Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness.

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.

Validation of an ambient measurement system (AMS) for physical activities in a paediatric population.

Ambient measurement systems (AMSs) can enable continuous assessment of functional performance at home, increasing the availability of data for monitoring of neuromuscular disease. An AMS passively measures movement whenever someone is in range of the sensor, without the need for any wearable sensors. The current study evaluates the performance of an AMS for three metrics associated with functional assessments in Duchenne muscular dystrophy (DMD): ambulation speed, rise-to-stand speed and arm-raise speed. Healthy paediatric subjects performed a series of functional tasks and were graded by both a human rater and an AMS. Linear mixed-effect models were fit to calculate agreement between the two measurement methods. For all activities, the AMS and human rater supplied similar measurements of average speed, with correlation coefficients of 0.76-0.92 and systematic differences ranging in magnitude from 0 to 0.48 m per second. The largest systematic difference was for the 10-m run, which was likely due to human rater reaction time. Systematic differences in arm-raise measurements were due to incomplete execution of movements by test participants. These results are consistent with previous studies comparing automated and manual measurements of movement. This study demonstrates that an AMS device is able to measure ambulation speed, rise-to-stand speed and arm-raise speed in a paediatric population in a controlled setting without the need for complicated installation, calibration or worn sensors.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4).

OBJECTIVE: To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens. METHODS: Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP. Samples were shipped and tested at a central laboratory. Utilizing the LPCP test, a suite of phenotypic biomarkers was analyzed and quantified using objective machine vision software. Biomarkers were objectively ranked via machine learning-derived statistical algorithms (MLDSA) to predict postsurgical adverse pathological features. Sensitivity and specificity were determined by comparing blinded predictions and unblinded RP surgical pathology reports, training MLDSAs on 70% of biopsy cells and testing MLDSAs on the remaining 30% of biopsy cells across the tested patient population. RESULTS: The LPCP test predicted adverse pathologies post-RP with area under the curve (AUC) via receiver operating characteristics analysis of greater than 0.80 and distinguished between Prostate Cancer Grade Groups 1, 2, and 3/Gleason Scores 3 + 3, 3 + 4, and 4 + 3. Further, LPCP derived-biomarker scores predicted Gleason pattern, stage, and adverse pathology with high precision-AUCs>0.80. CONCLUSION: Using MLDSA-derived phenotypic biomarker scores, the LPCP test successfully risk stratified Prostate Cancer Grade Groups 1, 2, and 3 (Gleason 3 + 3 and 7) into distinct subgroups predicted to have surgical adverse pathologies or not with high performance (>0.85 AUC).

Movement measurements at home for multiple sclerosis: walking speed measured by a novel ambient measurement system.

BACKGROUND: Gait disturbance is a major contributor to clinical disability in multiple sclerosis (MS). A sensor was developed to assess walking speed at home for people with MS using infrared technology in real-time without the use of wearables. OBJECTIVE: To develop continuous in-home outcome measures to assess gait in adults with MS. METHODS: Movement measurements were collected continuously for 8 months from six people with MS. Average walking speed and peak walking speed were calculated from movement data, then analyzed for variability over time, by room (location), and over the course of the day. In-home continuous gait outcomes and variability were correlated with standard in-clinic gait outcomes. RESULTS: Measured in-home average walking speed of participants ranged from 0.33 m/s to 0.96 m/s and peak walking speed ranged from 0.89 m/s to 1.51 m/s. Mean total within-participant coefficient of variation for daily average walking speed and peak walking speed were 10.75% and 10.93%, respectively. Average walking speed demonstrated a moderately strong correlation with baseline Timed 25-Foot Walk (rs = 0.714, P = 0.111). CONCLUSION: New non-wearable technology provides reliable and continuous in-home assessment of walking speed.

Walking speed measurement with an Ambient Measurement System (AMS) in patients with multiple sclerosis and walking impairment.

