Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer

Technological advances in biomarkers and imaging tests are creating new avenues to advance precision health for early detection of cancer. These advances have resulted in multiple layers of information that can be used to make clinical decisions, but how to best use these multiple sources of information is a challenging engineering problem due to the high cost and imperfect sensitivity and specificity of these tests. Questions that need to be addressed include which diagnostic tests to choose and how to best integrate them, in order to optimally balance the competing goals of early disease detection and minimal cost and harm from unnecessary testing. To study these research questions, we present new optimization-based models and data-driven analytic methods in three parts to improve early detection of prostate cancer (PCa). In the first part, we develop and validate predictive models to assess individual PCa risk using known clinical risk factors. Because not all men with newly-diagnosed PCa received imaging at diagnosis, we use an established method to correct for verification bias to evaluate the accuracy of published imaging guidelines. In addition to the published guidelines, we implement advanced classification modeling techniques to develop accurate classification rules identifying which patients should receive imaging. We propose a new algorithm for a classification model that considers information of patients with unverified disease and the high cost of misclassifying a metastatic patient. We summarize our development and implementation of state-wide, evidence-based imaging criteria that weigh the benefits and harms of radiological imaging for detection of metastatic PCa. In the second part of this thesis, we combine optimization and machine learning approaches into a robust optimization framework to design imaging guidelines that can account for imperfect calibration of predictions. We investigate efficient and effective ways to combine multiple medical diagnostic tests where the result of one test may be used to predict the outcome of another. We analyze the properties of the proposed optimization models from the perspectives of multiple stakeholders, and we present the results of fast approximation methods that we show can be used to solve large-scale models. In the third and final part of this thesis, we investigate the optimal design of composite multi-biomarker tests to achieve early detection of prostate cancer. Biomarker tests vary significantly in cost, and cause false positive and false negative results, leading to serious health implications for patients. Since no single biomarker on its own is considered satisfactory, we utilize simulation and statistical methods to develop the optimal diagnosis procedure for early detection of PCa consisting of a sequence of biomarker tests, balancing the benefits of early detection, such as increased survival, with the harms of testing, such as unnecessary prostate biopsies. In this dissertation, we identify new principles and methods to guide the design of early detection protocols for PCa using new diagnostic technologies. We provide important clinical evidence that can be used to improve health outcomes of patients while reducing wasteful application of diagnostic tests to patients for whom they are not effective. Moreover, some of the findings of this dissertation have been implemented directly into clinical practice in the state of Michigan. The models and methodologies we present in this thesis are not limited to PCa, and can be applied to a broad range of chronic diseases for which diagnostic tests are available.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143976/1/smerdan_1.pd

Free Prostate-specific Antigen Forms and Kallikrein-related Peptidase 2: Tools for Prostate Cancer Diagnostics

Prostate-specific antigen (PSA) is a marker that is commonly used in estimating prostate cancer risk. Prostate cancer is usually a slowly progressing disease, which might not cause any symptoms whatsoever. Nevertheless, some cases of cancer are aggressive and need to be treated before they become life-threatening. However, the blood PSA concentration may rise also in benign prostate diseases and using a single total PSA (tPSA) measurement to guide the decision on further examinations leads to many unnecessary biopsies, over-detection, and overtreatment of indolent cancers which would not require treatment. Therefore, there is a need for markers that would better separate cancer from benign disorders, and would also predict cancer aggressiveness. The aim of this study was to evaluate whether intact and nicked forms of free PSA (fPSA-I and fPSA-N) or human kallikrein-related peptidase 2 (hK2) could serve as new tools in estimating prostate cancer risk. First, the immunoassays for fPSA-I and free and total hK2 were optimized so that they would be less prone to assay interference caused by interfering factors present in some blood samples. The optimized assays were shown to work well and were used to study the marker concentrations in the clinical sample panels. The marker levels were measured from preoperative blood samples of prostate cancer patients scheduled for radical prostatectomy. The association of the markers with the cancer stage and grade was studied. It was found that among all tested markers and their combinations especially the ratio of fPSA-N to tPSA and ratio of free PSA (fPSA) to tPSA were associated with both cancer stage and grade. They might be useful in predicting the cancer aggressiveness, but further follow-up studies are necessary to fully evaluate the significance of the markers in this clinical setting. The markers tPSA, fPSA, fPSA-I and hK2 were combined in a statistical model which was previously shown to be able to reduce unnecessary biopsies when applied to large screening cohorts of men with elevated tPSA. The discriminative accuracy of this model was compared to models based on established clinical predictors in reference to biopsy outcome. The kallikrein model and the calculated fPSA-N concentrations (fPSA minus fPSA-I) correlated with the prostate volume and the model, when compared to the clinical models, predicted prostate cancer in biopsy equally well. Hence, the measurement of kallikreins in a blood sample could be used to replace the volume measurement which is time-consuming, needs instrumentation and skilled personnel and is an uncomfortable procedure. Overall, the model could simplify the estimation of prostate cancer risk. Finally, as the fPSA-N seems to be an interesting new marker, a direct immunoassay for measuring fPSA-N concentrations was developed. The analytical performance was acceptable, but the rather complicated assay protocol needs to be improved until it can be used for measuring large sample panels.Siirretty Doriast

