Embodiments predict prostate cancer (PCa) biochemical recurrence (BCR) employing an image acquisition circuit that accesses a first pre-treatment image and a second pre-treatment image of a region of tissue demonstrating PCa, a distension feature circuit that extracts a set of distension features from the first pre-treatment image, and computes a first probability of PCa BCR based on the set of distension features, a radiomics circuit that extracts a set of radiomics features from the second pre-treatment image, and computes a second probability of PCa recurrence based on the set of radiomics feature, a combined tumor induced organ distension with tumor radiomics (COnTRa) circuit that computes a joint probability that the region of tissue will experience PCa BCR based on the first probability and the second probability, and a display circuit that displays the joint probability.

The purpose of this embodiment is to describe a one stop shop for staging prostate cancer and a novel application of supervised target detection algorithms to spatially registered multiparametric MRI images in order to non-invasively detect, locate, and score prostate cancer at the voxel level and measure the tumor volume and assign color to the spatially registered MRI to highlight and display tumors, and detect metastases (specifically in the seminal vesicle). To test the approach advanced by the embodiment, a retrospective study analyzes MRI from 26 patients that had also undergone robotic prostatectomy. Whole-mount sections were stained for histopathologic evaluation and matched to the MRI. The stained sections were independently reviewed by pathologists. All slices of various types of MRI were spatially registered and stitched together. Signatures or image-based biomarkers from registered multiparametric MRI training sets were extracted. The untransformed and whitened-dewhitened transformed signatures (based on the statistics of the normal prostate) from a battery of Gleason scores were applied to the stitched hypercubes. Each voxel in the supervised target map was polled to find the signature that achieved the highest Gleason score likelihood. The Gleason scoring and volume measurements were quantitatively validated by comparing the results from 10 patients with prostate adenocarcinoma to the pathologist's assessment of the histology. High correlation between supervised target detection using whitened-dewhitened transformed signatures and histology was observed (p<0.02). Assigning red, green, and blue to the registered MRI hypercubes effectively displays tumors relative to normal prostate tissue. With only minor modifications, supervised target detection and transformation of target signatures and color display may be used to find metastases, specifically to the seminal vesicles. This novel application of supervised target detection algorithms to spatially registered multi-parametric MRI non-invasively detects, locates, and scores prostate cancer at each voxel level and measures the tumor volume.

A probe and connected system provide ultrasound imaging and optical tomographic imaging of the prostate. A transrectal component carries ultrasound and optical sources and detectors movably connected to a reference element called a probe carrier that provide a reference frame to coregister anatomical and optical tomographic image data.

Embodiments facilitate predicting a patient prostate cancer (PCa) DECIPHER risk group. A first set of embodiments relates to training of a machine learning classifier to compute a probability that a patient is a member of a DECIPHER low/intermediate risk group based on radiomic features extracted from bi-parametric magnetic resonance imaging (bpMRI) images. A second set of embodiments relates to classifying a patient as a member of DECIPHER low/intermediate risk group, or DECIPHER high-risk group, based on radiomic features extracted from bpMRI imagery of the patient.

Embodiments facilitate stratification of a patient according to prostate cancer (PCa) risk. A first set of embodiments relates to training of a machine learning classifier to compute a probability that a patient has a low-risk of PCa progression based on intratumoral radiomic features and peritumoral radiomic features extracted from multi-parametric magnetic resonance imaging (mpMRI) images. A second set of embodiments relates to classifying a patient as low-risk of PCa progression, or high-risk of PCa progression, based on radiomic features extracted from mpMRI imagery of the patient.

A diagnostic imaging device includes a probe that uses both an ultrasound transducer and frequency-domain diffuse optical imaging (FD-DOI) to assist with locating and diagnosing sub-tissue anomalies. According to one aspect, the diagnostic imaging device relates to a clip-on cap that can be utilized with existing ultrasound transducers. The diagnostic imaging device described herein can be utilized for image-guided needle biopsy to regions where prostate tissues are highly suspicious for high-grade cancer, as well as for image guided interventions, such as cryotherapy, photodynamic therapy, and brachytherapy for early-stage or localized prostate cancer.

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.

The present invention provides methods of monitoring and measuring tumor-associated free PSA (fPSA) with antibody polypeptides as an indication of androgen receptor signaling. In a particular embodiment, the methods may be used to assess the efficacy of anti-androgen and/or general anti-cancer treatments. The present invention also provides various methods and compositions relating to antibodies that are specific for tumor-associated or intratumoral fPSA. For example, the present invention provides compositions, including pharmaceutical compositions, comprising anti-fPSA antibodies, or fragments or characteristic portions thereof. The present invention further provides various therapeutic and/or diagnostic methods of using anti-fPSA antibodies and/or compositions.

A urine collector includes a receptacle formed of a water soluble material, which may be a bioplastic, and has a receiving volume for collecting urine therein defined by its shape. The receiving volume is equal to or greater than a predetermined threshold volume. A threshold volume indicator is configured to correspond to the predetermined threshold volume. The urine collector is configured to float in water and to remain buoyant for a time sufficient to allow a user to compare a volume of urine collected in the receiving volume to the threshold volume indicator. The urine collector may be stackable. The water soluble material may be layered on a substrate, which may be a thin sheet of paper, and may further be a non-stick paper product, such as parchment paper.

Methods and apparatus for computer-aided prostate condition diagnosis are disclosed. An example computer-aided prostate condition diagnosis apparatus includes memory to store instructions and a processor. The example processor can detect a lesion from an image of a prostate gland and generate a mapping of the lesion from the image to a sector map, the generating the mapping of the lesion comprising identifying a depth region of the lesion, wherein the depth region indicates a location of the lesion along a depth axis. The processor can also provide the sector map comprising a representation of the lesion within the prostate gland mapped from the image to the sector map.