The present disclosure relates to a method for detecting a prostate cancer related biomarker, including contacting the diluted sample with a modulating agent selected from a group consisting of sodium; 3-hydroxy-4-[(2-hydroxynaphthalen-1-yl)diazenyl]naphthalene-1-sulfonate, sodium; 3-hydroxy-4-[(1-hydroxynaphthalen-2-yl)diazenyl]-7-nitronaphthalene-1-sulfonate, triisopropylsilane, and iron(III) chloride, and a luminescent label to obtain a measurement sample. Then, the measurement sample is incubated for a period of time and excited thereafter. Time-resolved luminescence signal of the label in the measurement sample is measured, leading to an increased likelihood of prostate cancer of the human subject if the luminescence signal is at least 50% higher than for a control sample from a human subject without prostate cancer.

An exemplary system, method and computer-accessible medium for characterizing a microstructure of a prostate of a patient can be provided, which can include, for example, generating a magnetic resonance (MR) radiofrequency (RF) pulse(s) by varying (i) a diffusion time, (ii) a diffusion gradient direction, (iii) a diffusion gradient pulse width, or (iv) a diffusion gradient pulse shape, applying the MR RF pulse(s) to the prostate of the patient, receiving a resultant MR signal from the prostate of the patient that can be based on the MR RF pulse(s), determining information regarding a plurality of compartments for the prostate from the resultant MR signal by varying an echo time or a mixing time, and characterizing the microstructure for each of the compartments by applying a microstructural model(s) to each of the compartments.

An implant with one or more sensors to measure fluid and tissues of the patient. The sensor may comprise a mechanical sensor configured to measure growth of a tissue such as BPH or a tumor. Alternatively or in combination, the implant may comprise one or more electrodes to stimulate tissue related to urinary urgency or male sexual function.

A probability map of prostate tumor location is generated and displayed in response to receiving anatomic diagnostic medical imaging, such as from magnetic resonance (MR) scanning. A registration process is performed on the images in relation to a model built of prostate anatomy across different subjects in an enhanced prostate template. A probability map is created of tumor locations followed by transforming the imaging to incorporate the probability map and output a resultant image.

A method for performing magnetic resonance imaging is provided. The method includes providing a magnetic resonance imaging system comprising: a radio frequency receive system comprising a radio frequency receive coil, and a housing, wherein the housing comprises a permanent magnet for providing an inhomogeneous permanent gradient field, a radio frequency transmit system, and a single-sided gradient coil set. The method also includes placing the receive coil proximate a target subject; applying a sequence of chirped pulses via the transmit system; applying a multi-slice excitation along the inhomogeneous permanent gradient field; applying a plurality of gradient pulses via the gradient coil set orthogonal to the inhomogeneous permanent gradient field; acquiring a signal of the target subject via the receive system, wherein the signal comprises at least two chirped pulses; and forming a magnetic resonance image of the target subject.

A computer system that analyzes medical-imaging data to assess a risk for prostate cancer is described. The computer system may compute features (including intensity, texture and/or spatial features) based at least in part on the medical-imaging data. Then, using a pretrained predictive model, the computer system may determine cancer predictions on a voxel-by-voxel basis, based at least in part on the computed features. Note that the pretrained predictive model may include a boosted parallel random forests (BPRF) model with a boosted ensemble of bagging ensemble models, where a given bagging ensemble model includes an ensemble of random forests models. Next, the computer system may provide feedback based on the cancer predictions for the voxels. For a given voxel, the feedback may include a cancer prediction and a location. In some embodiments, for the given voxel, the feedback may include an aggressiveness of the predicted cancer and/or a recommended therapy.

A laser assembly is provided that includes a laser resonator that emits a first light having a first pulse width, and a trigger assembly electrically coupled to the laser resonator to actuate the laser resonator. The laser assembly also includes a sensor configured to detect the first light as the light emits from the laser resonator, and one or more processors coupled to the trigger assembly. The one or more processors are configured to obtain a first time delay interval from when the trigger assembly is actuated to when the sensor detects the first light, and actuate the laser resonator to emit a second light having a second pulse width based on the time delay interval determined.

One example method for AI prediction of prostate cancer outcomes involves receiving an image of prostate tissue; assigning Gleason pattern values to one or more regions within the image using an artificial intelligence Gleason grading model, the model trained to identify Gleason patterns on a patch-by-patch basis in a prostate tissue image; determining relative areal proportions of the Gleason patterns within the image; assigning at least one of a risk score or risk group value to the image based on the determined relative areal proportions; and outputting at least one of the risk score or the risk group value.

The present disclosure relates to a method and a system for imaging a biological tissue. A monochromatic image of the biological tissue is obtained. A texture analysis of the biological tissue is performed using spatial information of the monochromatic image to identity features of the biological tissue. A texture image is generated based on the features of the biological tissue. The biological tissue of the subject is classified as normal or abnormal at least in part based on a comparison between first order statistics of the texture image and predetermined values.

In some examples, a medical system includes a medical device. The medical device may include a housing configured to be implanted in a target site of a patient, a light emitter configured to emit a signal configured to cause a fluorescent marker to emit a fluoresced signal into the target site, and a light detector that may be configured to detect the fluoresced signal. The medical system may include processing circuitry configured to determine a characteristic of the fluorescent marker based on the emitted signal and the fluoresced signal. The characteristic of the fluorescent marker may be indicative of a presence of a compound in the patient, and the processing circuitry may be configured to track the presence of the compound of the patient based on the characteristic of the fluorescent marker.