PI-RADS v2 - What you need to know

Prostate cancer is the second most prevalent cancer in men worldwide and its incidence is expected to double by 2030. Multi-parametric magnetic resonance imaging (MRI) incorporating anatomical and functional imaging has now been validated as a means of detecting and characterising prostate tumours and can aid in risk stratification and treatment selection. The European Society of Urogenital Radiology (ESUR) in 2012 established the Prostate Imaging-Reporting and Data System (PI-RADS) guidelines aimed at standardising the acquisition, interpretation and reporting of prostate MRI. Subsequent experience and technical developments have highlighted some limitations, and a joint steering committee formed by the American College of Radiology, ESUR, and the AdMeTech Foundation have recently announced an updated version of the proposals. We summarise the main proposals of PI-RADS version 2, explore the evidence behind the recommendations, and highlight key differences for the benefit of those already familiar with the original.TB is supported the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.crad.2015.06.09

Sampling the spatial patterns of cancer: Optimized biopsy procedures for estimating prostate cancer volume and Gleason Score

Prostate biopsy is the current gold-standard procedure for prostate cancer diagnosis. Existing prostate biopsy procedures have been mostly focusing on detecting cancer presence. However, they often ignore the potential use of biopsy to estimate cancer volume (CV) and Gleason Score (GS, a cancer grade descriptor), the two surrogate markers for cancer aggressiveness and the two crucial factors for treatment planning. To fill up this vacancy, this paper assumes and demonstrates that, by optimally sampling the spatial patterns of cancer, biopsy procedures can be specifically designed for estimating CV and GS. Our approach combines image analysis and machine learning tools in an atlas-based population study that consists of three steps. First, the spatial distributions of cancer in a patient population are learned, by constructing statistical atlases from histological images of prostate specimens with known cancer ground truths. Then, the optimal biopsy locations are determined in a feature selection formulation, so that biopsy outcomes (either cancer presence or absence) at those locations could be used to differentiate, at the best rate, between the existing specimens having different (high vs. low) CV/GS values. Finally, the optimized biopsy locations are utilized to estimate whether a new-coming prostate cancer patient has high or low CV/GS values, based on a binary classification formulation. The estimation accuracy and the generalization ability are evaluated by the classification rates and the associated receiver-operating-characteristic (ROC) curves in cross validations. The optimized biopsy procedures are also designed to be robust to the almost inevitable needle displacement errors in clinical practice, and are found to be robust to variations in the optimization parameters as well as the training populations

Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors

Purpose The primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade >= 2).Methods This study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients.Results 705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naive (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p < 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG >= 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS >= 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively.Conclusions Exposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions.Trial registration The present study was registered at ClinicalTrials.gov number: NCT05078359

Uuden molekulaarisen menetelmän soveltaminen syöpädiagnostiikkaan : AR-V7 mRNA:n detektio

