An automatic deep learning-based workflow for glioblastoma survival prediction using pre-operative multimodal MR images

We proposed a fully automatic workflow for glioblastoma (GBM) survival prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade glioma) patients were included. 163 of the GBM patients had overall survival (OS) data. Every patient had four pre-operative MR scans and manually drawn tumor contours. For automatic tumor segmentation, a 3D convolutional neural network (CNN) was trained and validated using 122 glioma patients. The trained model was applied to the remaining 163 GBM patients to generate tumor contours. The handcrafted and DL-based radiomic features were extracted from auto-contours using explicitly designed algorithms and a pre-trained CNN respectively. 163 GBM patients were randomly split into training (n=122) and testing (n=41) sets for survival analysis. Cox regression models with regularization techniques were trained to construct the handcrafted and DL-based signatures. The prognostic power of the two signatures was evaluated and compared. The 3D CNN achieved an average Dice coefficient of 0.85 across 163 GBM patients for tumor segmentation. The handcrafted signature achieved a C-index of 0.64 (95% CI: 0.55-0.73), while the DL-based signature achieved a C-index of 0.67 (95% CI: 0.57-0.77). Unlike the handcrafted signature, the DL-based signature successfully stratified testing patients into two prognostically distinct groups (p-value<0.01, HR=2.80, 95% CI: 1.26-6.24). The proposed 3D CNN generated accurate GBM tumor contours from four MR images. The DL-based signature resulted in better GBM survival prediction, in terms of higher C-index and significant patient stratification, than the handcrafted signature. The proposed automatic radiomic workflow demonstrated the potential of improving patient stratification and survival prediction in GBM patients

The Clinical Development of Prostate Magnetic Resonance Imaging-Only Simulation for Radiation Therapy

Magnetic resonance imaging-only (MRI-only) simulation for external beam radiation therapy treatment planning of prostate cancer has seen increased clinical use. The use of a single imaging modality for simulation imaging brings benefits to radiation therapy workflows such as the elimination of systematic positional errors associated with multimodal image registration during treatment planning. However, several challenges remain for the widespread clinical adoption of MRI-only simulation imaging for radiation therapy such as the lack of robust pre-treatment alignment methods and dedicated quality assurance testing equipment. In the MRI-only simulation imaging workflow, synthetic computed tomography (CT) images are created for a variety of uses including providing tissue electron density information for dose calculations. Synthetic CT image generation algorithms are typically trained using patient data and are highly sensitive to human tissue contrast and geometry. Most institutions that treat patients with MRI-only simulation images cannot use commercially available phantoms to quality assurance test processes such as synthetic CT image generation. This is because most commercially available phantoms do not mimic human tissue geometry and tissue imaging characteristics for both MRI/CT modalities. The absence of MRI/CT compatible end-to-end quality assurance testing instruments could potentially lead to systematic errors in treatments using MRI-only simulation imaging because of the lack of imaging and dosimetric benchmarking standards.Studies on the commissioning of MRI-only simulation imaging for radiation therapy of prostate cancers have recommended the use of intraprostatic fiducial markers for pre-treatment patient positioning and alignment. However, fiducial markers appear as dark signal voids in MRI and are challenging to manually localize without the aid of CT imaging. Other intraprostatic objects such as calcifications produce similar signal voids to fiducial markers in MRI images. There is currently no consensus on the optimal fiducial marker or MRI sequence to detect fiducial markers with a high level of sensitivity and specificity in MRI-only simulation images. Additionally, there are no clinically available automatic marker detection workflows available to aid in the clinical transition to MRI-only simulation imaging. This thesis presents work undertaken to meet the challenges of the clinical development of MRI-only simulation imaging for radiation therapy of prostate cancers. In the presented work, the author describes the development of a novel system of multimodal tissue mimicking materials for MRI and CT imaging. The aforementioned system of materials was adapted into a novel 3D-printed anthropomorphic phantom for quality assurance testing of MRI-only simulation procedures. To address the issues with patient positioning and alignment, a human and phantom study was conducted to quantitatively evaluate the optimal fiducial marker and MRI sequence for patients receiving MRI-only radiation therapy simulation imaging. Finally, an automatic deep-learning based fiducial marker detection algorithm is presented to aid with the clinical transition of CT-based to MRI-only radiation therapy simulation workflow

Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.

