Magnetic resonance linear accelerator technology and adaptive radiation therapy: An overview for clinicians.

Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat abstract to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR-guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time-consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.

Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.

PURPOSE: We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. MATERIALS AND METHODS: Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. RESULTS: For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. CONCLUSIONS: Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

Structure Revision of the Lomaiviticins.

The lomaiviticins are dimeric genotoxic metabolites that contain unusual diazocyclopentadiene functional groups and 2-4 deoxyglycoside residues. Because only 6 of 19 carbon atoms in the monomeric aglycon unit are proton-attached, their structure determination by NMR spectroscopic analysis is difficult. Prior structure elucidation efforts established that the two halves of the lomaiviticins are joined by a single carbon-carbon bond appended to an oxidized cyclohexenone ring. This ring was believed to comprise a 4,5-dihydroxycyclohex-2-ene-1-one. The bridging bond was positioned at C6. This structure proposal has not been tested because no lomaiviticin has been prepared by total chemical synthesis or successfully analyzed by X-ray crystallography. Here, we disclose microED studies which establish that (-)-lomaiviticin C contains a 4,6-dihydroxy-cyclohex-2-ene-1-one residue, that the bridging carbon-carbon bond is located at C5, and that the orientation of the cyclohexenone ring and configuration of the secondary glycoside are reversed, relative to their original assignment. High-field (800 MHz) NMR analysis supports the revised assignment and suggests earlier efforts were misled by a combination of a near-zero 3JH4,H5 coupling constant and a 4JC,H coupling interpreted as a 3JC,H coupling. DFT calculations of the expected 13C chemical shifts and C-H coupling constants provide further robust support for the structure revision. Because the interconversion of lomaiviticins A, B, and C has been demonstrated, these findings apply to each isolate. These studies clarify the structures of this family of metabolites and underscore the power of microED analysis in natural product structure determination.

Generalized simultaneous multi-orientation 2D imaging.

PURPOSE: Flexibility in slice prescription is critical for precise motion monitoring during MR-guided therapies. Adding more slices to improve spatial coverage during rapid 2D cine imaging often hampers temporal resolution. This work describes a framework to simultaneously acquire multiple arbitrarily oriented slices which share a common frequency encoding axis. This framework allows for higher frame rates for a given number of slices compared to conventional interleaved-slice multi-orientation cine imaging. THEORY AND METHODS: A framework to calculate zeroth gradient moments to be played out between sequentially excited slices with multiple orientations is described here. Experiments were performed in phantom, and in vivo in the head/neck and abdomen of patients. RESULTS: Images arbitrarily rotated relative to one another were successfully obtained in phantom and in vivo. Simultaneous multi-orientation (SMO) images were also acquired with additional in-plane acceleration to demonstrate the capability of this method to rapidly image objects moving with physiological motion. CONCLUSIONS: The technical feasibility of the generalized SMO imaging framework was tested in this study. It shows promise for continued development for motion monitoring during MR-guided therapies.

A daily end-to-end quality assurance workflow for MR-guided online adaptive radiation therapy on MR-Linac.

PURPOSE: Magnetic Resonance (MR)-guided online adaptive radiation therapy (MRgOART), enabled with MR-Linac, has potential to revolutionize radiation therapy. MRgOART is a complex process. This work is to introduce a comprehensive end-to-end quality assurance (QA) workflow in routine clinic for MRgOART with a high-magnetic-field MR-Linac. MATERIALS AND METHOD: The major components in MRgOART with a high-magnetic field MR-Linac (Unity, Elekta) include: (1) a patient record and verification (R&V) system (e.g., Mosaiq, Elekta), (2) a treatment session manager, (3) an offline treatment planning system (TPS), (4) an online adaptive TPS, (5) a 1.5T MRI scanner, (6) an 7MV Linac, (7) an MV imaging controller (MVIC), and (8) ArtQA: software for plan data consistency checking and secondary dose calculation. Our end-to-end QA workflow was designed to test the performance and connectivity of all these components by transferring, adapting and delivering a specifically designed five-beam plan on a phantom. Beams 1-4 were designed to check Multi-Leaves Collimator (MLC) position shift based on rigid image registration in TPS, while beam 5 was used to check daily radiation output based on image pixel factor of MV image of the field. The workflow is initiated in the R&V system and followed by acquiring and registering daily MRI of the phantom, checking isocenter shift, performing online adaptive replanning, checking plan integrity and secondary 3D dose calculation, delivering the plan while acquiring MV imaging using MVIC, acquiring real-time images of the phantom, and checking the delivering parameters with ArtQA. RESULTS: It takes 10 min to finish the entire end-to-end QA workflow. The workflow has detected communication problems, permitted resolution prior to setting up patients for MRgOART. Up to 0.9 mm discrepancies in isocenter shift based on the image registration were detected. ArtQA performed the secondary 3D dose calculation, verified the plan integrity as well as the MR-MV isocenter alignment values in TPS. The MLC shapes of beam 1-4 in all adaptive plans were conformal to the target and agreed with MV images. The variation of daily output was within ±2.0%. CONCLUSIONS: The comprehensive end-to-end QA workflow can efficiently check the performance and communication between different components in MRgOART and has been successfully implemented for daily clinical practice.

