Search Results (10)

Background: Distinguishing tumor recurrence from radiation necrosis after radiotherapy for brain metastases remains a major diagnostic challenge. Perfusion MRI, particularly the measurement of relative cerebral blood volume (rCBV), is a commonly used technique to differentiate between these two entities. However, variations in the placement of regions of interest (ROIs) affect diagnostic accuracy. This study compares the diagnostic performance of different cerebral perfusion methods, including a novel volumetric 3D ROI method and automatic thresholding, to differentiate tumor recurrence from radiation necrosis. Methods: We retrospectively analyzed data from 23 patients, including 25 brain metastases treated with stereotactic radiotherapy, who were suspected of local recurrence and had histological confirmation via biopsy or surgical resection. Each patient underwent perfusion MRI before surgery. The diagnostic performance of the different ROI methods (manual and 3D) was evaluated using the area under the ROC curve (AUC), as well as sensitivity and specificity measures. An automatic thresholding method was also applied, generating tumor sub-volumes with predefined cut-off values to determine the rCBV threshold most specific for differentiating relapse from necrosis. Results: The 3D ROI method, considering the whole lesion and a healthy ROI in the head of the caudate nucleus, demonstrated superior diagnostic performance (AUC = 0.65), outperforming manual methods (AUC = 0.53). Robustness was moderate, with an intraclass correlation coefficient of 0.60 between Syngo.via and IntelliSpace. Conclusions: The 3D ROI method shows promise in improving diagnostic accuracy in distinguishing tumor recurrence from radiation necrosis. Further studies with standardized protocols and larger populations are needed to validate these results. Full article

Background: Focal boost radiotherapy was developed to deliver elevated doses to functional sub-volumes within a target. Such a technique was hypothesized to improve treatment outcomes without increasing toxicity in prostate cancer treatment. Purpose: To summarize and evaluate the efficacy and variability of focal boost radiotherapy by reviewing focal boost planning studies and clinical trials that have been published in the last ten years. Methods: Published reports of focal boost radiotherapy, that specifically incorporate dose escalation to intra-prostatic lesions (IPLs), were reviewed and summarized. Correlations between acute/late ≥G2 genitourinary (GU) or gastrointestinal (GI) toxicity and clinical factors were determined by a meta-analysis. Results: By reviewing and summarizing 34 planning studies and 35 trials, a significant dose escalation to the GTV and thus higher tumor control of focal boost radiotherapy were reported consistently by all reviewed studies. Reviewed trials reported a not significant difference in toxicity between focal boost and conventional radiotherapy. Acute ≥G2 GU and late ≥G2 GI toxicities were reported the most and least prevalent, respectively, and a negative correlation was found between the rate of toxicity and proportion of low-risk or intermediate-risk patients in the cohort. Conclusion: Focal boost prostate cancer radiotherapy has the potential to be a new standard of care. Full article

The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by “in house” simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the “next level” of HNC adaptive radiotherapy (ART). Full article

Intratumoral hypoxia is associated with a poor prognosis and poor response to treatment in head and neck cancers. Its identification would allow for increasing the radiation dose to hypoxic tumor subvolumes. 18F-FMISO PET imaging is the gold standard; however, quantitative multiparametric MRI could show the presence of intratumoral hypoxia. Thus, 16 patients were prospectively included and underwent 18F-FDG PET/CT, 18F-FMISO PET/CT, and multiparametric quantitative MRI (DCE, diffusion and relaxometry T1 and T2 techniques) in the same position before treatment. PET and MRI sub-volumes were segmented and classified as hypoxic or non-hypoxic volumes to compare quantitative MRI parameters between normoxic and hypoxic volumes. In total, 13 patients had hypoxic lesions. The Dice, Jaccard, and overlap fraction similarity indices were 0.43, 0.28, and 0.71, respectively, between the FDG PET and MRI-measured lesion volumes, showing that the FDG PET tumor volume is partially contained within the MRI tumor volume. The results showed significant differences in the parameters of SUV in FDG and FMISO PET between patients with and without measurable hypoxic lesions. The quantitative MRI parameters of ADC, T1 max mapping and T2 max mapping were different between hypoxic and normoxic subvolumes. Quantitative MRI, based on free water diffusion and T1 and T2 mapping, seems to be able to identify intra-tumoral hypoxic sub-volumes for additional radiotherapy doses. Full article

