The optimal timing of salvage androgen deprivation therapy (ADT) following definitive radiation therapy for prostate cancer (PCa) is unknown. This study evaluated the efficacy of early initiation of salvage-ADT (S-ADT) based on predetermined timing among patients with unfavorable PCa treated with high-dose intensity-modulated radiation therapy (IMRT).High-risk (HR) and very-high-risk (VHR) PCa patients treated with IMRT at our institution between September 2000 and December 2010 were analyzed retrospectively. Treatment consisted of high-dose IMRT (78 Gy/39 fractions) combined with 6 months of neoadjuvant-ADT (NA-ADT). S-ADT was initiated when prostate-specific antigen levels exceeded 4.0 ng/mL.In total, 268 (184 HR and 84 VHR) patients were analyzed. The median follow-up period was 114.4 months. The 10-year overall survival (OS), PCa-specific survival (PCSS), biochemical failure (BF), and clinical failure (CF) rates were 82.8%, 97.1%, 27.3%, and 12.8% among the HR PCa patients and 79.4%, 87.9%, 56.2%, and 26.7% among the VHR PCa patients (p = 0.839, = 0.0377, < 0.001, and < 0.001), respectively. The 10-year cumulative incidence rates of urinary and rectal (grades 2-3) toxicities were 22.6% and 5.8%, respectively. No grade 4 or higher toxicities were observed.High-dose IMRT combined with short-term NA-ADT resulted in long-term disease-free status, with acceptable morbidity among approximately three-fourths of the HR PCa patients and nearly half of the VHR PCa patients. Moreover, excellent survival outcomes were achieved by the early S-ADT initiation. This approach may be a promising alternative to uniform provision of long-term ADT.
Abstract Objectives The purpose of this study was to assess the radiological change patterns in skull base meningiomas after conventionally fractionated stereotactic radiotherapy (CFSRT) to determine a simple and valid method to assess the tumor response. Materials and methods Forty-one patients with a benign skull base meningioma treated by CFSRT from March 2007 to August 2015 were retrospectively evaluated. We measured tumor volume (TV), long-axis diameter (LD), and short-axis diameter (SD) on both pre-treatment images and follow-up images of 1, 3, and 5 years after CFSRT, respectively. The paired t test was used to detect differences in the LD and SD change rates. Spearman’s correlation coefficients were calculated to evaluate relationships between the TV and the diameters changes. Results The number of available follow-up MRIs that was performed at 1, 3, and 5 years after the CFSRT was 41 (100%), 34 (83%), and 23 (56%), respectively. The change rates of SD were significantly higher than those of LD at every time point and more strongly correlated with the change rates of tumor volume at 3 and 5 years after CFSRT. Conclusions SD may be useful as a simple indicator of the tumor response for skull base meningioma after CFSRT. Key Points • The change rate in short-axis diameter is a useful and simple indicator of the response of skull base meningioma to conventionally fractionated stereotactic radiotherapy. • Conventionally fractionated stereotactic radiotherapy for skull base meningioma achieved excellent 5-year local control.
Abstract Background Radiomics analysis using on‐board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. Purpose This study investigated the factors that influence the reproducibility of radiomic features extracted from on‐board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features. Methods The phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On‐board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone‐beam computed tomography (kV‐CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV‐CBCT, megavoltage‐CBCT (MV‐CBCT), and megavoltage computed tomography (MV‐CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first‐order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter‐based, and 744 (i.e., 93 × 8) wavelet filter‐based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature. Results For internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter‐tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter‐based and seven wavelet filter‐based features, were indicated as highly reproducible features. The gray‐level run‐length matrix (GLRLM) was classified as containing the most frequent features ( N = 8), followed by the gray‐level dependence matrix ( N = 7) and gray‐level co‐occurrence matrix ( N = 1) features. Conclusions We developed the standard phantom for radiomics analysis of kV‐CBCT, MV‐CBCT, and MV‐CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on‐board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter‐based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.
