Investigation of the diffusion abnormality index as a new imaging biomarker for early assessment of brain tumor response to radiation therapy

Dynamics of Daily Glioblastoma Evolution During Chemoradiation Therapy on the 0.35T Magnetic Resonance Imaging-Linear Accelerator

PURPOSE

Glioblastoma changes during chemoradiation therapy are inferred from magnetic resonance imaging (MRI) before and after treatment but are rarely investigated due to logistics of frequent MRI. Using a combination MRI-linear accelerator (MRI-linac), we evaluated changes during daily chemoradiation therapy.

METHODS AND MATERIALS

Patients with glioblastoma were prospectively imaged daily during chemoradiation therapy on 0.35T MRI-linac and at 3 timepoints with and without contrast on standalone high-field MRI. Tumor or edema (lesion) and resection cavity dynamics throughout treatment were analyzed and compared with standalone T1 postcontrast (T1+C) and T2 volumes.

RESULTS

Of 36 patients included in this analysis, 8 had cavity only, 12 had lesion only, and 16 had both cavity and lesion. Of these, 64% had lesion growth and 46% had cavity shrinkage during treatment on MRI-linac scans. The average MRI-linac migration distance was 1.3 cm (range, 0-4.1 cm) for lesion and 0.6 cm (range, 0.1-2.1 cm) for cavity. Standalone versus MRI-linac volumes correlated strongly with R2 values: 0.991 (T2 vs MRI-linac cavity), 0.972 (T1+C vs MRI-linac cavity), and 0.973 (T2 vs MRI-linac lesion). There was a moderate correlation between T1+C and MRI-linac lesion (R2 = 0.609), despite noncontrast MRI-linac inability to separate contrast enhancement from surrounding nonenhancing tumor and edema. From pretreatment to posttreatment in patients with all available scans (n = 35), T1+C and MRI-linac lesions changed together-shrank (n = 6), grew (n = 12), or unchanged (n = 8)-in 26 (74%) patients. Another 9 patients (26%) had growth on MRI-linac, although the T1+C component shrank. In no patient did T1+C lesion grow while MRI-linac lesion shrank.

CONCLUSIONS

Anatomic changes are seen in patients with glioblastoma imaged daily on MRI-linac throughout the chemoradiation therapy course. As surgical resection cavities shrink, margins may be reduced to save normal brain. Patients with unresected or growing lesions may require margin expansions to cover changes. Limited volume glioblastoma boost trials could consider triggered gadolinium contrast administration for evaluation of adaptive radiation therapy when lesion growth is seen on noncontrast MRI-linac.

Treatment Planning Methods for Dose Painting by Numbers Treatment in Gamma Knife Radiosurgery

Purpose

Dose painting radiation therapy delivers a nonuniform dose to tumors to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife machines making large-volume brain tumor treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on stereotactic radiosurgery (SRS) plan quality has not yet been studied. This research investigates the plan quality achievable for Gamma Knife SRS dose painting treatments when using optimization techniques and automated isocenter placement in treatment planning.

Methods and Materials

Dose painting prescription functions with varying parameters were applied to convert voxel image intensities to prescriptions for 10 sample cases. To study achievable plan quality and optimization, clinically placed isocenters were used with each dose painting prescription and optimized using a semi-infinite linear programming formulation. To study automated isocenter placement, a grassfire sphere-packing algorithm and a clinically available Leksell gamma plan isocenter fill algorithm were used. Plan quality for each optimized treatment plan was measured with dose painting SRS metrics.

Results

Optimization can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show more achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocenter placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocenters improves plan quality. The computational solve time for optimization is within 5 minutes in most cases, which is suitable for clinical planning.

Conclusions

The impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimization and automated isocenter placement are shown as possible treatment planning methods to obtain high quality plans.

