Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy

[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.

Imaging-Based Individualized Response Prediction Of Carbon Ion Radiotherapy For Prostate Cancer Patients

Purpose

To explore the value of the pre-treatment MRI radiomic features in individualized prediction of the therapeutic response of carbon ion radiotherapy (CIRT) for prostate cancer patients.

Patients and methods

Twenty-three patients with localized prostate cancer treated by CIRT were enrolled for analysis. Prostate tumors were manually delineated on T2-weighted (T2w) images and apparent diffusion coefficient (ADC) maps acquired before CIRT. Abundant radiomic features were extracted from the delineations, which were randomly deformed to account for delineation uncertainty. The robust features were selected and then compared between patient groups of different CIRT responses. Support vector machine (SVM) was subsequently applied to demonstrate the role of the radiomic features to predict individualized CIRT response in the way of artificial intelligence.

Results

Radiomic features from ADC had significantly higher intra-correlation coefficient (ICC) (0.71±0.28) than T2w features (0.60±0.31) (p<0.01), indicating higher robustness of ADC features against delineation uncertainty. More features were excellently robust in ADC (58.2% of all the radiomic feature candidates, compared to 41.3% in T2w). By combining the excellently robust radiomic features of T2w and ADC, SVM achieved high performance to predict individualized therapeutic response of CIRT, ie, area-under-curve (AUC) = 0.88.

Conclusion

Radiomic features extracted from T2w and ADC images displayed great robustness to quantify the tumor characteristics of prostate cancer and high accuracy to predict the individualized therapeutic response of CIRT. After further validation, the selected radiomic features may become potential imaging biomarkers in the management of prostate cancer through CIRT.

CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm

PURPOSE

Radiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancer patients based on computed tomography (CT) radiomics, clinical and dosimetric parameters.

METHODS

In this prospective study, prostate cancer patients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. Stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical-radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, - 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic.

RESULTS

Sixty-four prostate cancer patients were studied, and 33 and 52 patients developed ≥ grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical-radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical-radiomics models was 0.71, 0.67 and 0.77, respectively.

CONCLUSIONS

We have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling.

Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging

OBJECTIVES

To investigate the performance of the mean parametric values and texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

METHODS

Pretreatment IVIM-DWI was performed on 41 LARC patients receiving nCRT in this prospective study. The values of IVIM-DWI parameters (apparent diffusion coefficient, ADC; pure diffusion coefficient, D; pseudo-diffusion coefficient, D* and perfusion fraction, f), the first-order, and gray-level co-occurrence matrix (GLCM) texture features were compared between the pCR (n = 9) and non-pathological responder (non-pCR, n = 32) groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine the efficiency for identifying pCR.

RESULTS

The values of IVIM-DWI parameters and first-order texture features did not show significant differences between the pCR and non-pCR groups. The pCR group had lower Contrast and DifVarnc values extracted from the ADC, D, and D* maps, respectively, as well as lower Correlat_D value. Higher Correlat_D*, Correlat_f, SumAverg_ADC, and SumAverg_D values were observed in the pCR group. The area under the ROC curve (AUC) values for the individual predictors in univariate analysis ranged from 0.698 to 0.837, with sensitivities from 43.75% to 87.50% and specificities from 66.67 to 100.00%. In multivariate analysis, Correlat_D* (P < 0.001), DifVarnc_ADC (P = 0.024), and DifVarnc_D (P < 0.001) were the independent predictors to pCR, with an AUC of 0.986, a sensitivity of 93.75%, and a specificity of 100.00%.

CONCLUSION

Pretreatment GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC.

Radiation-induced lung injury: latest molecular developments, therapeutic approaches, and clinical guidance

Cancer research has advanced throughout the years with respect to the personalization of the treatments and to targeting cancer-related molecular signatures on different organs. Still, the adverse events of the treatments such as radiotherapy are of high concern as they may increase the mortality rate due to their severity. With the improved efficiency of cancer treatments, patient survival has been increasing. Consequently, the number of patients with adverse effects from radiotherapy is also expected to increase in the forthcoming years. Therefore, approaches for personalized treatments include the elimination of adverse events and decreasing the toxicity in healthy tissues while increasing the efficiency of cancer cytotoxicity. In this context, this paper aims to discuss the recent advances in the field of thorax irradiation therapy and its related toxicities leading to radiation pneumonitis in cancer patients. Molecular mechanisms involved in the radiation-induced lung injury and approaches used to overcome this lung injury are discussed. The discourse covers approaches such as therapeutic administration of natural products, current and prospective radioprotective drugs, and applications of mesenchymal stem cells for radiation-induced lung injury.