(1) The current literature contains several studies investigating the correlation between dual-energy-derived iodine concentration (IC) and positron emission tomography (PET)-derived Flourodeoxyglucose (¹⁸F-FDG) uptake in patients with non-small-cell lung cancer (NSCLC). In previously published studies, either the entire tumor volume or a region of interest containing the maximum IC or ¹⁸F-FDG was assessed. However, the results have been inconsistent. The objective of this study was to correlate IC with FDG both within the entire volume and regional sub-volumes of primary tumors in patients with NSCLC. (2) In this retrospective study, a total of 22 patients with NSCLC who underwent both dual-energy CT (DE-CT) and ¹⁸F-FDG PET/CT were included. A region of interest (ROI) encircling the entire primary tumor was delineated, and a rigid registration of the DE-CT, iodine maps and FDG images was performed for the ROI. The correlation between tumor measurements and area-specific measurements of IC_peak and the peak standardized uptake value (SUV_peak) was found. Finally, a correlation between tumor volume and the distance between SUV_peak and IC_peak centroids was found. (3) For the entire tumor, moderate-to-strong correlations were found between SUV_max and IC_max (R = 0.62, p = 0.002), and metabolic tumor volume vs. total iodine content (R = 0.91, p < 0.001), respectively. For local tumor sub-volumes, a negative correlation was found between IC_peak and SUV_peak (R = −0.58, p = 0.0046). Furthermore, a strong correlation was found between the tumor volume and the distance in millimeters between SUV_peak and IC_peak centroids (R = 0.81, p < 0.0001). (4) In patients with NSCLC, high FDG uptakes and high DE-CT-derived iodine concentrations correlated on a whole-tumor level, but the peak areas were positioned at different locations within the tumor. ¹⁸F-FDG PET/CT and DE-CT provide complementary information and might represent different underlying patho-physiologies. Full article

In this study, dual PET and contrast enhanced MRI were combined to investigate their correlation per voxel in patients at initial diagnosis with suspected glioblastoma. Correlation with contrast enhancement (CE) as an indicator of BBB leakage was further used to evaluate whether PET signal is likely caused by BBB disruption alone, or rather attributable to specific binding after BBB passage. PET images with [¹⁸F]GE180 and the amino acid [¹⁸F]FET were acquired and normalized to healthy background (tumor-to-background ratio, TBR). Contrast enhanced images were normalized voxel by voxel with the pre-contrast T1-weighted MRI to generate relative CE values (rCE). Voxel-wise analysis revealed a high PET signal even within the sub-volumes without detectable CE. No to moderate correlation of rCE with TBR voxel-values and a small overlap as well as a larger distance of the hotspots delineated in rCE and TBR-PET images were detected. In contrast, voxel-wise correlation between both PET modalities was strong for most patients and hotspots showed a moderate overlap and distance. The high PET signal in tumor sub-volumes without CE observed in voxel-wise analysis as well as the discordant hotspots emphasize the specificity of the PET signals and the relevance of combined differential information from dual PET and MRI images. Full article

Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions. Full article

Tumor hypoxia is associated with radiation resistance and can be longitudinally monitored by ¹⁸F-fluoromisonidazole (¹⁸F-FMISO)-PET/CT. Our study aimed at evaluating radiomics dynamics of ¹⁸F-FMISO-hypoxia imaging during chemo-radiotherapy (CRT) as predictors for treatment outcome in head-and-neck squamous cell carcinoma (HNSCC) patients. We prospectively recruited 35 HNSCC patients undergoing definitive CRT and longitudinal ¹⁸F-FMISO-PET/CT scans at weeks 0, 2 and 5 (W0/W2/W5). Patients were classified based on peritherapeutic variations of the hypoxic sub-volume (HSV) size (increasing/stable/decreasing) and location (geographically-static/geographically-dynamic) by a new objective classification parameter (CP) accounting for spatial overlap. Additionally, 130 radiomic features (RF) were extracted from HSV at W0, and their variations during CRT were quantified by relative deviations (∆_RF). Prediction of treatment outcome was considered statistically relevant after being corrected for multiple testing and confirmed for the two ¹⁸F-FMISO-PET/CT time-points and for a validation cohort. HSV decreased in 64% of patients at W2 and in 80% at W5. CP distinguished earlier disease progression (geographically-dynamic) from later disease progression (geographically-static) in both time-points and cohorts. The texture feature low grey-level zone emphasis predicted local recurrence with AUC_W2 = 0.82 and AUC_W5 = 0.81 in initial cohort (N = 25) and AUC_W2 = 0.79 and AUC_W5 = 0.80 in validation cohort. Radiomics analysis of ¹⁸F-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT. Full article

Quantitative mapping of hyperperfused and hypercellular regions of glioblastoma has been proposed to improve definition of tumor regions at risk for local recurrence following conventional radiation therapy. As the processing of the multiparametric dynamic contrast-enhanced (DCE-) and diffusion-weighted (DW-) magnetic resonance imaging (MRI) data for delineation of these subvolumes requires additional steps that go beyond the standard practices of target definition, we sought to devise a workflow to support the timely planning and treatment of patients. A phase II study implementing a multiparametric imaging biomarker for tumor hyperperfusion and hypercellularity consisting of DCE-MRI and high b-value DW-MRI to guide intensified (75 Gy/30 fractions) radiation therapy (RT) in patients with newly diagnosed glioblastoma was launched. In this report, the workflow and the initial imaging outcomes of the first 12 patients are described. Among all the first 12 patients, treatment was initiated within 6 weeks of surgery and within 2 weeks of simulation. On average, the combined hypercellular volume and high cerebral blood volume/tumor perfusion volume were 1.8 times smaller than the T1 gadolinium abnormality and 10 times smaller than the FLAIR abnormality. Hypercellular volume and high cerebral blood volume/tumor perfusion volume each identified largely distinct regions and showed 57% overlap with the enhancing abnormality, and minimal-to-no extension outside of the FLAIR. These results show the feasibility of implementing a workflow for multiparametric magnetic resonance-guided radiation therapy into clinical trials with a coordinated multidisciplinary team, and the unique and complementary tumor subregions identified by the combination of high b-value DW-MRI and DCE-MRI. Full article

This study aimed to develop an automated model to extract temporal features from DCE-MRI in head-and-neck (HN) cancers to localize significant tumor subvolumes having low blood volume (LBV) for predicting local and regional failure after chemoradiation therapy. Temporal features were extracted from time-intensity curves to build classification model for differentiating voxels with LBV from those with high BV. Support vector machine (SVM) classification was trained on the extracted features for voxel classification. Subvolumes with LBV were then assembled from the classified voxels with LBV. The model was trained and validated on independent datasets created from 456 873 DCE curves. The resultant subvolumes were compared to ones derived by a 2-step method via pharmacokinetic modeling of blood volume, and evaluated for classification accuracy and volumetric similarity by DSC. The proposed model achieved an average voxel-level classification accuracy and DSC of 82% and 0.72, respectively. Also, the model showed tolerance on different acquisition parameters of DCE-MRI. The model could be directly used for outcome prediction and therapy assessment in radiation therapy of HN cancers, or even supporting boost target definition in adaptive clinical trials with further validation. The model is fully automatable, extendable, and scalable to extract temporal features of DCE-MRI in other tumors. Full article