Plan Assessment Metrics for Dose Painting in Stereotactic Radiosurgery

Purpose

As radiation therapy treatment precision increases with advancements in imaging and radiation delivery, dose painting treatment becomes increasingly feasible, where targets receive a nonuniform radiation dose. The high precision of stereotactic radiosurgery (SRS) makes it a good candidate for dose painting treatments, but no suitable metrics to assess dose painting SRS plans exist. Existing dose painting assessment metrics weigh target overdose and underdose equally but are unsuited for SRS plans, which typically avoid target underdose more. Current SRS metrics also prioritize reducing healthy tissue dose through selectivity and dose fall-off, and these metrics assume single prescriptions. We propose a set of metrics for dose painting SRS that would meet clinical needs and are calculated with nonuniform dose painting prescriptions.

Methods and Materials

Sample dose painting SRS prescriptions are first created from Gamma Knife SRS cases, apparent diffusion coefficient magnetic resonance images, and various image-to-prescription functions. Treatment plans are found through semi-infinite linear programming optimization and using clinically determined isocenters, then assessed with existing and proposed metrics. Modified versions of SRS metrics are proposed, including coverage, selectivity, conformity, efficiency, and gradient indices. Quality factor, a current dose painting metric, is applied both without changes and with modifications. A new metric, integral dose ratio, is proposed as a measure of target overdose.

Results

The merits of existing and modified metrics are demonstrated and discussed. A modified conformity index using mean or minimum prescription dose would be suitable for dose painting SRS with integral or maximum boost methods, respectively. Either modified efficiency index is a suitable replacement for the existing gradient index.

Conclusions

The proposed modified SRS metrics are appropriate measures of plan quality for dose painting SRS plans and have the advantage of giving equal values as the original SRS metrics when applied to single-prescription plans.

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma

Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.35T MRI-linac. Tumor and edema (tumor lesion) and resection cavity kinetics throughout the treatment were manually segmented on these daily MRI. Utilizing a convolutional neural network, an automatic segmentation deep learning network was built. A nine-fold cross-validation schema was used to train the network using 80:10:10 for training, validation, and testing. Thirty-six glioblastoma patients were imaged pre-treatment and 30 times during radiotherapy (n = 31 volumes, total of 930 MRIs). The average tumor lesion and resection cavity volumes were 94.56 ± 64.68 cc and 72.44 ± 35.08 cc, respectively. The average Dice similarity coefficient between manual and auto-segmentation for tumor lesion and resection cavity across all patients was 0.67 and 0.84, respectively. This is the first brain lesion segmentation network developed for MRI-linac. The network performed comparably to the only other published network for auto-segmentation of post-operative glioblastoma lesions. Segmented volumes can be utilized for adaptive radiotherapy and propagated across multiple MRI contrasts to create a prognostic model for glioblastoma based on multiparametric MRI.

A "Seed-and-Soil" Radiomics Model Predicts Brain Metastasis Development in Lung Cancer: Implications for Risk-Stratified Prophylactic Cranial Irradiation

Introduction: Brain is a major site of metastasis for lung cancer, and effective therapy for developed brain metastasis (BM) is limited. Prophylactic cranial irradiation (PCI) has been shown to reduce BM rate and improve survival in small cell lung cancer, but this result was not replicated in unselected non-small cell lung cancer (NSCLC) and had the risk of inducing neurocognitive dysfunctions. We aimed to develop a radiomics BM prediction model for BM risk stratification in NSCLC patients. Methods: 256 NSCLC patients with no BM at baseline brain magnetic resonance imaging (MRI) were selected; 128 patients developed BM within three years after diagnosis and 128 remained BM-free. For radiomics analysis, both the BM and non-BM groups were randomly distributed into training and testing datasets at an 70%:30% ratio. Both brain MRI (representing the soil) and chest computed tomography (CT, representing the seed) radiomic features were extracted to develop the BM prediction models. We first developed the radiomic models using the training dataset (89 non-BM and 90 BM cases) and subsequently validated the models in the testing dataset (39 non-BM and 38 BM cases). A radiomics BM score (RadBM score) was generated, and BM-free survival were compared between RadBM score-high and RadBM score-low groups. Results: The radiomics model developed from baseline brain MRI features alone can predict BM development in NSCLC patients. A fusion model integrating brain MRI features with primary tumor CT features (seed-and-soil model) provided synergetic effect and was more efficient in predicting BM (areas under the receiver operating characteristic curve 0.84 (95% confidence interval: 0.80−0.89) and 0.80 (95% confidence interval: 0.71−0.88) in the training and testing datasets, respectively). BM-free survival was significantly shorter in the RadBM score-high group versus the RadBM score-low group (Log-rank, p < 0.001). Hazard ratios for BM were 1.056 (95% confidence interval: 1.044−1.068) per 0.01 increment in RadBM score. Cumulative BM rates at three years were 75.8% and 24.2% for the RadBM score-high and RadBM score-low groups, respectively. Only 1.2% (7/565) of the BM lesions were located within the hippocampal avoidance region. Conclusion: The results demonstrated that intrinsic features of a non-metastatic brain exert a significant impact on BM development, which is first-in-class in metastasis prediction studies. A radiomics BM prediction model utilizing both primary tumor and pre-metastatic brain features might provide a useful tool for individualized PCI administration in NSCLC patients more prone to develop BM.