Radiomics: Principles and radiotherapy applications

Radiomics is defined as the extraction of a large quantity of quantitative image features. The different radiomic indexes that have been proposed in the literature are described as well as the various factors that have an impact on the robustness of these indexes. We will see that several hundred quantitative features can be extracted per lesion and imaging modality. The ever-growing number of features studied raises the question of the statistical method of analysis used. This review addresses the research supporting the clinical use of radiomics in oncology in the staging of disease, discrimination between healthy and pathological tissues, the identification of genetic features, the prediction of patient survival, the response to treatment, the recurrence after radiotherapy and chemoradiotherapy and the side effects. Based on the existing literature, it remains difficult to identify features that should be used for current clinical practice.

Radiomic analysis of planning computed tomograms for predicting radiation-induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy

OBJECTIVES

To predict radiation-induced lung injury and outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT) from radiomic features of the primary tumor.

METHODS

In all, 110 patients with primary stage I/IIa NSCLC were analyzed for local control (LC), disease-free survival (DFS), overall survival (OS) and development of local lung injury up to fibrosis (LF). First-order (histogram), second-order (GLCM, Gray Level Co-occurrence Matrix) and shape-related radiomic features were determined from the unprocessed or filtered planning CT images of the gross tumor volume (GTV), subjected to LASSO (Least Absolute Shrinkage and Selection Operator) regularization and used to construct continuous and dichotomous risk scores for each endpoint.

RESULTS

Continuous scores comprising 1-5 histogram or GLCM features had a significant (p = 0.0001-0.032) impact on all endpoints that was preserved in a multifactorial Cox regression analysis comprising additional clinical and dosimetric factors. At 36 months, LC did not differ between the dichotomous risk groups (93% vs. 85%, HR 0.892, 95%CI 0.222-3.590), while DFS (45% vs. 17%, p < 0.05, HR 0.457, 95%CI 0.240-0.868) and OS (80% vs. 37%, p < 0.001, HR 0.190, 95%CI 0.065-0.556) were significantly lower in the high-risk groups. Also, the frequency of LF differed significantly between the two risk groups (63% vs. 20% at 24 months, p < 0.001, HR 0.158, 95%CI 0.054-0.458).

CONCLUSION

Radiomic analysis of the gross tumor volume may help to predict DFS and OS and the development of local lung fibrosis in early stage NSCLC patients treated with stereotactic radiotherapy.

Challenges and Promises of PET Radiomics

PURPOSE

Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.

METHODS AND MATERIALS

Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly.

RESULTS

In relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.

CONCLUSIONS

Although at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.

Potential of serum microRNAs as biomarkers of radiation injury and tools for individualization of radiotherapy

Due to tremendous technological advances, radiation oncologists are now capable of personalized treatment plans and deliver the dose in a highly precise manner. However, a crucial challenge is how to escalate radiation doses to cancer cells while reducing damage to surrounding healthy tissues. This determines the probability of achieving therapeutic success whilst safeguarding patients from complications. The current dose constraints rely on observational data. Therefore, incidental toxicity observed in a minority of patients limits the admissible dose thresholds for the whole population, theoretically narrowing down the curative potential of radiotherapy. Future tools for measurements of individual's radiosensitivity before and during treatment would allow proper treatment personalization. Variation in tissue tolerance is at least partially genetically-determined and recent progress in the field of molecular biology raises the possibility that novel assays will allow to predict the response to ionizing radiation. Recently, microRNAs have garnered interest as stable biomarkers of tumor radiation response and normal-tissue toxicity. Preclinical studies in mice and nonhuman primates have shown that serum circulating microRNAs can be used to accurately distinguish pre- and postirradiation states and predict the biological impact of high-dose irradiation. First reports from human studies are also encouraging, however biology-driven precision radiation oncology, which tailors treatment to individual patient's needs, still remains to be translated into clinical studies. In this review, we summarize current knowledge about the potential of serum microRNAs as biodosimeters and biomarkers for radiation injury to lung and hematopoietic cells.

Diabetes mellitus and radiation induced lung injury after thoracic stereotactic body radiotherapy

BACKGROUND

Radiographic radiation induced lung injury (RILI) is frequently observed after stereotactic body radiotherapy (SBRT). Models of radiographic change can identify patient risk factors that predict clinical toxicity. We examined the association between radiographic lung changes and lung tissue dose-density response over time with clinical risk factors for RILI, such as diabetes.

METHODS

424 baseline and follow up CT scans at 3, 6, and 12 months post-treatment were analyzed in 116 patients (27 with diabetes) undergoing thoracic SBRT. Volumes of dense/hazy regions and lung parenchyma dose-density response curves were evaluated with respect to follow up time, diabetes, and other factors.

RESULTS

Dense and hazy tissue regions were larger in the diabetic population, with the effect most pronounced at 3 months. Similarly, dose-density response curves showed greater density change versus dose in the diabetic group (all p < 0.05). Diabetes, time, the interaction of diabetes and time, smoking status, African American race, baseline lung density, and tumor location were significantly associated with radiographic changes on mixed effect analyses. PTV size, pulmonary function, and medication exposure did not significantly impact RILI. Clinical grade 1-2 pneumonitis was more prevalent in diabetic patients (p = 0.02). However, radiographic change did not correlate with clinical pneumonitis.

