In men with low-risk prostate cancer, the efficacy of 70 Gy in 28 fractions over 5.6 weeks is not inferior to 73.8 Gy in 41 fractions over 8.2 weeks, although an increase in late GI/genitourinary adverse events was observed in patients treated with H-RT.

Radiation therapy (RT) is an essential component of effective cancer care and is used across nearly all cancer types. The delivery of RT is becoming more precise through rapid advances in both computing and imaging. The direct integration of magnetic resonance imag-ing (MRI) with linear accelerators represents an exciting development with the potential todramatically impact cancer research and treatment. These impacts extend beyond improved imaging and dose deposition. Real-time MRI-guided RT is actively transforming the work flows and capabilities of virtually every aspect of RT. It has the opportunity to change entirely the delivery methods and response assessments of numerous malignancies. This review intends to approach the topic of MRI-based RT guidance from a vendor neutral and international perspective. It also aims to provide an *

Objective: Carbohydrate antigen 19-9 (CA19-9) is a prognostic marker for patients with pancreatic cancer (PC), but its value as a treatment biomarker is unclear. Summary Background Data: Although CA19-9 is an established prognostic marker for patients with PC, it is unclear how CA19-9 monitoring should be used to guide multimodality treatment and what level of change in CA19-9 constitutes a meaningful treatment response. Methods: CA19-9 measurements at diagnosis (pretx), after completion of all planned neoadjuvant therapy (preop), and after surgery (postop) were analyzed in patients with localized PC who had an elevated CA19-9 (≥35 U/dL) at diagnosis. Patients were classified by: 1) quartiles of pretx CA19-9 (Q1-4); 2) proportional changes in CA19-9 (ΔCA19-9) after the completion of neoadjuvant therapy; 3) normalization (CA19-9 <35 U/dL) of preop CA19-9; and 4) normalization of postop CA19-9. Results: Among 131 patients, the median overall survival (OS) was 30 months; 68 months for the 33 patients in Q1 of pretx CA19-9 (<80 U/dL) compared with 25 months for the 98 patients in Q2-4 (P = 0.03). For the 98 patients in Q2-4, preop CA19-9 declined (from pretx) in 86 (88%), but there was no association between the magnitude of ΔCA19-9 and OS (P = 0.77). Median OS of the 98 patients who did (n = 29) or did not (n = 69) normalize their preop CA19-9 were 46 and 23 months, respectively (P = 0.02). Of the 69 patients with an elevated preop CA19-9, 32 (46%) normalized their postop CA19-9. Failure to normalize preop or postop CA19-9 was associated with a 2.77-fold and 4.03-fold increased risk of death, respectively (P < 0.003) as compared with patients with normal preop CA19-9. Conclusions: Following neoadjuvant therapy, normalization of CA19-9, rather than the magnitude of change, is the strongest prognostic marker for long-term survival.

Changes of radiomic features over time in longitudinal images, delta radiomics, can potentially be used as a biomarker to predict treatment response. This study aims to develop a delta-radiomic process based on machine learning by (1) acquiring and registering longitudinal images, (2) segmenting and populating regions of interest (ROIs), (3) extracting radiomic features and calculating their changes (delta-radiomic features, DRFs), (4) reducing feature space and determining candidate DRFs showing treatment-induced changes, and (5) creating outcome prediction models using machine learning. This process was demonstrated by retrospectively analyzing daily non-contrast CTs acquired during routine CT-guided-chemoradiation therapy for 90 pancreatic cancer patients. A total of 2520 CT sets (28-daily-fractions-per-patient) along with their pathological response were analyzed. Over 1300 radiomic features were extracted from the segmented ROIs. Highly correlated DRFs were ruled out using Spearman correlations. Correlation between the selected DRFs and pathological response was established using linear-regression-models. T test and linear-mixed-effects-models were used to determine which DRFs changed significantly compared with first fraction. A Bayesian-regularization-neural-network was used to build a response prediction model. The model was trained using 50 patients and leave-one-out-cross-validation. Performance was judged using the area-under-ROC-curve. External independent validation was done using data from the remaining 40 patients. The results show that 13 DRFs passed the tests and demonstrated significant changes following 2–4 weeks of treatment. The best performing combination differentiating good versus bad responders (CV-AUC = 0.94) was obtained using normalized-entropy-to-standard-deviation-difference-(NESTD), kurtosis, and coarseness. With further studies using larger data sets, delta radiomics may develop into a biomarker for early prediction of treatment response.

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