Clinical Biomarkers for Early Identification of Patients with Intracranial Metastatic Disease

Simple Summary

The development of brain metastases, or intracranial metastatic disease (IMD), is a serious and life-altering complication for many patients with cancer. While there have been substantial advancements in the treatments available for IMD and in our understanding of its pathogenesis, conventional methods remain insufficient to detect IMD at an early stage. In this review, we discuss current research on biomarkers specific to IMD. In particular, we highlight biomarkers that can be easily accessed via the bloodstream or cerebrospinal fluid, including circulating tumor cells and DNA, as well as advanced imaging techniques. The continued development of these assays could enable clinicians to detect IMD prior to the development of IMD-associated symptoms and ultimately improve patient prognosis and survival.

Abstract

Nearly 30% of patients with cancer will develop intracranial metastatic disease (IMD), and more than half of these patients will die within a few months following their diagnosis. In light of the profound effect of IMD on survival and quality of life, there is significant interest in identifying biomarkers that could facilitate the early detection of IMD or identify patients with cancer who are at high IMD risk. In this review, we will highlight early efforts to identify biomarkers of IMD and consider avenues for future investigation.

Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma

BACKGROUND

Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma.

METHODS

We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM.

RESULTS

The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), _T2WIKurtosis (p = 0.010), _DWIMode (p = 0.038), _DWICV (p = 0.038), and _T2-mapP5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%.

CONCLUSION

The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer.

The centrally restricted diffusion sign on MRI for assessment of radiation necrosis in metastases treated with stereotactic radiosurgery

Purpose

Differentiation of radiation necrosis from tumor progression in brain metastases treated with stereotactic radiosurgery (SRS) is challenging. For this, we assessed the performance of the centrally restricted diffusion sign.

Methods

Patients with brain metastases treated with SRS who underwent a subsequent intervention (biopsy/resection) for a ring-enhancing lesion on preoperative MRI between 2000 and 2020 were included. Excluded were lesions containing increased susceptibility limiting assessment of DWI. Two neuroradiologists classified the location of the diffusion restriction with respect to the post-contrast T1 images as centrally within the ring-enhancement (the centrally restricted diffusion sign), peripherally correlating to the rim of contrast enhancement, both locations, or none. Measures of diagnostic accuracy and 95% CI were calculated for the centrally restricted diffusion sign. Cohen's kappa was calculated to identify the interobserver agreement.

Results

Fifty-nine patients (36 female; mean age 59, range 40 to 80) were included, 36 with tumor progression and 23 with radiation necrosis based on histopathology. Primary tumors included 34 lung, 12 breast, 5 melanoma, 3 colorectal, 2 esophagus, 1 head and neck, 1 endometrium, and 1 thyroid. The centrally restricted diffusion sign was seen in 19/23 radiation necrosis cases (sensitivity 83% (95% CI 63 to 93%), specificity 64% (95% CI 48 to 78%), PPV 59% (95% CI 42 to 74%), NPV 85% (95% CI 68 to 94%)) and 13/36 tumor progression cases (difference p < 0.001). Interobserver agreement was substantial, at 0.61 (95% CI 0.45 to 70.8).

