AAPM Task Group 119 has produced quantitative confidence limits as baseline expectation values for IMRT commissioning. A set of test cases was developed to assess the overall accuracy of planning and delivery of IMRT treatments. Each test uses contours of targets and avoidance structures drawn within rectangular phantoms. These tests were planned, delivered, measured, and analyzed by nine facilities using a variety of IMRT planning and delivery systems. Each facility had passed the Radiological Physics Center credentialing tests for IMRT. The agreement between the planned and measured doses was determined using ion chamber dosimetry in high and low dose regions, film dosimetry on coronal planes in the phantom with all fields delivered, and planar dosimetry for each field measured perpendicular to the central axis. The planar dose distributions were assessed using gamma criteria of 3%/3 mm. The mean values and standard deviations were used to develop confidence limits for the test results using the concept confidence limit = /mean/ + 1.96sigma. Other facilities can use the test protocol and results as a basis for comparison to this group. Locally derived confidence limits that substantially exceed these baseline values may indicate the need for improved IMRT commissioning.

Through a systematic analysis of multiparametric MR imaging features, we are able to build models with improved predictive value over conventional imaging metrics. The results are encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailoring the treatment into the era of personalized medicine. Clin Cancer Res; 22(21); 5256-64. ©2016 AACR.

Background Our previous analysis of papillary thyroid carcinomas (PTC) from the Ukrainian-American cohort exposed to 131I from the Chernobyl accident found RET/PTC rearrangements and other driver mutations in 60% of tumors. Methods In this study, we analyzed the remaining, mutation-negative tumors using RNA-Seq and RT-PCR to identify novel chromosomal rearrangements and characterize their relationship with radiation dose. Results The ETV6-NTRK3 rearrangement was identified by RNA-Seq in a tumor from a patient who received a high 131I dose. Overall, it was detected in 9/62 (14.5%) of post-Chernobyl and in 3/151 (2%) of sporadic PTCs (p=0.019). The most common fusion type was between exon 4 of ETV6 and exon 14 of NTRK3. The ETV6-NTRK3 prevalence in post-Chernobyl PTCs was associated with increasing 131I dose, albeit at borderline significance (p=0.126). The group of rearrangement-positive PTCs (ETV6-NTRK3, RET/PTC, PAX8-PPARγ) was associated with significantly higher dose response compared to the group of PTCs with point mutations (BRAF, RAS) (p<0.001). In vitro exposure of human thyroid cells to 1 Gy of 131I and γ-radiation resulted in the formation of ETV6-NTRK3 with a rate of 7.9 × 10−6 and 3.0 ×10−6 cells, respectively. Conclusions We report here the occurrence of ETV6-NTRK3 rearrangements in thyroid cancer and show that this rearrangement is significantly more common in tumors associated with exposure to 131I and has a borderline significant dose response. Moreover, ETV6-NTRK3 can be directly induced in thyroid cells by ionizing radiation in vitro and therefore may represent a novel mechanism of radiation-induced carcinogenesis.

