesions by using a strategy of risk-adapted dose prescription with a dose range between 48 and 70 Gy in 3-10 consecutive fractions. For the purpose of the analysis, the gross tumour volume (GTV) was recontoured for each patient at first and last CBCT using two lung levels/windows: (a) -600/1000 HU and (b) -1000/250 HU. Univariate analysis was performed to evaluate a correlation between lung lesion variations on CBCT using the two levels/windows and treatment response 6 months after SABR. Independent variables were the number of fractions, time between initial and final fraction, biologically effective dose and pre-SABR GTV. Cut points of lesion volume reduction were evaluated to determine the correlation with complete response 6 months after SABR.

RESULTS:

41 lung lesions were evaluated. 82 lung lesions were recontoured for each CBCT level/window. A lung lesion shrinkage of at least 20% was revealed to be statistically related to complete response 6 months after SABR for both the CBCT levels/windows used. The probability of complete response ranged between six and eight times higher in respect to CBCT levels/windows -600/1000 HU and -1000/250 HU, respectively, compared with patients without a lesion shrinkage of 20% at the last session of SABR.

CONCLUSION:

According to current findings, a lung lesion shrinkage of at least 20% at the last session of SABR could be predictable of complete response 6 months thereafter. Further investigations about this topic are needed.

ADVANCES IN KNOWLEDGE:

Prediction of the early tumour response could be useful to personalize imaging restaging after the completion of SABR or to incorporate additional therapies in case of poor responders to improve clinical outcomes.

Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer

Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction.

The use of isodose levels to interpret radiation induced lung injury: a quantitative analysis of computed tomography changes

BACKGROUND

Patients treated with stereotactic body radiation therapy (SBRT) for lung cancer are often found to have radiation-induced lung injury (RILI) surrounding the treated tumor. We investigated whether treatment isodose levels could predict RILI.

METHODS

Thirty-seven lung lesions in 32 patients were treated with SBRT and received post-treatment follow up (FU) computed tomography (CT). Each CT was fused with the original simulation CT and treatment isodose levels were overlaid. The RILI surrounding the treated lesion was contoured. The RILI extension index [fibrosis extension index (FEI)] was defined as the volume of RILI extending outside a given isodose level relative to the total volume of RILI and was expressed as a percentage.

RESULTS

Univariate analysis revealed that the planning target volume (PTV) was positively correlated with RILI volume at FU: correlation coefficient (CC) =0.628 and P<0.0001 at 1(st) FU; CE =0.401 and P=0.021 at 2(nd) FU; CE =0.265 and P=0.306 at 3(rd) FU. FEI -40 Gy at 1(st) FU was significantly positively correlated with FEI -40 Gy at subsequent FU's (CC =0.689 and P=6.5×10(-5) comparing 1(st) and 2(nd) FU; 0.901 and P=0.020 comparing 2(nd) and 3(rd) FU. Ninety-six percent of the RILI was found within the 20 Gy isodose line. Sixty-five percent of patients were found to have a decrease in RILI on the second 2(nd) CT.

CONCLUSIONS

We have shown that RILI evolves over time and 1(st) CT correlates well with subsequent CTs. Ninety-six percent of the RILI can be found to occur within the 20 Gy isodose lines, which may prove beneficial to radiologists attempting to distinguish recurrence vs. RILI.

The Fate of Residual Tumor Masses That Persist After Stereotactic Body Radiotherapy for Solitary Lung Nodules: Will They Recur?

BACKGROUND

After stereotactic body radiotherapy (SBRT) to the lung, radiation pneumonitis and fibrotic changes often develop, and the tumor shadow usually becomes indistinguishable from the fibrotic shadow. Occasionally, however, a residual mass in patients with no or mild radiation pneumonitis will be observed on serial computed tomography (CT) scans. The purpose of the present study was to evaluate the fate of such residual masses and, if possible, to determine the types of tumor at increased risk of local recurrence.