BACKGROUND: Magnetic resonance image only (MRI-only) simulation for head and neck (H&amp;N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomography (sCT) is generated from MRI to provide electron density information for dose calculation. Bone/air regions produce little MRI signal which could lead to electron density misclassification in sCT. Establishing the dosimetric impact of this error could inform quality assurance (QA) procedures using MRI-only RT planning or compensatory methods for accurate dosimetric calculation. PURPOSE: The aim of this study was to investigate if Hounsfield unit (HU) voxel misassignments from sCT images result in dosimetric errors in clinical treatment plans. METHODS: Fourteen H&amp;N cancer patients undergoing same-day CT and 3T MRI simulation were retrospectively identified. MRI was deformed to the CT using multimodal deformable image registration. sCTs were generated from T1w DIXON MRIs using a commercially available deep learning-based generator (MRIplanner, Spectronic Medical AB, Helsingborg, Sweden). Tissue voxel assignment was quantified by creating a CT-derived HU threshold contour. CT/sCT HU differences for anatomical/target contours and tissue classification regions including air (&lt;250 HU), adipose tissue (-250 HU to -51 HU), soft tissue (-50 HU to 199 HU), spongy (200 HU to 499 HU) and cortical bone&nbsp;(&gt;500 HU) were quantified. t-test was used to determine if sCT/CT HU differences were significant. The frequency of structures that had a HU difference&nbsp;&gt;&nbsp;80 HU (the CT window-width setting for intra-cranial structures) was computed to establish structure classification accuracy. Clinical intensity modulated radiation therapy (IMRT) treatment plans created on CT were retrospectively recalculated on sCT images and compared using the gamma metric. RESULTS: The mean ratio of sCT HUs relative to CT for air, adipose tissue, soft tissue, spongy and cortical bone were 1.7&nbsp;±&nbsp;0.3, 1.1&nbsp;±&nbsp;0.1, 1.0&nbsp;±&nbsp;0.1, 0.9&nbsp;±&nbsp;0.1 and 0.8&nbsp;±&nbsp;0.1 (value of 1 indicates perfect agreement). T-tests (significance set at t&nbsp;=&nbsp;0.05) identified differences in HU values for air, spongy and cortical bone in sCT images compared to CT. The structures with sCT/CT HU differences&nbsp;&gt;&nbsp;80 HU of note were the left and right (L/R) cochlea and mandible (&gt;79% of the tested cohort), the oral cavity (for 57% of the tested cohort), the epiglottis (for 43% of the tested cohort) and the L/R TM joints (occurring&nbsp;&gt;&nbsp;29% of the cohort). In the case of the cochlea and TM joints, these structures contain dense bone/air interfaces. In the case of the oral cavity and mandible, these structures suffer the additional challenge of being positionally altered in CT versus MRI simulation (due to a non-MR safe immobilizing bite block requiring absence of bite block in MR). Finally, the epiglottis HU assignment suffers from its small size and unstable positionality. Plans recalculated on sCT yielded global/local gamma pass rates of 95.5%&nbsp;±&nbsp;2% (3&nbsp;mm, 3%) and 92.7%&nbsp;±&nbsp;2.1% (2&nbsp;mm, 2%). The largest mean differences in D95, Dmean , D50 dose volume histogram (DVH) metrics for organ-at-risk (OAR) and planning tumor volumes (PTVs) were 2.3%&nbsp;±&nbsp;3.0% and 0.7%&nbsp;±&nbsp;1.9% respectively. CONCLUSIONS: In this cohort, HU differences of CT and sCT were observed but did not translate into a reduction in gamma pass rates or differences in average PTV/OAR dose metrics greater than 3%. For sites such as the H&amp;N where there are many tissue interfaces we did not observe large scale dose deviations but further studies using larger retrospective cohorts are merited to establish the variation in sCT dosimetric accuracy which could help to inform QA limits on clinical sCT usage

Incidental findings and safety events from magnetic resonance imaging simulation for head and neck radiation treatment planning: A single institution experience.

