proviz - a machine learning software, to support targeting of prostate biopsies on mr images in biopsy-naive patients

Conditions: Prostate Cancer Diagnosis

Summary: to perform a traditional feasibility clinical investigation, as defined in iso 14155:2020, to investigate preliminary feasibility, safety, and clinical performance information of a near-final design of the investigational software. this will be performed through a prospective clinical study on biopsy naïve men with suspected prostate cancer examined with mri at st. olavs hospital, trondheim, norway, in order to adequately plan an appropriate pivotal clinical investigation.

in this prospective study, after referral for suspected prostate cancer, the patient is scanned with magnetic resonance imaging (mri) in accordance with guidelines of the standardized healthcare pathway. for consenting patients, the images are interpreted in two ways: first, in the conventional manner, i.e., manually by a radiologist, to determine whether clinically significant cancer is suspected. if so, the radiologist will delineate the suspicious lesions. in the second approach, the software will perform the same task as the radiologist, but automatically. if either or both interpretations point to significant cancer, the patient will be sent for targeted biopsy sampling. histopathologic evaluation of the samples will then determine whether significant cancer is present in each of the targeted lesions (delineated by the radiologist, software, or both). feasibility is evaluated by measuring the overall failure rate of the software. this is measured by the technical performance log automatically generated by the software, which records all errors and failures during the study. if the record showed an overall failure rate less than 10% across all subjects, the software will be considered feasible. the safety of the software is evaluated by the records of the serious adverse device effects (sades) of the software during the study. the software will be considered safe with no occurrence of death or serious injury. the results of the histopathological evaluation will be used to evaluate the performance of the investigational software. this allows comparisons to be made between results obtained with the traditional manual approach alone, with the software alone, and with the manual approach assisted by the software. statistical analysis will be performed to determine if there are significant differences and if the software adds value.

Eligibility criteria:
inclusion criteria: * biopsy-naive men undergoing mri examination for suspected prostate cancer via the norwegian standardized care pathway. * patients who give consent to participate during the enrollment period. exclusion criteria: * patients who have undergone a biopsy for prostate cancer in the past 3 years. * patients who currently enrolled in an active surveillance program for prostate cancer. * patients who have had hip replacements that may affect the quality of the image. * patients with claustrophobia. * patients who intolerance to glucagon or buscopan. * patients who unable to sign the informed consent themselves.

Keywords: prostate magnetic resonance imaging artificial intelligence machine learning computer-aided diagnosis radiomics