PATIENTS AND METHODS

A total of 50 patients (underlying disease: primary lung cancer in 45, local recurrence in 2, and metastasis in 3) were selected because they had been followed up for > 2 years or until death and had observable CT changes and measurable tumor size for > 1 year, regardless of the influence of radiation pneumonitis. The patients' outcomes were compared according to various patient and tumor characteristics, including the presence and absence of emphysema, tumor size, and tumor shrinkage rate. The median follow-up period was 52 months.

RESULTS

Of the 50 patients, only 8 developed local recurrence. The local control rate was 81% at 3 years and 73% at 5 years. The local control rates were similar between patients with a high tumor shrinkage rate and those with a low rate. The patients with emphysema exhibited a lower local control rate.

CONCLUSION

The persistence of lung masses for > 1 year after SBRT is not necessarily indicative of an increased risk of local recurrence. A low tumor shrinkage rate also was not predictive of recurrence.

Patterns of CT lung injury and toxicity after stereotactic radiotherapy delivered with helical tomotherapy in early stage medically inoperable NSCLC

OBJECTIVE

To evaluate toxicity and patterns of radiologic lung injury on CT images after hypofractionated image-guided stereotactic body radiotherapy (SBRT) delivered with helical tomotherapy (HT) in medically early stage inoperable non-small-cell lung cancer (NSCLC).

METHODS

28 elderly patients (31 lesions) with compromised pulmonary reserve were deemed inoperable and enrolled to undergo SBRT. Patterns of lung injury based on CT appearance were assessed at baseline and during follow up. Acute (6 months or less) and late (more than 6 months) events were classified as radiation pneumonitis and radiation fibrosis (RF), respectively.

RESULTS

After a median follow-up of 12 months (range, 4-20 months), 31 and 25 lesions were examined for acute and late injuries, respectively. Among the former group, 25 (80.6%) patients showed no radiological changes. The CT appearance of RF revealed modified conventional, mass-like and scar-like patterns in three, four and three lesions, respectively. No evidence of late lung injury was demonstrated in 15 lesions. Five patients developed clinical pneumonitis (four patients, grade 2 and one patient, grade 3, respectively), and none of whom had CT findings at 3 months post-treatment. No instance of symptomatic RF was detected. The tumour response rate was 84% (complete response + partial response). Local control was 83% at 1 year.

CONCLUSION

Our findings show that HT-SBRT can be considered an effective treatment with a mild toxicity profile in medically inoperable patients with early stage NSCLC. No specific pattern of lung injury was demonstrated.

ADVANCES IN KNOWLEDGE

Our study is among the few showing that HT-SBRT represents a safe and effective option in patients with early stage medically inoperable NSCLC, and that it is not associated with a specific pattern of lung injury.

Challenges in the treatment of early non-small cell lung cancer: what is the standard, what are the challenges and what is the future for radiotherapy?

In the last 15 years, the use of Stereotactic Ablative Radiation Therapy (SABRT) in the management of small peripheral lung tumours has developed considerably, so that it currently represents a standard of care for inoperable stage I non-small cell lung cancer (NSCLC), offering a survival advantage over traditional radiotherapy, local control rates at 3 years around 90%, with a low risk of toxicity. Indications have extended to larger tumours up to 5 cm and centrally located tumours. In this review we will explore the role of SABRT in early stage NSCLC, the state of the art, the challenges and the future for this technique. There are ongoing studies to optimize such approaches within a multicentric setting. Trials comparing surgery to SABRT in operable or marginally operable have failed because of poor accrual. Several questions remain that need to be addressed in prospective studies.

Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer

PURPOSE

Benign computed tomography (CT) changes due to radiation induced lung injury (RILI) are common following stereotactic ablative radiotherapy (SABR) and can be difficult to differentiate from tumor recurrence. The authors measured the ability of CT image texture analysis, compared to more traditional measures of response, to predict eventual cancer recurrence based on CT images acquired within 5 months of treatment.

