Timothy Pok Chi Yeung

Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment

PURPOSE Stereotactic ablative radiation therapy (SABR) is a guideline-specified treatment option for early-stage lung cancer. However, significant posttreatment fibrosis can occur and obfuscate the detection of local recurrence. The goal of this study was to assess physician ability to detect timely local recurrence and to compare physician performance with a radiomics tool. METHODS AND MATERIALS Posttreatment computed tomography (CT) scans (n=182) from 45 patients treated with SABR (15 with local recurrence matched to 30 with no local recurrence) were used to measure physician and radiomic performance in assessing response. Scans were individually scored by 3 thoracic radiation oncologists and 3 thoracic radiologists, all of whom were blinded to clinical outcomes. Radiomic features were extracted from the same images. Performances of the physician assessors and the radiomics signature were compared. RESULTS When taking into account all CT scans during the whole follow-up period, median sensitivity for physician assessment of local recurrence was 83% (range, 67%-100%), and specificity was 75% (range, 67%-87%), with only moderate interobserver agreement (κ = 0.54) and a median time to detection of recurrence of 15.5 months. When determining the early prediction of recurrence within <6 months after SABR, physicians assessed the majority of images as benign injury/no recurrence, with a mean error of 35%, false positive rate (FPR) of 1%, and false negative rate (FNR) of 99%. At the same time point, a radiomic signature consisting of 5 image-appearance features demonstrated excellent discrimination, with an area under the receiver operating characteristic curve of 0.85, classification error of 24%, FPR of 24%, and FNR of 23%. CONCLUSIONS These results suggest that radiomics can detect early changes associated with local recurrence that are not typically considered by physicians. This decision support system could potentially allow for early salvage therapy of patients with local recurrence after SABR.

Dynamic perfusion CT in brain tumors

Dynamic perfusion CT (PCT) is an imaging technique for assessing the vascular supply and hemodynamics of brain tumors by measuring blood flow, blood volume, and permeability-surface area product. These PCT parameters provide information complementary to histopathologic assessments and have been used for grading brain tumors, distinguishing high-grade gliomas from other brain lesions, differentiating true progression from post-treatment effects, and predicting prognosis after treatments. In this review, the basic principles of PCT are described, and applications of PCT of brain tumors are discussed. The advantages and current challenges, along with possible solutions, of PCT are presented.

Improving Quantitative CT Perfusion Parameter Measurements Using Principal Component Analysis

RATIONALE AND OBJECTIVES To evaluate the improvements in measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS) after principal component analysis (PCA) filtering of computed tomography (CT) perfusion images. To evaluate the improvement in CT perfusion image quality with poor contrast-to-noise ratio (CNR) in vivo. MATERIALS AND METHODS A digital phantom with CT perfusion images reflecting known values of BF, BV, and PS was created and was filtered using PCA. Intraclass correlation coefficients and Bland-Altman analysis were used to assess reliability of measurements and reduction in measurement errors, respectively. Rats with C6 gliomas were imaged using CT perfusion, and the raw CT perfusion images were filtered using PCA. Differences in CNR, BF, BV, and PS before and after PCA filtering were assessed using repeated measures analysis of variance. RESULTS From simulation, mean errors decreased from 12.8 (95% confidence interval [CI] = -19.5 to 45.0) to 1.4 mL/min/100 g (CI = -27.6 to 30.4), 0.2 (CI = -1.1 to 1.4) to -0.1 mL/100 g (CI = -1.1 to 0.8), and 2.9 (CI = -2.4 to 8.1) to 0.2 mL/min/100 g (CI = -3.5 to 3.9) for BF, BV, and PS, respectively. Map noise in BF, BV, and PS were decreased from 51.0 (CI = -3.5 to 105.5) to 11.6 mL/min/100 g (CI = -7.9 to 31.2), 2.0 (CI = 0.7 to 3.3) to 0.5 mL/100 g (CI = 0.1 to 1.0), and 8.3 (CI = -0.8 to 17.5) to 1.4 mL/min/100 g (CI = -0.4 to 3.1), respectively. For experiments, CNR significantly improved with PCA filtering in normal brain (P < .05) and tumor (P < .05). Tumor and brain BFs were significantly different from each other after PCA filtering with four principal components (P < .05). CONCLUSIONS PCA improved image CNR in vivo and reduced the measurement errors of BF, BV, and PS from simulation. A minimum of four principal components is recommended.