Cobalt(III)-Catalyzed C-H Amidation of Dehydroalanine for the Site-Selective Structural Diversification of Thiostrepton.

Thiostrepton is a potent antibiotic against a broad range of Gram-positive bacteria, but its medical applications have been limited by its poor aqueous solubility. In this work, the first C(sp2 )-H amidation of dehydroalanine (Dha) residues was applied to the site selective modification of thiostrepton to prepare a variety of derivatives. Unlike all prior methods for the modification of thiostrepton, the alkene framework of the Dha residue is preserved and with complete selectivity for the Z-stereoisomer. Additionally, an aldehyde group was introduced by C-H amidation, enabling oxime ligation for the installation of an even greater range of functionality. The thiostrepton derivatives generally maintain antimicrobial activity, and importantly, eight of the derivatives displayed improved aqueous solubility (up to 28-fold), thereby addressing a key shortcoming of this antibiotic. The exceptional functional group compatibility and site selectivity of CoIII -catalyzed C(sp2 )-H Dha amidation suggests that this approach could be generalized to other natural products and biopolymers containing Dha residues.

Structural basis for DNA cleavage by the potent antiproliferative agent (-)-lomaiviticin A.

(-)-Lomaiviticin A (1) is a complex antiproliferative metabolite that inhibits the growth of many cultured cancer cell lines at low nanomolar-picomolar concentrations. (-)-Lomaiviticin A (1) possesses a C2-symmetric structure that contains two unusual diazotetrahydrobenzo[b]fluorene (diazofluorene) functional groups. Nucleophilic activation of each diazofluorene within 1 produces vinyl radical intermediates that affect hydrogen atom abstraction from DNA, leading to the formation of DNA double-strand breaks (DSBs). Certain DNA DSB repair-deficient cell lines are sensitized toward 1, and 1 is under evaluation in preclinical models of these tumor types. However, the mode of binding of 1 to DNA had not been determined. Here we elucidate the structure of a 1:1 complex between 1 and the duplex d(GCTATAGC)2 by NMR spectroscopy and computational modeling. Unexpectedly, we show that both diazofluorene residues of 1 penetrate the duplex. This binding disrupts base pairing leading to ejection of the central AT bases, while placing the proreactive centers of 1 in close proximity to each strand. DNA binding may also enhance the reactivity of 1 toward nucleophilic activation through steric compression and conformational restriction (an example of shape-dependent catalysis). This study provides a structural basis for the DNA cleavage activity of 1, will guide the design of synthetic DNA-activated DNA cleavage agents, and underscores the utility of natural products to reveal novel modes of small molecule-DNA association.

Measurement validation of treatment planning for a MR-Linac.

PURPOSE: The magnetic field can cause a nonnegligible dosimetric effect in an MR-Linac system. This effect should be accurately accounted for by the beam models in treatment planning systems (TPS). The purpose of the study was to verify the beam model and the entire treatment planning and delivery process for a 1.5 T MR-Linac based on comprehensive dosimetric measurements and end-to-end tests. MATERIAL AND METHODS: Dosimetry measurements and end-to-end tests were performed on a preclinical MR-Linac (Elekta AB) using a multitude of detectors and were compared to the corresponding beam model calculations from the TPS for the MR-Linac. Measurement devices included ion chambers (IC), diamond detector, radiochromic film, and MR-compatible ion chamber array and diode array. The dose in inhomogeneous phantom was also verified. The end-to-end tests include the generation, delivery, and comparison of 3D and IMRT plan with measurement. RESULTS: For the depth dose measurements with Farmer IC, micro IC and diamond detector, the absolute difference between most measurement points and beam model calculation beyond the buildup region were <1%, at most 2% for a few measurement points. For the beam profile measurements, the absolute differences were no more than 1% outside the penumbra region and no more than 2.5% inside the penumbra region. Results of end-to-end tests demonstrated that three 3D static plans with single 5 × 10 cm2 fields (at gantry angle 0°, 90° and 270°) and two IMRT plans successfully passed gamma analysis with clinical criteria. The dose difference in the inhomogeneous phantom between the calculation and measurement was within 1.0%. CONCLUSIONS: Both relative and absolute dosimetry measurements agreed well with the TPS calculation, indicating that the beam model for MR-Linac properly accounts for the magnetic field effect. The end-to-end tests verified the entire treatment planning process.