Huolimatta viimeaikaisista edistysaskelista syöpädiagnostiikan ja syöpähoitojen saralla, on tämä kompleksi ja monitahoinen tauti yhä yksi maailman yleisimmistä kuolinsyistä. Uusia ja nopeita diagnostisia menetelmiä tarvitaan syöpätautien tunnistamiseen niiden aikaisessa kehitysvaiheessa, jotta tautien aiempi prognoosi, parempi riskienhallinta ja tehokkaampi hoito olisivat mahdollisia. Kiinnostus spesifisiin molekulaarisiin biomerkkiaineisiin, jotka toimivat syövän tunnusmerkkeinä, on vähitellen kasvamassa. Näiden merkkiaineiden tunnistaminen nestemäisistä näytemateriaaleista kehittyneiden molekulaaristen diagnostiikkamenetelmien avulla tarjoaa huomattavia etuja perinteisiin onkologiassa käytettäviin kuvantamismenetelmiin verrattuna. Tämän tutkielman tavoite oli tutkia uuden molekulaarisen menetelmän, SIBA®:n (Strand Invasion Based Amplification), soveltuvuutta syöpämerkkiaineiden tunnistamiseen, sekä kehittää testi androgeenireseptorin silmukointivariantti 7 (AR-V7) mRNA:n tunnistamiseen. AR-V7:ä on esitetty hoitovaste-biomerkkiaineeksi potilaissa, joilla on metastaattinen kastraatioresistentti eturauhassyöpä (mCRPC). Tämän variantin ekspressio voi ilmaista kehittynyttä resistenssiä edistyneen eturauhassyövän hoitoon käytetyille hormonaalisille hoidoille. Eturauhassyöpä on maailmanlaajuisesti toiseksi yleisin miehillä esiintyvä syöpä keuhkosyövän jälkeen, ja se voi vähitellen kehittyä pitkälle edenneeksi kuolettavaksi metastaattiseksi kastraatioresistentiksi eturauhassyöväksi, johon androgeeni-deprivaatiohoito ei enää toimi. Positiivisen AR-V7-statuksen on esitetty edustavan tämän pitkälle edenneen eturauhassyövän fenotyyppiä, ja sen tunnistaminen voi auttaa sopivan hoitomuodon valinnassa mCRPC-potilaille. SIBA on uusi isotermaalinen menetelmä nukleiinihappojen monistamiseen ja tunnistamiseen. Teknologia tarjoaa merkittäviä etuja perinteiseen molekulaariseen tunnistusmenetelmään, polymeraasiketjureaktioon (PCR) verrattuna, sillä SIBA-monistus tapahtuu vakiolämpötilassa eikä vaadi lämpösykliseen monistamiseen tarvittavaa hienostunutta laboratoriovälineistöä. Käänteistranskriptio-SIBA (RT-SIBA) mahdollistaa RNA:n käänteistranskription cDNA:ksi samanaikaisesti cDNA:n monistuksen ja tunnistuksen kanssa yksivaiheisessa reaktiossa ja isotermaalisissa olosuhteissa. Menetelmä on osoittanut korkeaa analyyttistä herkkyyttä sekä spesifisyyttä kohdenukleiinihapoille. RT-SIBA-teknologiaa ei ole aiemmin sovellettu ihmisperäisen DNA:n tai RNA:n tunnistamiseen. Tämän tutkielman tärkein havainto oli, että RT-SIBA-teknologiaa voidaan soveltaa molekulaaristen syöpämerkkiaineiden, kuten AR-V7 mRNA:n, nopeaan ja spesifiseen tunnistamiseen. Tässä tutkimuksessa kehitettiin ja optimoitiin kaksi RT-SIBA-testiä, jotka kohdistuivat täyspitkän androgeenireseptori (AR-FL) mRNA:n sekä androgeenireseptorin silmukointivariantti 7 (AR-V7) mRNA:n tunnistamiseen. Näiden testien suorituskykyä arvioitiin testaamalla RNA:ta, joka oli eristetty AR-V7 positiivisista sekä negatiivisista eturauhassyöpäsoluista. Reaktiossa oli samanaikaisesti läsnä myös nestemäistä näytemateriaalia; kokoverta tai plasmaa. Kehitetyt RT-SIBA-testit olivat analyyttisesti erittäin spesifisiä ja herkkiä: ne monistivat alhaisia kopiomääriä kohde-mRNA:ta alle 20 minuutissa ilman epäspesifisten amplikonien muodostumista. Tulokset osoittavat, että RT-SIBA-teknologiaa voidaan hyödyntää AR-V7 ja AR-FL mRNA:n helppoon ja nopeaan tunnistukseen suoraan nestemäisestä näytemateriaalista ilman aikaa vievää näytteenkäsittelyä. Jatkokokeet todellisilla AR-V7-positiivisilla mCRPC-potilaiden kliinisillä näytteillä ovat tarpeellisia, jotta kehitetyt testit voidaan validoida luotettavasti.Despite recent advances in understanding, diagnosis and treatment of cancer, this complex and versatile disease remains one of the leading causes of death worldwide. New and rapid diagnostic methods are needed to detect cancers at their early stages of development, thus enabling earlier prognosis, better risk assessment and more efficient treatment of the disease. There has been an increasing interest in specific molecular biomarkers as the hallmark for cancer