Relationship of computed tomography perfusion and positron emission tomography to tumour progression in malignant glioma.

This study aimed to explore the potential for computed tomography (CT) perfusion and 18‐Fluorodeoxyglucose positron emission tomography (FDG‐PET) in predicting sites of future progressive tumour on a voxel‐by‐voxel basis after radiotherapy and chemotherapy.

CT Perfusion Imaging as an Early Biomarker of Differential Response to Stereotactic Radiosurgery in C6 Rat Gliomas

Background The therapeutic efficacy of stereotactic radiosurgery for glioblastoma is not well understood, and there needs to be an effective biomarker to identify patients who might benefit from this treatment. This study investigated the efficacy of computed tomography (CT) perfusion imaging as an early imaging biomarker of response to stereotactic radiosurgery in a malignant rat glioma model. Methods Rats with orthotopic C6 glioma tumors received either mock irradiation (controls, N = 8) or stereotactic radiosurgery (N = 25, 12 Gy in one fraction) delivered by Helical Tomotherapy. Twelve irradiated animals were sacrificed four days after stereotactic radiosurgery to assess acute CT perfusion and histological changes, and 13 irradiated animals were used to study survival. Irradiated animals with survival >15 days were designated as responders while those with survival ≤15 days were non-responders. Longitudinal CT perfusion imaging was performed at baseline and regularly for eight weeks post-baseline. Results Early signs of radiation-induced injury were observed on histology. There was an overall survival benefit following stereotactic radiosurgery when compared to the controls (log-rank P<0.04). Responders to stereotactic radiosurgery showed lower relative blood volume (rBV), and permeability-surface area (PS) product on day 7 post-stereotactic radiosurgery when compared to controls and non-responders (P<0.05). rBV and PS on day 7 showed correlations with overall survival (P<0.05), and were predictive of survival with 92% accuracy. Conclusions Response to stereotactic radiosurgery was heterogeneous, and early selection of responders and non-responders was possible using CT perfusion imaging. Validation of CT perfusion indices for response assessment is necessary before clinical implementation.

Quantitative Perfusion and Permeability Biomarkers in Brain Cancer from Tomographic CT and MR Images

Dynamic contrast-enhanced perfusion and permeability imaging, using computed tomography and magnetic resonance systems, are important techniques for assessing the vascular supply and hemodynamics of healthy brain parenchyma and tumors. These techniques can measure blood flow, blood volume, and blood-brain barrier permeability surface area product and, thus, may provide information complementary to clinical and pathological assessments. These have been used as biomarkers to enhance the treatment planning process, to optimize treatment decision-making, and to enable monitoring of the treatment noninvasively. In this review, the principles of magnetic resonance and computed tomography dynamic contrast-enhanced perfusion and permeability imaging are described (with an emphasis on their commonalities), and the potential values of these techniques for differentiating high-grade gliomas from other brain lesions, distinguishing true progression from posttreatment effects, and predicting survival after radiotherapy, chemotherapy, and antiangiogenic treatments are presented.

A generalized parametric response mapping method for analysis of multi‐parametric imaging: A feasibility study with application to glioblastoma

Purpose: Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well‐suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub‐volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single‐parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi‐parametric data while maintaining the key advantages of the original PRM method. Methods: MRI‐derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3‐months post‐RT for 19 patients with high‐grade glioma were used to demonstrate the algorithm. Images were first co‐registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four‐dimensional Cartesian space with coordinate values equal to a voxels image intensity in each of the image volumes and an origin defined as the multi‐parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre‐determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non‐responding patients within a training dataset. Voxel classifications were visualized via familiar three‐class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast‐enhancing lesion (CEL) and a 1 cm shell of surrounding peri‐tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave‐one‐out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P‐values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months. Results: Single‐parameter PRM and multi‐parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single‐parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri‐tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01). Conclusions: The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single‐parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi‐parametric response into consideration and enables visualization. MPRM analysis of peri‐tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset.