Diversity of Secondary Structure in Catalytic Peptides with ß-Turn-Biased Sequences.

X-ray crystallography has been applied to the structural analysis of a series of tetrapeptides that were previously assessed for catalytic activity in an atroposelective bromination reaction. Common to the series is a central Pro-Xaa sequence, where Pro is either l- or d-proline, which was chosen to favor nucleation of canonical ß-turn secondary structures. Crystallographic analysis of 35 different peptide sequences revealed a range of conformational states. The observed differences appear not only in cases where the Pro-Xaa loop-region is altered, but also when seemingly subtle alterations to the flanking residues are introduced. In many instances, distinct conformers of the same sequence were observed, either as symmetry-independent molecules within the same unit cell or as polymorphs. Computational studies using DFT provided additional insight into the analysis of solid-state structural features. Select X-ray crystal structures were compared to the corresponding solution structures derived from measured proton chemical shifts, 3J-values, and 1H-1H-NOESY contacts. These findings imply that the conformational space available to simple peptide-based catalysts is more diverse than precedent might suggest. The direct observation of multiple ground state conformations for peptides of this family, as well as the dynamic processes associated with conformational equilibria, underscore not only the challenge of designing peptide-based catalysts, but also the difficulty in predicting their accessible transition states. These findings implicate the advantages of low-barrier interconversions between conformations of peptide-based catalysts for multistep, enantioselective reactions.

Simultaneous orthogonal plane imaging.

PURPOSE: Intrafraction motion can result in a smearing of planned external beam radiation therapy dose distributions, resulting in an uncertainty in dose actually deposited in tissue. The purpose of this paper is to present a pulse sequence that is capable of imaging a moving target at a high frame rate in two orthogonal planes simultaneously for MR-guided radiotherapy. THEORY: By balancing the zero gradient moment on all axes, slices in two orthogonal planes may be spatially encoded simultaneously. The orthogonal slice groups may be acquired with equal or nonequal echo times. METHODS: A Cartesian spoiled gradient echo simultaneous orthogonal plane imaging (SOPI) sequence was tested in phantom and in vivo. Multiplexed SOPI acquisitions were performed in which two parallel slices were imaged along two orthogonal axes simultaneously. An autocalibrating phase-constrained 2D-SENSE-GRAPPA (generalized autocalibrating partially parallel acquisition) algorithm was implemented to reconstruct the multiplexed data. RESULTS: SOPI images without intraslice motion artifacts were reconstructed at a maximum frame rate of 8.16 Hz. The 2D-SENSE-GRAPPA reconstruction separated the parallel slices aliased along each orthogonal axis. CONCLUSION: The high spatiotemporal resolution provided by SOPI has the potential to be beneficial for intrafraction motion management during MR-guided radiation therapy or other MRI-guided interventions. Magn Reson Med 78:1700-1710, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Evaluation of absolute and normalized apparent diffusion coefficient (ADC) values within the post-operative T2/FLAIR volume as adverse prognostic indicators in glioblastoma.

To evaluate the association of normalized and absolute ADC metrics with progression free survival (PFS) and overall survival (OS) in patients treated for glioblastoma multiforme (GBM). Fifty-two patients with preradiotherapy diffusion weighted imaging treated with post-operative chemoradiation for GBM were evaluated. Region of interest analysis for ADC metrics including mean and minimum ADC value (ADCmean) and (ADCmin) was performed within the T2/FLAIR volume. Normalized (N)ADC values were generated relative to contralateral white matter. PFS and OS were analyzed relative to ADC parameters using a regression model. Kaplan-Meier and Cox proportional hazards analysis with respect to (N)ADCmean, and (N)ADCmin was performed. A (N)ADC threshold <1.3 within the T2/FLAIR volume was analyzed with respect to PFS and OS. Regression analysis indicated that normalized ADC values provide the strongest association with PFS and OS. Kaplan-Meier analysis revealed a non-significant trend toward inferior PFS and OS associated with (N)ADCmean <1.7, and a significant decrement to PFS and OS associated with (N)ADCmin <0.3. (N)ADCmin was a significant prognostic factor when taking into account age, performance status, and extent of resection. ADC thresholding analysis revealed that a retained volume of >0.45 cc per mL FLAIR volume was associated with a trend toward inferior PFS and OS. In the post-operative, pre-radiotherapy setting, the (N)ADCmin is the strongest predictor of outcomes in patients treated for GBM. ADC thresholding analysis indicates that a large volume of normalized ADC value <1.3 may be associated with adverse outcomes.