Conclusion

We found a low probability of radiation necrosis in the absence of the centrally restricted diffusion sign.

Imaging challenges of immunotherapy and targeted therapy in patients with brain metastases: response, progression, and pseudoprogression

The advent of immunotherapy using immune checkpoint inhibitors (ICIs) and targeted therapy (TT) has dramatically improved the prognosis of various cancer types. However, following ICI therapy or TT-either alone (especially ICI) or in combination with radiotherapy-imaging findings on anatomical contrast-enhanced MRI can be unpredictable and highly variable, and are often difficult to interpret regarding treatment response and outcome. This review aims at summarizing the imaging challenges related to TT and ICI monotherapy as well as combined with radiotherapy in patients with brain metastases, and to give an overview on advanced imaging techniques which potentially overcome some of these imaging challenges. Currently, major evidence suggests that imaging parameters especially derived from amino acid PET, perfusion-/diffusion-weighted MRI, or MR spectroscopy may provide valuable additional information for the differentiation of treatment-induced changes from brain metastases recurrence and the evaluation of treatment response.

Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy

MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.

Role and future of MRI in radiation oncology

Technical innovations and developments in areas such as disease localization, dose calculation algorithms, motion management and dose delivery technologies have revolutionized radiation therapy resulting in improved patient care with superior outcomes. A consequence of the ability to design and accurately deliver complex radiation fields is the need for improved target visualization through imaging. While CT imaging has been the standard of care for more than three decades, the superior soft tissue contrast afforded by MR has resulted in the adoption of this technology in radiation therapy. With the development of real time MR imaging techniques, the problem of real time motion management is enticing. Currently, the integration of an MR imaging and megavoltage radiation therapy treatment delivery system (MR-linac or MRL) is a reality that has the potential to provide improved target localization and real time motion management during treatment. Higher magnetic field strengths provide improved image quality potentially providing the backbone for future work related to image texture analysis-a field known as Radiomics-thereby providing meaningful information on the selection of future patients for radiation dose escalation, motion-managed treatment techniques and ultimately better patient care. On-going advances in MRL technologies promise improved real time soft tissue visualization, treatment margin reductions, beam optimization, inhomogeneity corrected dose calculation, fast multileaf collimators and volumetric arc radiation therapy. This review article provides rationale, advantages and disadvantages as well as ideas for future research in MRI related to radiation therapy mainly in adoption of MRL.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

MR-guided radiation therapy: transformative technology and its role in the central nervous system

This review article describes advancement of magnetic resonance imaging technologies in radiation therapy planning, guidance, and adaptation of brain tumors. The potential for MR-guided radiation therapy to improve outcomes and the challenges in its adoption are discussed.

Proton Therapy for Juvenile Pilocytic Astrocytoma: Quantifying Treatment Responses by Magnetic Resonance Diffusion Tensor Imaging

Purpose

Proton therapy is increasingly used to treat pediatric brain tumors. However, the response of both tumors and healthy tissues to proton therapy is currently under investigation. One way of assessing this response is magnetic resonance (MR) diffusion tensor imaging (DTI), which can measure molecular mobility at the cellular level, quantified by the apparent diffusion coefficient (ADC). In addition, DTI may reveal axonal fiber directional information in white matter, quantified by fractional anisotropy (FA). Here we report use of DTI to assess tumor and unexposed healthy brain tissue responses in a child who received proton therapy for juvenile pilocytic astrocytoma.

Materials and Methods

A 10-year-old boy with recurrent juvenile pilocytic astrocytoma of the left thalamus received proton therapy to a dose of 50.4Gy (RBE) in 28 fractions. Functional magnetic resonance imaging was used to select beam angles for treatment planning. Over the course of the 7-year follow-up period, magnetic resonance imaging including DTI was done to assess response. The MR images were registered to the treatment-planning computed tomography scan, and the gross tumor volume (GTV) was mapped onto the MR images at each follow-up. The GTV contour was then mirrored to the right side of brain through the midline to represent unexposed healthy brain tissue.