research, and the detection of these markers from liquid biopsies using advanced molecular diagnostics methods provides major advantages over the conventional imaging methods currently used in oncology. The aims of this thesis were to examine the applicability of a novel molecular method, SIBA® (Strand Invasion Based Amplification), for the detection of cancer biomarkers, and to develop an assay targeting androgen receptor splice variant 7 (AR-V7) mRNA. The AR-V7 is proposed as a treatment-response biomarker in patients with castration-resistant metastatic prostate cancer (mCRPC). The expression of this variant can indicate resistance to hormonal therapies used for the treatment of advanced prostate cancer. Prostate cancer is the most common cancer after lung cancer in men worldwide and can gradually develop into a highly advanced lethal form, mCRPC, that is not responsive to androgen deprivation therapies. Positive AR-V7 status is suggested to represent the phenotype of this advanced stage of prostate cancer, and its detection can assist in treatment selection for the mCRPC patients. SIBA is a novel isothermal method for the amplification and detection of nucleic acids. The technology offers significant advantages over the more conventional molecular detection method, polymerase chain reaction (PCR), since the amplification reaction occurs at constant temperature and does not require sophisticated laboratory equipment for the thermal cycling. Reverse transcription SIBA (RT-SIBA) enables reverse transcription of RNA to cDNA as well as the simultaneous amplification and detection of the cDNA in one-step reaction under isothermal conditions. The method displays both high analytical sensitivity and specificity to the target nucleic acids. The RT-SIBA technology has not formerly been applied for the detection of human DNA or RNA. The main finding of this thesis was, that the RT-SIBA technology can be applied for rapid detection of specific molecular cancer biomarkers such as the AR-V7 mRNA. In this study, two RT-SIBA assays targeting the full-length androgen receptor (AR-FL) mRNA and the AR splice variant 7 mRNA were developed and optimized. Performance of the assays were evaluated by testing RNA isolates from AR-V7 positive and negative prostate cancer cell lines in the presence of human whole blood and plasma in the reaction. The developed RT-SIBA assays provided high analytical sensitivity and specificity: low copies of the target mRNA were amplified within 20 minutes without the production of non-intended amplicons. The results suggest that the RT-SIBA technology can be utilized for easy and rapid detection of AR-V7 and AR-FL mRNA directly from liquid sample material without a need for time-consuming sample treatment. Further performance evaluation using real AR-V7 positive clinical samples from mCRPC patients is necessary for the reliable validation of the developed assays

Beyond Diagnosis: Evolving Prostate Biopsy in the Era of Focal Therapy

Despite decades of use as the “gold standard” in the detection of prostate cancer, the optimal biopsy regimen is still not universally agreed upon. While important aspects such as the need for laterally placed biopsies and the importance of apical cancer are known, repeated studies have shown significant patients with cancer on subsequent biopsy when the original biopsy was negative and an ongoing suspicion of cancer remained. Attempts to maximise the effectiveness of repeat biopsies have given rise to the alternate approaches of saturation biopsy and the transperineal approach. Recent interest in focal treatment of prostate cancer has further highlighted the need for accurate detection of prostate cancer, and in response, the introduction of transperineal template-guided biopsy. While the saturation biopsy approach and the transperineal template approach increase the detection rate of cancer in men with a previous negative biopsy and appear to have acceptable morbidity, there is a lack of clinical trials evaluating the different biopsy strategies. This paper reviews the evolution of prostatic biopsy and current controversies