Evaluation of pre-radiotherapy apparent diffusion coefficient (ADC): patterns of recurrence and survival outcomes analysis in patients treated for glioblastoma multiforme.

PURPOSE: To investigate the association of pre-radiotherapy apparent diffusion coefficient (ADC) abnormalities with patterns of recurrence and outcomes in patients with glioblastoma multiforme (GBM). MATERIALS AND METHODS: Fifty-two patients with recurrent GBM were retrospectively evaluated. Diffusion MRI images were acquired for all patients postoperatively prior to radiotherapy. ADC images were evaluated for geographic regions of diffusion restriction (hypointensity) within the FLAIR volume. If identified, the ADC map and the T1+C MRI at the time of recurrence were registered to the original plan to determine the pattern of recurrence and the coverage of the ADC abnormality by the 60 Gy isodose line (IDL). Progression-free and overall survival was determined for patients with and without an ADC hypointensity. RESULTS: An ADC hypointensity was identified in 32 (62%) of cases. The recurrence pattern in these cases was central in 27/32 (84%), marginal in 4/32 (13%) and distant in 1/32 (3%). The recurrence overlapped with the ADC hypointensity in 28 (88%) patients. The ADC hypointensity was covered by 95% of the 60 Gy IDL in all cases. Kaplan-Meier analysis revealed inferior progression free survival and overall survival in patients with an ADC hypointensity compared to those without, despite similarities between the groups in terms of age, RT dose, performance status, and extent of resection. CONCLUSIONS: The presence of an ADC hypointensity on pre-radiotherapy diffusion-weighted imaging is associated with the location of tumor recurrence as demonstrated by frequent overlap in this series, and is associated with a trend toward inferior outcomes. This abnormality may reflect a high risk region of hypercellularity and warrants consideration with respect to radiotherapy planning.

Quantitative and Qualitative Comparison of Single-Source Dual-Energy Computed Tomography and 120-kVp Computed Tomography for the Assessment of Pancreatic Ductal Adenocarcinoma.

PURPOSE: The aim of this study was to compare contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) between pancreatic-phase dual-energy computed tomography (DECT) and 120-kVp CT for pancreatic ductal adenocarcinoma (PDA). MATERIALS AND METHODS: Seventy-eight patients underwent multiphasic pancreatic imaging protocols for PDA (40, DECT; 38, 120-kVp CT [control]). Using pancreatic phase, CNR and SNR for PDA were obtained for DECT at monochromatic energies 50 through 80 keV, iodine material density images, and 120-kVp images. Using a 5-point scale (1, excellent; 5, markedly limited), images were qualitatively assessed by 2 radiologists in consensus for PDA detection, extension, vascular involvement, and noise. Wilcoxon signed rank and 2-sample tests were used to compare the qualitative measures, CNR and SNR, for DECT and 120-kVp images. Bonferroni correction was applied. RESULTS: Iodine material density image had significantly higher CNR and SNR for PDA than any monochromatic energy images (P < 0.0001) and the 120-kVp images. Qualitatively, 70-keV images were rated highest in the categories of tumor extension and vascular invasion and were similar to 120-kVp images. CONCLUSIONS: Our results indicate that DECT improves PDA lesion conspicuity compared with routine 120-kVp CT, which may allow for better detection of PDA.

Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma.

Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.

Changes in Daily Apparent Diffusion Coefficient on Fully Quantitative Magnetic Resonance Imaging Correlate With Established Genomic Pathways of Radiation Sensitivity and Reveal Novel Biologic Associations.

