Purpose: Radiation-induced normal-tissue toxicities are common, complex, and distressing side effects that affect 90% of patients receiving breast-cancer radiotherapy and 40% of patients post radiotherapy. In this study, the authors investigated the use of spectrophotometry and ultrasound to quantitatively measure radiation-induced skin discoloration and subcutaneous-tissue fibrosis. The study's purpose is to determine whether skin discoloration correlates with the development of fibrosis in breast-cancer radiotherapy. Methods: Eighteen breast-cancer patients were enrolled in our initial study. All patients were previously treated with a standard course of radiation, and the median follow-up time was 22 months. The treated and untreated breasts were scanned with a spectrophotometer and an ultrasound. Two spectrophotometer parameters-melanin and erythema indices-were used to quantitatively assess skin discoloration. Two ultrasound parameters-skin thickness and Pearson coefficient of the hypodermis-were used to quantitatively assess severity of fibrosis. These measurements were correlated with clinical assessments (RTOG late morbidity scores). Results: Significant measurement differences between the treated and contralateral breasts were observed among all patients: 27.3% mean increase in skin thickness (p < 0.001), 34.1% mean decrease in Pearson coefficient (p < 0.001), 27.3% mean increase in melanin (p < 0.001), and 22.6% mean increase in erythema (p < 0.001). All parameters except skin thickness correlated with RTOG scores. A moderate correlation exists between melanin and erythema; however, spectrophotometer parameters do not correlate with ultrasound parameters. Conclusions: Spectrophotometry and quantitative ultrasound are objective tools that assess radiationinduced tissue injury. Spectrophotometer parameters did not correlate with those of quantitative ultrasound suggesting that skin discoloration cannot be used as a marker for subcutaneous fibrosis. These tools may prove useful for the reduction of radiation morbidities and improvement of patient quality of life.

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Purpose/Objective(s): Quantitative Cone Bean CT (CBCT) imaging is on increasing demand for precise image-guided radiation therapy (RT) because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. With more precise treatment monitoring from CBCT, dose delivery errors can be significantly reduced in each fraction or compensated for in subsequent fractions using adaptive RT. However, the current CBCT has severe artifacts mainly due to scatter contamination and its current clinical application is therefore limited to patient setup based only on bony structures. This study's purpose is to develop a learningbased approach to improve CBCT image quality for quantitative analysis during adaptive RT. Materials/Methods: The first step is to build a set of paired training images including planning CT and CBCT. For each pair, the planning CT is used as the regression target of the CBCT. We then remove the uninformative regions, reduce noise, and perform an alignment between CT and CBCT. The proposed correction algorithm consists of two major stages: the training stage and the prediction stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, and the most robust and informative CT-CBCT features are identified by feature selection to train random forests. During the correction stage, we extract the selected features from the new (target) CBCT and feed them into the well-trained forests to predict the corrected CBCT. This prediction-based correction algorithm was tested with brain CBCT and CT images of 9 patients. We performed leave-one-out cross-validation method to evaluate the proposed prediction-based correction algorithm. Results: The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and feature similarity (FSIM) indexes were used to quantify the differences between the corrected CBCT and planning CT. For 9 patients, the mean MAE, PSNR, and FSIM were 5.87AE4.39, 28.47AE4.71, and 0.90AE0.08, respectively, which demonstrated the corrected CBCT prediction accuracy of the proposed learning-based method. Conclusion: To improve CBCT imaging, we have developed a novel learning-based method in which a random forest regression with a patchbased anatomical signature is used to effectively capture the relationship between the planning CT and CBCT. We have demonstrated that this method could significantly reduce scatter artifacts. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore allowing its quantitative use in adaptive RT.

Twenty-three patients (61%) had progressive disease and 15 patients (39%) had no response or a partial response at the conclusion of salvage chemotherapy. Twenty-two patients (58%) underwent high dose chemotherapy and proceeded to HDT and ASCT. The percentage of patients that underwent ASCT who received accelerated fractionated SRT compared to conventionally fractionated SRT was not statistically different (59% vs 56%, pZ0.86). Accelerated fractionation compared to conventional fractionation was associated with a significant improvement in complete response (CR) rate (86% vs 50%, pZ0.015) and 2-year disease free survival (59% vs 19%, pZ0.031). There was a trend towards a significant improvement in 2-year loco-regional control (82% vs 56%, pZ0.052); however, 2-year overall survival (59% vs 63%, pZ0.96) was not significantly different. Conclusion: In patients with DLBCL who were refractory to salvage chemotherapy, the use of salvage radiation therapy was associated with a high rate of conversion to ASCT eligibility in a group of patients that are frequently ineligible for ASCT. The use of accelerated fractionation compared to conventional fractionation SRT was associated with a significantly improve CR rate and DFS. Prospective trials are needed to further identify the patients that would derive the most benefit from accelerated fractionated SRT in this setting.