A method for predicting the aggressiveness or indolence of prostate cancer in a patient previously diagnosed with prostate cancer, comprising the steps of: providing a blood-based sample obtained from the prostate cancer patient; conducting mass spectrometry of the blood-based sample with a mass spectrometer and thereby obtaining mass spectral data including intensity values at a multitude of m/z features in a spectrum produced by the mass spectrometer; performing pre-processing averaging and normalization operations on the mass spectral data; classifying the sample with a programmed computer implementing a classifier operating on the intensity values of the sample after the pre-processing operations are performed and a set of stored intensity values of m/z features from a constitutive set of mass spectra obtained from blood-based samples of a multitude of prostate cancer patients having a Total Gleason score of 6 or lower, wherein the classifier is defined from one or more master classifiers generated by conducting logistic regression with extreme drop-out on a multitude of filtered mini-classifiers, which satisfy thresholds on performance metrics, with regularization, wherein the mini-classifiers execute a K-nearest neighbor classification algorithm on a set of features; wherein the classifier produces a class label for the blood based sample of High, Early, or the equivalent signifying the patient is at high risk of early progression/relapse of the prostate cancer indicating aggressiveness of the prostate cancer, or Low, Late or the equivalent, signifying that the patient is at low risk of early progression/relapse of the prostate cancer indicating indolence of the prostate cancer. providing a blood-based sample obtained from the prostate cancer patient; conducting mass spectrometry of the blood-based sample with a mass spectrometer and thereby obtaining mass spectral data including intensity values at a multitude of m/z features in a spectrum produced by the mass spectrometer; performing pre-processing averaging and normalization operations on the mass spectral data; classifying the sample with a programmed computer implementing a classifier operating on the intensity values of the sample after the pre-processing operations are performed and a set of stored intensity values of m/z features from a constitutive set of mass spectra obtained from blood-based samples of a multitude of prostate cancer patients having a Total Gleason score of 6 or lower, wherein the classifier is defined from one or more master classifiers generated by conducting logistic regression with extreme drop-out on a multitude of filtered mini-classifiers, which satisfy thresholds on performance metrics, with regularization, wherein the mini-classifiers execute a K-nearest neighbor classification algorithm on a set of features; wherein the classifier produces a class label for the blood based sample of High, Early, or the equivalent signifying the patient is at high risk of early progression/relapse of the prostate cancer indicating aggressiveness of the prostate cancer, or Low, Late or the equivalent, signifying that the patient is at low risk of early progression/relapse of the prostate cancer indicating indolence of the prostate cancer. <EOS>