BACKGROUND: Walking speed is an important measure of gait impairment in multiple sclerosis (MS). The clinical assessment of walking speed requires dedicated time, space, and personnel, and may not accurately gauge real-world performance. The term "Ambient Measurement System" (AMS) refers to a new class of device that passively measures walking speed at home, without the need for dedicated space or specialized setup. This study compared an AMS, Echo5D, versus in-clinic standard measures of walking speed on a straight path. METHODS: Twenty participants with MS and walking impairment were recruited from the Cleveland Clinic Mellen Center for MS. Each participant traversed an electronic GAITRite CIRFace (GC) sensor mat four times (two at comfortable pace, two at fast pace). Each participant then performed the Timed 25-Foot Walk (T25FW) twice, measured by a manual stopwatch (SW). All traversals were simultaneously measured by an array of Echo5D devices. Echo5D speeds were correlated with the Patient-Determined Disease Steps and the MS Walking Scale-12 patient-reported outcomes. RESULTS: Pearson correlations between Echo5D and clinical tests ranged from 0.89 to 0.98 (p < 0.0001). No statistically significant bias was found between Echo5D and GC. A small statistically significant bias was found between Echo5D and SW, with Echo5D reporting approximately 5% faster walking speeds in aggregate. CONCLUSIONS: Among MS patients with walking impairments, the Echo5D AMS acquired walking speeds which were closely correlated with the standard measures of GC and SW. The strong agreement supports the use of Echo5D to assess in-home, real-world walking performance in MS.

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

Validation of an ambient measurement system (AMS) for walking speed.

Walking speed is an important indicator of worsening in a variety of neurological and neuromuscular diseases, yet typically is measured only infrequently and in a clinical setting. Passive measurement of walking speed at home could provide valuable information to track the progression of many neuromuscular conditions. The purpose of this study was to validate the measurement of walking speed by a shelf-top ambient measurement system (AMS) that can be placed in a patient's home. Twenty-eight healthy adults (16 male, 12 female) were asked to walk three pre-defined routes two times each (total of 168 traversals). For each traversal, walking speed was measured simultaneously by five sources: two independent AMSs and three human timers with stopwatches. Measurements across the five sources were compared by generalised estimating equations (GEE). Correlation coefficients compared pairwise for walking speeds across the two AMSs, three human timers, and three routes all exceeded 0.86 (p < .0001), and for AMS-to-AMS exceeded 0.92 (p < .0001). Aggregated across all routes, there was no significant difference in measured walking speeds between the two AMSs (p = .596). There was a statistically significant difference between the AMSs and human timers of 8.5 cm/s (p < .0001), which is comparable to differences reported for other non-worn sensors. The tested AMS demonstrated the ability to automatically measure walking speeds comparable to manual observation and recording, which is the current standard for assessing walking speed in a clinical setting. The AMS may be used to detect changes in walking speed in community settings.

Rapid and Short-term Extracellular Matrix-mediated In Vitro Culturing of Tumor and Nontumor Human Primary Prostate Cells From Fresh Radical Prostatectomy Tissue.

OBJECTIVE: To culture prostate cells from fresh biopsy core samples from radical prostatectomy (RP) tissue. Further, given the genetic heterogeneity of prostate cells, the ability to culture single cells from primary prostate tissue may be of importance toward enabling single-cell characterization of primary prostate tissue via molecular and cellular phenotypic biomarkers. METHODS: A total of 260 consecutive tissue samples from RPs were collected between October 2014 and January 2016, transported at 4°C in serum-free media to an off-site central laboratory, dissociated, and cultured. A culture protocol, including a proprietary extracellular matrix formulation (ECMf), was developed that supports rapid and short-term single-cell culture of primary human prostate cells derived from fresh RP samples. RESULTS: A total of 251 samples, derived from RP samples, yielded primary human tumor and nontumor prostate cells. Cultured cells on ECMf exhibit (1) survival after transport from the operating room to the off-site centralized laboratory, (2) robust (>80%) adhesion and survival, and (3) expression of different cell-type-specific markers. Cells derived from samples of increasing Gleason score exhibited a greater number of focal adhesions and more focal adhesion activation as measured by phospho-focal adhesion kinase (Y397) immunofluorescence when patient-derived cells were cultured on ECMf. Increased Ki67 immunofluorescence levels were observed in cells derived from cancerous RP tissue when compared to noncancerous RP tissue. CONCLUSION: By utilizing a unique and defined extracellular matrix protein formulation, tumor and nontumor cells derived from primary human prostate tissue can be rapidly cultured and analyzed within 72 hours after harvesting from RP tissue.