METHODS

A total of 24 lesions from 22 patients treated with SABR were selected for this study: 13 with moderate to severe benign RILI, and 11 with recurrence. Three-dimensional (3D) consolidative and ground-glass opacity (GGO) changes were manually delineated on all follow-up CT scans. Two size measures of the consolidation regions (longest axial diameter and 3D volume) and nine appearance features of the GGO were calculated: 2 first-order features [mean density and standard deviation of density (first-order texture)], and 7 second-order texture features [energy, entropy, correlation, inverse difference moment (IDM), inertia, cluster shade, and cluster prominence]. For comparison, the corresponding response evaluation criteria in solid tumors measures were also taken for the consolidation regions. Prediction accuracy was determined using the area under the receiver operating characteristic curve (AUC) and two-fold cross validation (CV).

RESULTS

For this analysis, 46 diagnostic CT scans scheduled for approximately 3 and 6 months post-treatment were binned based on their recorded scan dates into 2-5 month and 5-8 month follow-up time ranges. At 2-5 months post-treatment, first-order texture, energy, and entropy provided AUCs of 0.79-0.81 using a linear classifier. On two-fold CV, first-order texture yielded 73% accuracy versus 76%-77% with the second-order features. The size measures of the consolidative region, longest axial diameter and 3D volume, gave two-fold CV accuracies of 60% and 57%, and AUCs of 0.72 and 0.65, respectively.

CONCLUSIONS

Texture measures of the GGO appearance following SABR demonstrated the ability to predict recurrence in individual patients within 5 months of SABR treatment. Appearance changes were also shown to be more accurately predictive of recurrence, as compared to size measures within the same time period. With further validation, these results could form the substrate for a clinically useful computer-aided diagnosis tool which could provide earlier salvage of patients with recurrence.

Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review

BACKGROUND

Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice.

METHODOLOGY

The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared.

PRINCIPAL FINDINGS

Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description.

CONCLUSIONS

In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.

Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer

PURPOSE

To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non-small cell lung cancer (NSCLC).

METHODS AND MATERIALS

We retrospectively reviewed 91 cases with stage III NSCLC treated with definitive chemoradiation therapy. All patients underwent pretreatment diagnostic contrast enhanced computed tomography (CE-CT) followed by 4-dimensional CT (4D-CT) for treatment simulation. We used the average-CT and expiratory (T50-CT) images from the 4D-CT along with the CE-CT for texture extraction. Histogram, gradient, co-occurrence, gray tone difference, and filtration-based techniques were used for texture feature extraction. Penalized Cox regression implementing cross-validation was used for covariate selection and modeling. Models incorporating texture features from the 33 image types and CPFs were compared to those with models incorporating CPFs alone for overall survival (OS), local-regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan-Meier curves were generated using leave-one-out cross-validation. Patients were stratified based on whether their predicted outcome was above or below the median. Reproducibility of texture features was evaluated using test-retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test-retest scans to our original models and determined the classification reproducibility.

RESULTS

Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone for OS (P=.046), LRC (P=.01), and FFDM (P=.005). The average CCCs were 0.89, 0.91, and 0.67 for texture features extracted from the average-CT, T50-CT, and CE-CT, respectively. Incorporating reproducibility within our models yielded 80.4% (±3.7% SD), 78.3% (±4.0% SD), and 78.8% (±3.9% SD) classification reproducibility in terms of OS, LRC, and FFDM, respectively.

CONCLUSIONS

Pretreatment tumor texture may provide prognostic information beyond that obtained from CPFs. Models incorporating feature reproducibility achieved classification rates of ∼80%. External validation would be required to establish texture as a prognostic factor.

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Predicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

New techniques for assessing response after hypofractionated radiotherapy for lung cancer

Hypofractionated radiotherapy (HFRT) is an effective and increasingly-used treatment for early stage non-small cell lung cancer (NSCLC). Stereotactic ablative radiotherapy (SABR) is a form of HFRT and delivers biologically effective doses (BEDs) in excess of 100 Gy10 in 3-8 fractions. Excellent long-term outcomes have been reported; however, response assessment following SABR is complicated as radiation induced lung injury can appear similar to a recurring tumor on CT. Current approaches to scoring treatment responses include Response Evaluation Criteria in Solid Tumors (RECIST) and positron emission tomography (PET), both of which appear to have a limited role in detecting recurrences following SABR. Novel approaches to assess response are required, but new techniques should be easily standardized across centers, cost effective, with sensitivity and specificity that improves on current CT and PET approaches. This review examines potential novel approaches, focusing on the emerging field of quantitative image feature analysis, to distinguish recurrence from fibrosis after SABR.

