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Background: Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models. Purpose: In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors. Materials and methods: Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: (1) after the first consultation of the patient, and (2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area Under the Curve (AUC) analysis. Results: A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH, respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models. Conclusion: The models substantially outperformed ROs' predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact

Item does not contain fulltext, BACKGROUND: Radiotherapy prevents local recurrence of breast cancer after breast-conserving surgery. We evaluated the effect of a supplementary dose of radiation to the tumor bed on the rates of local recurrence among patients who received radiotherapy after breast-conserving surgery for early breast cancer. METHODS: After lumpectomy and axillary dissection, patients with stage I or II breast cancer received 50 Gy of radiation to the whole breast in 2-Gy fractions over a five-week period. Patients with a microscopically complete excision were randomly assigned to receive either no further local treatment (2657 patients) or an additional localized dose of 16 Gy, usually given in eight fractions by means of an external electron beam (2661 patients). RESULTS: During a median follow-up period of 5.1 years, local recurrences were observed in 182 of the 2657 patients in the standard-treatment group and 109 of the 2661 patients in the additional-radiation group. The five-year actuarial rates of local recurrence were 7.3 percent (95 percent confidence interval, 6.8 to 7.6 percent) and 4.3 percent (95 percent confidence interval, 3.8 to 4.7 percent), respectively (P<0.001), yielding a hazard ratio for local recurrence of 0.59 (99 percent confidence interval, 0.43 to 0.81) associated with an additional dose. Patients 40 years old or younger benefited most; at five years, their rate of local recurrence was 19.5 percent with standard treatment and 10.2 percent with additional radiation (hazard ratio, 0.46 [99 percent confidence interval, 0.23 to 0.89]; P=0.002). At five years in the age group 41 to 50 years old, no differences were found in rates of metastasis or overall survival (which were 87 and 91 percent, respectively). CONCLUSIONS: In patients with early breast cancer who undergo breast-conserving surgery and receive 50 Gy of radiation to the whole breast, an additional dose of 16 Gy of radiation to the tumor bed reduces the risk of local recurrence, especially in patient

Contains fulltext : 185882.pdf (publisher's version ) (Closed access)

Contains fulltext : 168361.pdf (publisher's version ) (Closed access)