PURPOSE: Changes in quantitative magnetic resonance imaging (qMRI) are frequently observed during chemotherapy or radiation therapy (RT). It is hypothesized that qMRI features are reflective of underlying tissue responses. It's unknown what underlying genomic characteristics underly qMRI changes. We hypothesized that qMRI changes may correlate with DNA damage response (DDR) capacity within human tumors. Therefore, we designed the current study to correlate qMRI changes from daily RT treatment with underlying tumor transcriptomic profiles. METHODS AND MATERIALS: Study participants were prospectively enrolled (National Clinical Trial 03500081). RNA expression levels for 757 genes from pretreatment biopsies were obtained using a custom panel that included signatures of radiation sensitivity and DDR. Daily qMRI data were obtained from a 1.5 Tesla MR linear accelerator. Using these images, d-slow, d-star, perfusion, and apparent diffusion coefficient-mean values in tumors were plotted per-fraction, over time, and associated with genomic pathways. RESULTS: A total of 1022 qMRIs were obtained from 39 patients and both genomic data and qMRI data from 27 total patients. For 20 of those patients, we also generated normal tissue transcriptomic data. Radio sensitivity index values most closely associated with tissue of origin. Multiple genomic pathways including DNA repair, peroxisome, late estrogen receptor responses, KRAS signaling, and UV response were significantly associated with qMRI feature changes (P < .001). CONCLUSIONS: Genomic pathway associations across metabolic, RT sensitivity, and DDR pathways indicate common tumor biology that may correlate with qMRI changes during a course of treatment. Such data provide hypothesis-generating novel mechanistic insight into the biologic meaning of qMRI changes during treatment and enable optimal selection of imaging biomarkers for biologically MR-guided RT.

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

Objective. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed purposes, but poses a challenge, since unlike computed tomography, they do not occupy all dark regions on the image. The purpose of this study is to develop an automated method to segment the air regions on MRI-guided adaptive radiation therapy (MRgART) of abdominal tumors.Approach. A modified ResUNet3D deep learning (DL)-based auto air delineation model was trained using 102 patients' MR images. The MR images were acquired by a dedicated in-house sequence named 'Air-Scan', which is designed to generate air regions that are especially dark and accentuated. The air volumes generated by the newly developed DL model were compared with the manual air contours using geometric similarity (Dice Similarity Coefficient (DSC)), and dosimetric equivalence using Gamma index and dose-volume parameters.Main results. The average DSC agreement between the DL generated and manual air contours is 99% ± 1%. The gamma index between the dose calculations with overriding the DL versus manual air volumes with density of 0.01 is 97% ± 2% for a local gamma calculation with a tolerance of 2% and 2 mm. The dosimetric parameters from planning target volume-PTV and organs at risk-OARs were all within 1% between when DL versus manual contours were overridden by air density. The model runs in less than five seconds on a PC with 28 Core processor and NVIDIA Quadro®P2000 GPU.Significance: a DL based automated segmentation method was developed to generate air volumes on specialized abdominal MR images and generate results that are practically equivalent to the manual contouring of air volumes.

Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs.

Introduction: Multi-sequence multi-parameter MRIs are often used to define targets and/or organs at risk (OAR) in radiation therapy (RT) planning. Deep learning has so far focused on developing auto-segmentation models based on a single MRI sequence. The purpose of this work is to develop a multi-sequence deep learning based auto-segmentation (mS-DLAS) based on multi-sequence abdominal MRIs. Materials and methods: Using a previously developed 3DResUnet network, a mS-DLAS model using 4 T1 and T2 weighted MRI acquired during routine RT simulation for 71 cases with abdominal tumors was trained and tested. Strategies including data pre-processing, Z-normalization approach, and data augmentation were employed. Additional 2 sequence specific T1 weighted (T1-M) and T2 weighted (T2-M) models were trained to evaluate performance of sequence-specific DLAS. Performance of all models was quantitatively evaluated using 6 surface and volumetric accuracy metrics. Results: The developed DLAS models were able to generate reasonable contours of 12 upper abdomen organs within 21 seconds for each testing case. The 3D average values of dice similarity coefficient (DSC), mean distance to agreement (MDA mm), 95 percentile Hausdorff distance (HD95% mm), percent volume difference (PVD), surface DSC (sDSC), and relative added path length (rAPL mm/cc) over all organs were 0.87, 1.79, 7.43, -8.95, 0.82, and 12.25, respectively, for mS-DLAS model. Collectively, 71% of the auto-segmented contours by the three models had relatively high quality. Additionally, the obtained mS-DLAS successfully segmented 9 out of 16 MRI sequences that were not used in the model training. Conclusion: We have developed an MRI-based mS-DLAS model for auto-segmenting of upper abdominal organs on MRI. Multi-sequence segmentation is desirable in routine clinical practice of RT for accurate organ and target delineation, particularly for abdominal tumors. Our work will act as a stepping stone for acquiring fast and accurate segmentation on multi-contrast MRI and make way for MR only guided radiation therapy.