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Purpose To improve image quality and computed tomography (CT) number accuracy of daily cone beam CT (CBCT) through a deep learning methodology with generative adversarial network. Methods One hundred and fifty paired pelvic CT and CBCT scans were used for model training and validation. An unsupervised deep learning method, 2.5D pixel‐to‐pixel generative adversarial network (GAN) model with feature mapping was proposed. A total of 12 000 slice pairs of CT and CBCT were used for model training, while ten‐fold cross validation was applied to verify model robustness. Paired CT–CBCT scans from an additional 15 pelvic patients and 10 head‐and‐neck (HN) patients with CBCT images collected at a different machine were used for independent testing purpose. Besides the proposed method above, other network architectures were also tested as: 2D vs 2.5D; GAN model with vs without feature mapping; GAN model with vs without additional perceptual loss; and previously reported models as U‐net and cycleGAN with or without identity loss. Image quality of deep‐learning generated synthetic CT (sCT) images was quantitatively compared against the reference CT (rCT) image using mean absolute error (MAE) of Hounsfield units (HU) and peak signal‐to‐noise ratio (PSNR). The dosimetric calculation accuracy was further evaluated with both photon and proton beams. Results The deep‐learning generated sCTs showed improved image quality with reduced artifact distortion and improved soft tissue contrast. The proposed algorithm of 2.5 Pix2pix GAN with feature matching (FM) was shown to be the best model among all tested methods producing the highest PSNR and the lowest MAE to rCT. The dose distribution demonstrated a high accuracy in the scope of photon‐based planning, yet more work is needed for proton‐based treatment. Once the model was trained, it took 11–12 ms to process one slice, and could generate a 3D volume of dCBCT (80 slices) in less than a second using a NVIDIA GeForce GTX Titan X GPU (12 GB, Maxwell architecture). Conclusion The proposed deep learning algorithm is promising to improve CBCT image quality in an efficient way, thus has a potential to support online CBCT‐based adaptive radiotherapy.

Autophagy is a conserved defense strategy against viral infection. However, little is known about the counterdefense strategies of plant viruses involving interference with autophagy. Here, we show that γb protein from (BSMV), a positive single-stranded RNA virus, directly interacts with AUTOPHAGY PROTEIN7 (ATG7). BSMV infection suppresses autophagy, and overexpression of γb protein is sufficient to inhibit autophagy. Furthermore, silencing of autophagy-related gene and in plants enhanced BSMV accumulation and viral symptoms, indicating that autophagy plays an antiviral role in BSMV infection. Molecular analyses indicated that γb interferes with the interaction of ATG7 with ATG8 in a competitive manner, whereas a single point mutation in γb, Tyr29Ala (Y29A), made this protein deficient in the interaction with ATG7, which was correlated with the abolishment of autophagy inhibition. Consistently, the mutant BSMV virus showed reduced symptom severity and viral accumulation. Taken together, our findings reveal that BSMV γb protein subverts autophagy-mediated antiviral defense by disrupting the ATG7-ATG8 interaction to promote plant RNA virus infection, and they provide evidence that ATG7 is a target of pathogen effectors that functions in the ongoing arms race of plant defense and viral counterdefense.

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Purpose We sought to evaluate whether tumor response using cone beam computed tomography (CBCT) performed as part of the routine care during chemoradiation therapy (CRT) could forecast the outcome of unresectable, locally advanced, non-small cell lung cancer (NSCLC). Methods and Materials We manually delineated primary tumor volumes (TV) of patients with NSCLC who were treated with radical CRT on days 1, 8, 15, 22, 29, 36, and 43 on CBCTs obtained as part of the standard radiation treatment course. Percentage reductions in TV were calculated and then correlated to survival and pattern of recurrence using Cox proportional hazard models. Clinical information including histologic subtype was also considered in the study of such associations. Results We evaluated 38 patients with a median follow-up time of 23.4 months. The median TV reduction was 39.3% (range, 7.3%-69.3%) from day 1 (D1) to day 43 (D43) CBCTs. Overall survival was associated with TV reduction from D1 to D43 (hazard ratio [HR] 0.557, 95% CI 0.39–0.79, P=.0009). For every 10% decrease in TV from D1 to D43, the risk of death decreased by 44.3%. For patients whose TV decreased ≤39.3 or <39.3%, log-rank test demonstrated a separation in survival (P=.02), with median survivals of 31 months versus 10 months, respectively. Neither local recurrence (HR 0.791, 95% CI 0.51–1.23, P=.29), nor distant recurrence (HR 0.78, 95% CI 0.57–1.08, P=.137) correlated with TV decrease from D1 to D43. Histologic subtype showed no impact on our findings. Conclusions TV reduction as determined by CBCT during CRT as part of routine care predicts post-CRT survival. Such knowledge may justify intensification of RT or application of additional therapies. Assessment of genomic characteristics of these tumors may permit a better understanding of behavior or prediction of therapeutic outcomes.