PURPOSE: Having dedicated MRI scanners within radiation oncology departments may present unexpected challenges since radiation oncologists and radiation therapists are generally not trained in this modality and there are potential patient safety concerns. This study retrospectively reviews the incidental findings and safety events that were observed at a single institution during introduction of MRI sim for head and neck radiotherapy planning. METHODS: Consecutive patients from March 1, 2020, to May 31, 2022, who were scheduled for MRI sim after having completed CT simulation for head and neck radiotherapy were included for analysis. Patients first underwent a CT simulation with a thermoplastic mask and in most cases with an intraoral stent. The same setup was then reproduced in the MRI simulator. Safety events were instances where scheduled MRI sims were not completed due to the MRI technologist identifying MRI-incompatible devices or objects at the time of sim. Incidental findings were identified during weekly quality assurance rounds as a joint enterprise of head and neck radiation oncology and neuroradiology. Categorical variables between completed and not completed MRI sims were compared using the Chi-Square test and continuous variables were compared using the Mann-Whitney U test with a p-value of&nbsp;&lt;&nbsp;0.05 considered to be statistically significant. RESULTS: 148 of 169 MRI sims (88&nbsp;%) were completed as scheduled and 21 (12&nbsp;%) were not completed (Table 1). Among the 21 aborted MRI sims, the most common reason was due to safety events flagged by the MRI technologist (n&nbsp;=&nbsp;8, 38&nbsp;%) because of the presence of metal or a medical device that was not noted at the time of initial screening by the administrative coordinator. Patients who did not complete MRI sim were more likely to be treated for non-squamous head and neck primary tumor (p&nbsp;=&nbsp;0.016) and were being treated post-operatively (p&nbsp;&lt;&nbsp;0.001). CT and MRI sim scans each had 17 incidental findings. CT simulation detected 3 cases of new metastases in lungs, which were outside the scan parameters of MRI sim. MRI sim detected one case of dural venous thrombosis and one case of cervical spine epidural abscess, which were not detected by CT simulation. CONCLUSIONS: Radiation oncology departments with dedicated MRI simulation scanners would benefit from diagnostic radiology review for incidental findings and having therapists with MRI safety credentialing to catch near-miss events

Incidental findings and safety events from magnetic resonance imaging simulation for head and neck radiation treatment planning: A single institution experience

Purpose: Having dedicated MRI scanners within radiation oncology departments may present unexpected challenges since radiation oncologists and radiation therapists are generally not trained in this modality and there are potential patient safety concerns. This study retrospectively reviews the incidental findings and safety events that were observed at a single institution during introduction of MRI sim for head and neck radiotherapy planning. Methods: Consecutive patients from March 1, 2020, to May 31, 2022, who were scheduled for MRI sim after having completed CT simulation for head and neck radiotherapy were included for analysis. Patients first underwent a CT simulation with a thermoplastic mask and in most cases with an intraoral stent. The same setup was then reproduced in the MRI simulator. Safety events were instances where scheduled MRI sims were not completed due to the MRI technologist identifying MRI-incompatible devices or objects at the time of sim. Incidental findings were identified during weekly quality assurance rounds as a joint enterprise of head and neck radiation oncology and neuroradiology. Categorical variables between completed and not completed MRI sims were compared using the Chi-Square test and continuous variables were compared using the Mann-Whitney U test with a p-value of < 0.05 considered to be statistically significant. Results: 148 of 169 MRI sims (88 %) were completed as scheduled and 21 (12 %) were not completed (Table 1). Among the 21 aborted MRI sims, the most common reason was due to safety events flagged by the MRI technologist (n = 8, 38 %) because of the presence of metal or a medical device that was not noted at the time of initial screening by the administrative coordinator. Patients who did not complete MRI sim were more likely to be treated for non-squamous head and neck primary tumor (p = 0.016) and were being treated post-operatively (p < 0.001). CT and MRI sim scans each had 17 incidental findings. CT simulation detected 3 cases of new metastases in lungs, which were outside the scan parameters of MRI sim. MRI sim detected one case of dural venous thrombosis and one case of cervical spine epidural abscess, which were not detected by CT simulation. Conclusions: Radiation oncology departments with dedicated MRI simulation scanners would benefit from diagnostic radiology review for incidental findings and having therapists with MRI safety credentialing to catch near-miss events