Results

Proton therapy delivered the full prescribed dose to the target while completely sparing the contralateral brain. The MR ADC images obtained before and after proton therapy showed that enhancement corresponding to the GTV had nearly disappeared by 25 months. The ADC and FA measurements confirmed that contralateral healthy brain tissue was not affected, and the GTV reverted to clinically normal ADC and FA values.

Conclusion

Use of DTI allowed quantitative evaluation of tumor and healthy brain tissue responses to proton therapy.

A longitudinal magnetic resonance elastography study of murine brain tumors following radiation therapy

An accurate and noninvasive method for assessing treatment response following radiotherapy is needed for both treatment monitoring and planning. Measurement of solid tumor volume alone is not sufficient for reliable early detection of therapeutic response, since changes in physiological and/or biomechanical properties can precede tumor volume change following therapy. In this study, we use magnetic resonance elastography to evaluate the treatment effect after radiotherapy in a murine brain tumor model. Shear modulus was calculated and compared between the delineated tumor region of interest (ROI) and its contralateral, mirrored counterpart. We also compared the shear modulus from both the irradiated and non-irradiated tumor and mirror ROIs longitudinally, sampling four time points spanning 9-19 d post tumor implant. Results showed that the tumor ROI had a lower shear modulus than that of the mirror ROI, independent of radiation. The shear modulus of the tumor ROI decreased over time for both the treated and untreated groups. By contrast, the shear modulus of the mirror ROI appeared to be relatively constant for the treated group, while an increasing trend was observed for the untreated group. The results provide insights into the tumor properties after radiation treatment and demonstrate the potential of using the mechanical properties of the tumor as a biomarker. In future studies, more closely spaced time points will be employed for detailed analysis of the radiation effect.

Biological imaging in clinical oncology: radiation therapy based on functional imaging

Radiation therapy is one of the most effective tools for cancer treatment. In recent years, intensity-modulated radiation therapy has become increasingly popular in that target dose-escalation can be done while sparing adjacent normal tissues. For this reason, the development of measures to pave the way for accurate target delineation is of great interest. With the integration of functional information obtained by biological imaging with radiotherapy, strategies using advanced biological imaging to visualize metabolic pathways and to improve therapeutic index and predict treatment response are discussed in this article.

Predicting liver metastasis of gastrointestinal tract cancer by diffusion-weighted imaging of apparent diffusion coefficient values

AIM: To determine if efficacy of chemotherapy on liver metastasis of gastrointestinal tract cancer can be predicted by apparent diffusion coefficient (ADC) values of diffusion-weighted imaging (DWI).

METHODS: In total, 86 patients with liver metastasis of gastrointestinal tract cancer (156 metastatic lesions) diagnosed in our hospital were included in this study. The maximum diameters of these tumors were compared with each other before treatment, 2 wk after treatment, and 12 wk after treatment. Selected patients were classified as the effective group and the ineffective group, depending on the maximum diameter of the tumor after 12 wk of treatment; and the ADC values at different treatment times between the two groups were compared. Spearman rank correlation was used to analyze the relationship between ADC value and tumor diameter. Receiver operating characteristic curve (ROC curve) was used to analyze the ADC values before treatment to predict the patient’s sensitivity and specificity degree of efficacy to the chemotherapy.

RESULTS: There was no difference in age between the two groups and in maximum tumor diameter before treatment and 2 wk after treatment. However, after 12 wk of treatment, maximum tumor diameter in the effective group was significantly lower than that in the ineffective group (P < 0.05). Before treatment, ADC values in the ineffective group were significantly higher than those in the effective group (P < 0.05). There was no difference in ADC values between the effective and ineffective groups after 2 and 12 wk of treatment. However, ADC values were significantly higher after 2 and 12 wk of treatment compared to before treatment in the effective group (P < 0.05). Spearman rank correlation analysis showed that ADC value before treatment and the reduced percentage of the maximum tumor diameter after 12 wk of treatment were negatively correlated, while the increase in the percentage of the ADC value 12 wk after treatment and the decrease in the percentage of the maximum tumor diameter were significantly positively correlated. The results of the ROC curve showed that ADC value with a chemotherapy ineffective threshold value of 1.14 × 10^-3 mm²/s before treatment had a sensitivity and specificity of 94.3% and 76.7%, respectively.