Predicting Treatment Response From Extracellular Volume Fraction for Chemoradiation Therapy of Pancreatic Cancer.

PURPOSE: Dual-energy computed tomography (DECT) data can be used to calculate the extracellular volume fraction (ECVf) in tumors, which has been correlated with treatment outcome. This study sought to find a correlation between ECVf and treatment response as measured by the change in cancer antigen (CA) 19 to 9 during chemoradiation therapy (CRT) for pancreatic cancer. METHODS AND MATERIALS: Dual-energy CT data acquired during the late arterial contrast phase in the standard radiation therapy simulation on a dual-source DECT simulator for 25 patients with pancreatic cancer, along with their CA19-9 and hematocrit data, were analyzed. Each patient underwent preoperative CRT with a prescription of 50.4 Gy in 28 fractions. The patients were chosen based on the presence of a solid tumor in the pancreas that could be clearly delineated. A region of interest (ROI) was placed in the tumor and in the aorta. From the ratio of the iodine density calculated from the DECT in the ROI and the hematocrit taken at the time of simulation, the ECVf was calculated. The ECVf was then compared with the change in CA19-9 before and after the CRT. Distant metastases as the cause of CA19-9 elevation were ruled out on subsequent restaging images before surgery. The DECT-derived iodine ratio was validated using a phantom study. RESULTS: The DECT-derived iodine concentration agreed with the phantom measurements (R2, 1.0). The average hematocrit, ECVf, and change in CA19-9 during the treatment for the 25 patients was 35.6 ± 5.4%, 7.3 ± 4.9%, and -4.6 ± 21.8 respectively. A linear correlation was found between the ECVf and the change in CA19-9, with an R2 of 0.7: ΔCA19-9 = 3.63 × ECVf - 31.1. The correlation was statistically significant (P = .006). CONCLUSIONS: The calculated ECV fraction based on iodine maps from dual-source DECT may be used to predict treatment response after neoadjuvant chemoradiation therapy for pancreatic cancer.

Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier.

PURPOSE: MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set. METHODS: A total of 109 daily MRI sets acquired on a 1.5T MR-Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No-OLAR) plans were created and the superior plan with the daily fraction determined per clinical dose-volume criteria. SSIM and entropy maps were extracted from each daily MRI set, with respect to its reference (e.g., dry-run) MRI in the region enclosed by 50-100% isodose surfaces. A total of six common features were extracted from SSIM maps. Pearson's rank correlation coefficient was utilized to rule out redundant SSIM features. A t-test was used to determine significant SSIM features which were combined with the change in entropy to develop anensemble machine classifier with fivefold cross validation. The performance of the classifier was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: A machine learning classifier model using two SSIM features (mean and full width at half maximum) and change in entropy was determined to be able to significantly discriminate between No-OLAR and OLAR groups. The obtained machine learning ensemble classifier can predict OLAR necessity with a cross validated AUC of 0.93. Misclassification was found primarily for No-OLAR cases with dosimetric plan quality closely comparable to the corresponding OLAR plans, thus, are not a major practical concern. CONCLUSION: A machine learning classifier based on simple first-order image features, that is, SSIM features and change in entropy, was developed to determine when OLAR is necessary for a daily MRI set with practical acceptable prediction accuracy. This classifier may be implemented in the MRgART process to automatically and objectively determine if OLAR is required following daily MRI.

A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer.

PURPOSE: Treatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS). MATERIALS AND METHODS: A retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed. Clinical factors together with 18 radiomic features extracted from different radiomics matrices were analyzed. The delta radiomic features (DRFs) were extracted from MRI on the first and last brachytherapy fractions. Support Vector Machine (SVM) models were fitted to combinations of 2-3 DRFs found significant after Spearman correlation and Wilcoxon rank sum test statistics. Additional models were tested that included clinical factors together with DRFs. RESULTS: A total of 39 patients were included in the analysis with a median patient age of 52 years. Progression occurred in 20% of patients (8/39). The significant DRFs using two DRF feature combinations was a model using auto correlation (AC) and sum variance (SV). The best performing three feature model combined mean, AC & SV. Additionally, the inclusion of FIGO stages with the 2- and 3 DRF combination model(s) improved performance compared to models with only DRFs. However, all the clinical factor + DRF models were not significantly different from one another (all AUCs were 0.77). CONCLUSIONS: Our study shows promising evidence that radiomics metrics are associated with progression free survival in cervical cancer.