CONCLUSIONS

The presence of diabetes and other clinical factors is associated with increased volume and density of radiographic RILI after lung SBRT, most prominently early after treatment. This is the first report demonstrating the increased severity of RILI after SBRT in diabetic patients. Increased caution treating diabetic patients may be warranted.

Proton relative biological effectiveness (RBE): a multiscale problem

Proton radiotherapy is undergoing rapid expansion both within the UK and internationally, but significant challenges still need to be overcome if maximum benefit is to be realised from this technique. One major limitation is the persistent uncertainty in proton relative biological effectiveness (RBE). While RBE values are needed to link proton radiotherapy to our existing experience with photon radiotherapy, RBE remains poorly understood and is typically incorporated as a constant dose scaling factor of 1.1 in clinical plans. This is in contrast to extensive experimental evidence indicating that RBE is a function of dose, tissue type, and proton linear energy transfer, among other parameters. In this article, we discuss the challenges associated with obtaining clinically relevant values for proton RBE through commonly-used assays, and highlight the wide range of other experimental end points which can inform our understanding of RBE. We propose that accurate and robust optimization of proton radiotherapy ultimately requires a multiscale understanding of RBE, integrating subcellular, cellular, and patient-level processes.

Clinical Results of Mean GTV Dose Optimized Robotic-Guided Stereotactic Body Radiation Therapy for Lung Tumors

Introduction

We retrospectively evaluated the efficacy and toxicity of gross tumor volume (GTV) mean dose optimized stereotactic body radiation therapy (SBRT) for primary and secondary lung tumors with and without robotic real-time motion compensation.

Materials and methods

Between 2011 and 2017, 208 patients were treated with SBRT for 111 primary lung tumors and 163 lung metastases with a median GTV of 8.2 cc (0.3–174.0 cc). Monte Carlo dose optimization was performed prioritizing GTV mean dose at the potential cost of planning target volume (PTV) coverage reduction while adhering to safe normal tissue constraints. The median GTV mean biological effective dose (BED)₁₀ was 162.0 Gy₁₀ (34.2–253.6 Gy₁₀) and the prescribed PTV BED₁₀ ranged 23.6–151.2 Gy₁₀ (median, 100.8 Gy₁₀). Motion compensation was realized through direct tracking (44.9%), fiducial tracking (4.4%), and internal target volume (ITV) concepts with small (≤5 mm, 33.2%) or large (>5 mm, 17.5%) motion. The local control (LC), progression-free survival (PFS), overall survival (OS), and toxicity were analyzed.

Results

Median follow-up was 14.5 months (1–72 months). The 2-year actuarial LC, PFS, and OS rates were 93.1, 43.2, and 62.4%, and the median PFS and OS were 18.0 and 39.8 months, respectively. In univariate analysis, prior local irradiation (hazard ratio (HR) 0.18, confidence interval (CI) 0.05–0.63, p = 0.01), GTV/PTV (HR 1.01–1.02, CI 1.01–1.04, p < 0.02), and PTV prescription, mean GTV, and maximum plan BED₁₀ (HR 0.97–0.99, CI 0.96–0.99, p < 0.01) were predictive for LC while the tracking method was not (p = 0.97). For PFS and OS, multivariate analysis showed Karnofsky Index (p < 0.01) and tumor stage (p ≤ 0.02) to be significant factors for outcome prediction. Late radiation pneumonitis or chronic rip fractures grade 1–2 were observed in 5.3% of the patients. Grade ≥3 side effects did not occur.

Conclusion

Robotic SBRT is a safe and effective treatment for lung tumors. Reducing the PTV prescription and keeping high GTV mean doses allowed the reduction of toxicity while maintaining high local tumor control. The use of real-time motion compensation is strongly advised, however, well-performed ITV motion compensation may be used alternatively when direct tracking is not feasible.

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.

Development and clinical application of radiomics in lung cancer

Since the discovery of X-rays at the end of the 19^th century, medical imageology has progressed for 100 years, and medical imaging has become an important auxiliary tool for clinical diagnosis. With the launch of the human genome project (HGP) and the development of various high-throughput detection techniques, disease exploration in the post-genome era has extended beyond investigations of structural changes to in-depth analyses of molecular abnormalities in tissues, organs and cells, on the basis of gene expression and epigenetics. These techniques have given rise to genomics, proteomics, metabolomics and other systems biology subspecialties, including radiogenomics. Radiogenomics is an important revolution in the traditional visually identifiable imaging technology and constitutes a new branch, radiomics. Radiomics is aimed at extracting quantitative imaging features automatically and developing models to predict lesion phenotypes in a non-invasive manner. Here, we summarize the advent and development of radiomics, the basic process and challenges in clinical practice, with a focus on applications in pulmonary nodule evaluations, including diagnostics, pathological and molecular classifications, treatment response assessments and prognostic predictions, especially in radiotherapy.