CONCLUSION: DWI ADC values can be used to predict the response of patients with liver metastasis of gastrointestinal tract cancer to chemotherapy with high sensitivity and relatively high specificity.

The predictive capacity of apparent diffusion coefficient (ADC) in response assessment of brain metastases following radiation

To investigate the predictive capacity of the apparent diffusion coefficient (ADC) as a biomarker of radiation response in brain metastases. Seventy brain metastases from 42 patients treated with either stereotactic radiosurgery or whole brain radiotherapy were imaged at baseline, 1 week, and 1 month post-treatment using diffusion-weighted MRI. Mean and median relative ADC for metastases was calculated by normalizing ADC measurements to baseline ADC. At 1 year post-treatment, or last available follow-up MRI, volume criteria determined final tumour response status. Uni- and multivariate analysis was used to account for factors associated with tumour response at 1 week and 1 month. A generalized estimating equations model took into consideration multiple tumours per subject. Optimal thresholds that distinguished responders from non-responders, as well as sensitivity and specificity were determined by receiver operator characteristic analysis and Youden's index. Lower relative ADC values distinguished responders from non-responders at 1 week and 1 month (P < 0.05). Optimal cut-off values for response were 1.060 at 1 week with a sensitivity and specificity of 75.0 and 56.3 %, respectively. At 1 month, the cut-off was 0.971 with a sensitivity and specificity of 70.0 and 68.8 %, respectively. A multivariate general estimating equations analysis identified no prior radiation [odds ratio (OR) 0.211 and 0.137, P = 0.033 and 0.0177], and a lower median relative ADC at 1 week and 1 month (OR 0.619 and 0.694, P = 0.0036 and 0.005), as predictors of tumour response. Lower relative ADC values at 1 week and 1 month following radiation distinguished responders from non-responders and may be a promising biomarker of early radiation response.

Unsanctifying the sanctuary: challenges and opportunities with brain metastases

While the use of targeted therapies, particularly radiosurgery, has broadened therapeutic options for CNS metastases, patients respond minimally and prognosis remains poor. The inability of many systemic chemotherapeutic agents to penetrate the blood-brain barrier (BBB) has limited their use and allowed brain metastases to become a burgeoning clinical challenge. Adequate preclinical models that appropriately mimic the metastatic process, the BBB, and blood-tumor barriers (BTB) are needed to better evaluate therapies that have the ability to enhance delivery through or penetrate into these barriers and to understand the mechanisms of resistance to therapy. The heterogeneity among and within different solid tumors and subtypes of solid tumors further adds to the difficulties in determining the most appropriate treatment approaches and methods of laboratory and clinical studies. This review article discusses therapies focused on prevention and treatment of CNS metastases, particularly regarding the BBB, and the challenges and opportunities these therapies present.

Clinical applications for diffusion magnetic resonance imaging in radiotherapy

In this article, we review the clinical applications of diffusion magnetic resonance imaging (MRI) in the radiotherapy treatment of several key clinical sites, including those of the central nervous system, the head and neck, the prostate, and the cervix. Diffusion-weighted MRI (DWI) is an imaging technique that is rapidly gaining widespread acceptance owing to its ease and wide availability. DWI measures the mobility of water within tissue at the cellular level without the need of any exogenous contrast agent. For radiotherapy treatment planning, DWI improves upon conventional imaging techniques, by better characterization of tumor tissue properties required for tumor grading, diagnosis, and target volume delineation. Because DWI is also a sensitive marker for alterations in tumor cellularity, it has potential clinical applications in the early assessment of treatment response following radiation therapy.