Pathologic states within the prostate may be reflected by changes in serum proteomic patterns. To test this hypothesis, we analyzed serum proteomic mass spectra with a bioinformatics tool to reveal the most fit pattern that discriminated the training set of sera of men with a histopathologic diagnosis of prostate cancer (serum prostate-specific antigen [PSA] > or =4 ng/mL) from those men without prostate cancer (serum PSA level <1 ng/mL). Mass spectra of blinded sera (N = 266) from a test set derived from men with prostate cancer or men without prostate cancer were matched against the discriminating pattern revealed by the training set. A predicted diagnosis of benign disease or cancer was rendered based on similarity to the discriminating pattern discovered from the training set. The proteomic pattern correctly predicted 36 (95%, 95% confidence interval [CI] = 82% to 99%) of 38 patients with prostate cancer, while 177 (78%, 95% CI = 72% to 83%) of 228 patients were correctly classified as having benign conditions. For men with marginally elevated PSA levels (4-10 ng/mL; n = 137), the specificity was 71%. If validated in future series, serum proteomic pattern diagnostics may be of value in deciding whether to perform a biopsy on a man with an elevated PSA level.
SBRT has an important role to play in treating early-stage NSCLC, particularly for medically inoperable patients with limited other treatment options. Shared decision-making with patients should be performed in all cases to ensure the patient understands the risks related to SBRT, the side effects, and the alternative treatments available.
A proton therapy workflow based on CBCT provided clinical indicators similar to those using rCT for patients with lung cancer with considerable anatomic changes.
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