Does early posttreatment surveillance imaging affect subsequent management following stereotactic body radiation therapy for early-stage non-small cell lung cancer?

PURPOSE

Uncertainty exists regarding the optimal surveillance imaging strategy following stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC), particularly with respect to timing. We sought to determine how routine use of early (<6 months) posttreatment imaging affects subsequent management.

METHODS AND MATERIALS

The records of all patients treated with SBRT between January 2007 and January 2013 for early-stage NSCLC were reviewed. Eligible patients underwent ≥ 1 early (defined as within 6 months following SBRT) surveillance imaging study. Radiographic findings and subsequent diagnostic or therapeutic interventions were identified. Proportions and exact 95% confidence intervals (CI) with early posttreatment surveillance findings and altered treatment were calculated, and cases were examined descriptively.

RESULTS

Sixty-two patients with 67 lung tumors underwent 92 early surveillance imaging studies (86 computed tomographic [CT] and 6 positron emission/CT) at a median of 2.1 months (range, 0.1-5.9 months). New lung nodules were identified in 8 patients (13%), leading to a diagnosis of metastatic disease treated with systemic therapy in 2 patients and biopsy proven solitary lung recurrence in 2 patients, both treated successfully with local therapy. Tumor growth meeting Response Evaluation Criteria in Solid Tumors (RECIST) criteria was identified in 1 patient, who was followed with subsequent radiographic regression. In aggregate, the treatment of 4 patients (6.5%, 95% CI 1.7%-15.2%) was altered by early imaging; 2 (3.2%, 95% CI 0.4%-10.8%) with a potentially curative intervention. No predictors for utility of early surveillance were identified.

CONCLUSIONS

Imaging within 6 months following SBRT for early-stage NSCLC resulted in a definitive intervention in approximately 3% of patients. In the era of cost-effective health care, a first scan at 6 months posttreatment may be adequate for most patients. Larger scale prospective studies are needed to address the optimal surveillance regimen following SBRT and to identify patients who may benefit from more aggressive surveillance regimens.

Quantification of radiation-induced lung damage with CT scans: the possible benefit for radiogenomics

BACKGROUND

Radiation-induced lung damage (RILD) is an important problem. Although physical parameters such as the mean lung dose are used in clinical practice, they are not suited for individualised radiotherapy. Objective, quantitative measurements of RILD on a continuous instead of on an ordinal, semi-quantitative, semi-subjective scale, are needed.

METHODS

Hounsfield unit (HU) changes before versus three months post-radiotherapy were correlated per voxel with the radiotherapy dose in 95 lung cancer patients. Deformable registration was used to register pre- and post-CT scans and the density increase was quantified for various dose bins. The dose-response curve for increased HU was quantified using the slope of a linear regression (HU/Gy). The end-point for the toxicity analysis was dyspnoea ≥ grade 2.

RESULTS

Radiation dose was linearly correlated with the change in HU (mean R(2) = 0.74 ± 0.28). No differences in HU/Gy between groups treated with stereotactic radiotherapy, conventional radiotherapy alone, sequential or concurrent chemo- radiotherapy were observed. In the whole patient group, 33/95 (34.7%) had dyspnoea ≥ G2. Of the 48 patients with a HU/Gy below the median, 16 (33.3%) developed dyspnoea ≥ G2, while in the 47 patients with a HU/Gy above the median, 17 (36.1%) had dyspnoea ≥ G2 (not significant). Individual patients showed a nearly 21-fold difference in radiosensitivity, with HU/Gy ranging from 0 to 10 HU/Gy.

CONCLUSIONS

HU changes identify objectively the whole range of individual radiosensitivity on a continuous, quantitative scale. CT density changes may allow more robust and accurate radiogenomics studies.