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Purpose We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). Methods In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Results Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. Conclusions This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.

Purpose: Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Methods: Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC) and feature engineering analyses were performed on the feature spaces of the K-SOMs to quantify the distinction power of different radiomics features compared to raw-DCE-MRI for classification of different nested models. Results: Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875%±12.922%, pConclusions: This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.

Evaluate custom beam models for a second check dose calculation system using statistically verifiable passing criteria for film analysis, DVH, and 3D gamma metrics.Custom beam models for nine linear accelerators for the Sun Nuclear Dose Calculator algorithm (SDC, Sun Nuclear) were evaluated using the AAPM-TG119 test suite (5 Intensity Modulated Radiation Therapy (IMRT) and 5 Volumetric Modulated Arc Therapy (VMAT) plans) and a set of clinical plans. Where deemed necessary, adjustments to Multileaf Collimator (MLC) parameters were made to improve results. Comparisons to the Analytic Anisotropic Algorithm (AAA), and gafchromic film measurements were performed. Confidence intervals were set to 95% per TG-119. Film gamma criteria were 3%/3 mm (conventional beams) or 3%/1 mm (Stereotactic Radiosurgery [SRS] beams). Dose distributions in solid water phantom were evaluated based on DVH metrics (e.g., D95, V20) and 3D gamma criteria (3%/3 mm or 3%/1 mm). Film passing rates, 3D gamma passing rates, and DVH metrics were reported for HD MLC machines and Millennium MLC Machines.For HD MLC machines, SDC gamma film agreement was 98.76% ± 2.30% (5.74% CL) for 6FFF/6srs (3%/1 mm), and 99.80% ± 0.32% (0.83% CL) for 6x (3%/3 mm). For Millennium MLC machines, film passing rates were 98.20% ± 3.14% (7.96% CL), 99.52% ± 1.14% (2.71% CL), and 99.69% ± 0.82% (1.91% CL) for 6FFF, 6x, and 10x, respectively. For SDC to AAA comparisons: HD MLC Linear Accelerators (LINACs); DVH point agreement was 0.97% ± 1.64% (4.18% CL) and 1.05% ± 2.12% (5.20% CL); 3D gamma agreement was 99.97% ± 0.14% (0.30% CL) and 100.00% ± 0.02% (0.05% CL), for 6FFF/6srs and 6x, respectively; Millennium MLC LINACs: DVH point agreement was 0.77% ± 2.40% (5.47% CL), 0.80% ± 3.40% (7.47% CL), and 0.07% ± 2.15% (4.30% CL); 3D gamma agreement was 99.97% ± 0.13% (0.29% CL), 99.97% ± 0.17% (0.36% CL), and 99.99% ± 0.06% (0.12% CL) for 6FFF, 6x, and 10x, respectively.SDC shows agreement well within TG119 CLs for film and redundant dose calculation comparisons with AAA. In some models (SRS), this was achieved using stricter criteria. TG119 plans can be used to help guide model adjustments and to establish clinical baselines for DVH and 3D gamma criteria.

Cell surface molecules expressed on cancer cells can be used as diagnostic and therapeutic tools. In breast cancer, CD44 and CD24 were identified as cell surface markers characterizing cancer cell stemness that may correlate with prognosis. Breast cancer cells with high CD44 and low CD24 expression have been shown to exhibit proliferative, invasive and metastatic properties that may relate to drug sensitivity and metastatic risk in patients. In addition to stemness, immune evasion is another hallmark of cancer with CD47 “don’t eat me” and CD274 “don’t find me” molecules playing important roles in antitumor immunity. Although each of these molecules are of interest as therapeutic targets, current therapeutic paradigms involve multimodal approach with radiation therapy often having a central role. Hence, the effect of irradiation, and related radio-sensitizers, on the expression of cancer cells surface markers to be targeted with an adjuvant therapeutic mode must be considered when designing such therapies. The goal of this study was to determine the dynamics of CD44, CD24, CD47 and CD274 expression in radio-sensitized and irradiated MDA-MB 231 breast cancer cells. We have recently reported that gold nanoparticles (AuNPs) sensitized breast cancer cells to irradiation at kV and MV energies. Here, we hypothesize that AuNP may also modulate the irradiation altered expression of cell surface markers. We explored this by using 10 MV energies and 6 Gy radiation dose. MDA-MB-231 cells were incubated for 3 hours with 14nm AuNPs, irradiated and allowed to grow for 24h and 72h after which percenteges of positive cells were determined by flow cytometry. Results are expressed as a percent of control, non-irradiated cells that was set at 100%. At 24h non-irradiated cells pre-incubated with AuNP exhibited a decrease in the percentage of CD24+ and CD44+ cells, while no significant change in the percentage of CD47+ and CD274+ cells was observed. Irradiation with 6Gy at 10 MV induced a decrease in CD24+ and an increase in CD44+ cells and AuNP potentiated this effect. However, 72h post-irradiation, percentages of CD24+ and CD44+ cells significantly increased, compared to non-irradiated controls and this effect was of lesser magnitude in AuNP pretreated cells. Percentage of CD47+ and CD274+ cells also significantly increased 24h post-irradiation, but with less magnitude in AuNP treated cells. At 72h in AuNP treated irradiated cells no change was observed in the percentage of CD274+ cells, while the percentage of CD47+ cells significantly increased in comparison to the irradiated cells not exposed to AuNPs. Preliminary data shown here indicate that expression of molecules important for cancer progression, metastasis and immune evasion undergo changes in response to irradiation, and that these changes are affected by AuNPs. Further studies will shed more light on the mechanisms behind these observed effects. Citation Format: Branislava Janic, Fangchao Liu, Kevin Bobbitt, Stephen Brown, Guangzhao Mao, Indrin Chetty, Benjamin Movsas, Ning Wen. Effect of irradiation and gold nanoparticle on expression dynamics of cell surface markers in MDA-MB 231 breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4862.

To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (HN) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment.Planning CT images of ten HN patients were artificially deformed to create "digital phantom" images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients' actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment.Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA's ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion.Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during HN radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are established early in a treatment course, or based on population models.