Radiomics versus physician assessment for the early prediction of local cancer recurrence after stereotactic radiotherapy for lung cancer

Stereotactic ablative radiotherapy (SABR) has recently become a standard treatment option for patients with early-stage lung cancer, which achieves local control rates similar to surgery. Local recurrence following SABR typically presents after one year post-treatment. However, benign radiological changes mimicking local recurrence can appear on CT imaging following SABR, complicating the assessment of response. We hypothesize that subtle changes on early post- SABR CT images are important in predicting the eventual incidence of local recurrence and would be extremely valuable to support timely salvage interventions. The objective of this study was to extract radiomic image features on post-SABR follow-up images for 45 patients (15 with local recurrence and 30 without) to aid in the early prediction of local recurrence. Three blinded thoracic radiation oncologists were also asked to score follow-up images as benign injury or local recurrence. A radiomic signature consisting of five image features demonstrated a classification error of 24%, false positive rate (FPR) of 24%, false negative rate (FNR) of 23%, and area under the receiver operating characteristic curve (AUC) of 0.85 at 2–5 months post-SABR. At the same time point, three physicians assessed the majority of images as benign injury for overall errors of 34–37%, FPRs of 0–4%, and FNRs of 100%. These results suggest that radiomics can detect early changes associated with local recurrence which are not typically considered by physicians. We aim to develop a decision support system which could potentially allow for early salvage therapy of patients with local recurrence following SABR.

Evaluation of CT Perfusion Biomarkers of Tumor Hypoxia.

Background Tumor hypoxia is associated with treatment resistance to cancer therapies. Hypoxia can be investigated by immunohistopathologic methods but such procedure is invasive. A non-invasive method to interrogate tumor hypoxia is an attractive option as such method can provide information before, during, and after treatment for personalized therapies. Our study evaluated the correlations between computed tomography (CT) perfusion parameters and immunohistopathologic measurement of tumor hypoxia. Methods Wistar rats, 18 controls and 19 treated with stereotactic radiosurgery (SRS), implanted with the C6 glioma tumor were imaged using CT perfusion on average every five days to monitor tumor growth. A final CT perfusion scan and the brain were obtained on average 14 days (8–22 days) after tumor implantation. Tumor hypoxia was detected immunohistopathologically with pimonidazole. The tumor, necrotic, and pimonidazole-positive areas on histology samples were measured. Percent necrotic area and percent hypoxic areas were calculated. Tumor volume (TV), blood flow (BF), blood volume (BV), and permeability-surface area product (PS) were obtained from the CT perfusion studies. Correlations between CT perfusion parameters and histological parameters were assessed by Spearman’s ρ correlation. A Bonferroni-corrected P value < 0.05 was considered significant. Results BF and BV showed significant correlations with percent hypoxic area ρ = -0.88, P < 0.001 and ρ = -0.81, P < 0.001, respectively, for control animals and ρ = -0.7, P < 0.001 and ρ = -0.6, P = 0.003, respectively, for all animals, while TV and BV were correlated (ρ = -0.64, P = 0.01 and ρ = -0.43, P = 0.043, respectively) with percent necrotic area. PS was not correlated with either percent necrotic or percent hypoxic areas. Conclusions Percent hypoxic area provided significant correlations with BF and BV, suggesting that CT perfusion parameters are potential non-invasive imaging biomarkers of tumor hypoxia.

Can parametric response maps predict voxel-wise treatment response? Implications for locally adaptive radiotherapy.

Parametric response map (PRM) analysis is a voxel-based image analysis method for predicting treatment response which shows promise as a means for guiding locally adaptive radiotherapy (RT). However to date, studies have focused on verifying PRM predictive utility with respect to global outcomes such as overall survival (OS). Here we investigated whether voxel-wise treatment response information can be inferred from a PRM analysis that has been correlated with OS. PRMs were generated from repeat MRI-derived apparent diffusion coefficient (ADC) maps (1 and 3 months post-RT) for n = 14 patients treated for high-grade glioblastoma. The proportion of voxels in each PRM class that remained within the tumor boundary at 6 months post-RT was computed. Voxels classified as significantly increasing in ADC were more likely to remain within the boundaries of the tumor at 6 months post- RT compared to voxels classified as significantly decreasing in ADC (p < 0.001). However in contrast, the fractional tumor volume classified as significantly increasing in ADC was positively correlated with OS (ρ = 0.63, p = 0.02). The PRM was found to show potential for predicting both global and voxelwise treatment response, however, the relationship between the two could not be directly inferred suggesting that rigorous validation is needed if the PRM is to be used to guide locally adaptive RT.