scholarly journals Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

2021 ◽  
Vol 11 ◽  
Author(s):  
Jake Kendrick ◽  
Roslyn Francis ◽  
Ghulam Mubashar Hassan ◽  
Pejman Rowshanfarzad ◽  
Robert Jeraj ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Metastatic Prostate Cancer ◽  
External Validation ◽  
Temporal Variations ◽  
The Body ◽  
Disease Spread ◽  
Imaging Biomarkers ◽  
Artificial Intelligence Techniques ◽  
Multiple Imaging

Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

scholarly journals A Multidisciplinary Approach for Evaluating Spatial and Temporal Variations in Water Quality

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 853 ◽  
Author(s):  
Viet Thang Le ◽  
Nguyen Hong Quan ◽  
Ho Huu Loc ◽  
Nguyen Thi Thanh Duyen ◽  
Tran Duc Dung ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Confusion Matrix ◽  
Temporal Variations ◽  
Machine Learning Techniques ◽  
Spatial And Temporal Variations ◽  
Support Vector ◽  
Domestic Water ◽  
Supply Source ◽  
Artificial Intelligence Techniques

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality.

A novel artificial intelligence biomarker: Validation of the automated bone scan index (aBSI) in veterans with metastatic prostate cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17507-e17507
Author(s):  
Vipal P. Durkal ◽  
Nicholas George Nickols ◽  
Matthew Rettig
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Overall Survival ◽  
Bone Scan ◽  
Metastatic Prostate Cancer ◽  
Unmet Need ◽  
Bone Scan Index ◽  
Cancer Specific Survival ◽  
Cancer Specific Mortality ◽  
Bone Scans

e17507 Background: Prostate cancer commonly metastasizes to the bone and is associated with reduced survival, pathologic fractures and bone pain. The assessment of bone lesions is made with the technetium Tc99m(99mTc) bone scan, which relies on the subjective interpretation of radiologists and has a wide interobserver variability. There is an unmet need for a more objective and quantifiable measurement tool. Progenics Pharmaceuticals has introduced an automated bone scan index (aBSI), which employs artificial intelligence to quantify skeletal tumor burden. The automated bone scan index has been prospectively validated and is reproducible in large Phase III studies. The aBSI was validated by our study in the Veteran population at the West LA VA Medical Center. Methods: The first positive technetium 99 Tc99m bone scans of veterans diagnosed with metastatic, castration-sensitive prostate cancer were evaluated. Since 2011, a total of 107 evaluable patient bone scans were studied (n = 107). Patients with visceral metastases were excluded to evaluate only those with skeletal metastases. An automated bone scan index (aBSI) was generated for each scan using the Progenics Pharmaceuticals’ artificial intelligence platform. Multivariate analysis of aBSI with overall survival, prostate cancer specific survival, time from diagnosis to first positive bone scan, age at diagnosis, ethnicity, and Gleason score was assessed. Results: The study demonstrated a wide range of aBSI values (Range 0-16.84). Values calculated above the Median aBSI value (1.0) were prognostic for Overall Survival (p = 0.0009) and Prostate Cancer-Specific Survival (p = 0.0011). Patients in the highest quartile of aBSI values (range 5.2-16.84) showed a statistically significant Prostate Cancer-Specific Mortality (p = 0.0300) when compared to the lowest two quartiles (Range 0-1.07). The time from diagnosis to the first positive Tc99m bone scan statistically correlated with aBSI values (p = 0.0016). Multivariate analysis using Cox regression was utilized in the final statistical analysis of prostate cancer-specific mortality and overall survival. Conclusions: The automated Bone Scan Index provides a quantifiable and validated artificial intelligence biomarker to address an unmet need among metastatic prostate cancer patients. This tool was validated among Veterans, a pertinent population that is commonly affected by metastatic prostate cancer.

scholarly journals A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation

2021 ◽  
Vol 11 ◽  
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Arianna Defeudis ◽  
Giuseppe Stranieri ◽  
Marco Calandri ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
External Validation ◽  
Specific Antigen ◽  
Support Vector ◽  
Cad System ◽  
Intelligence System ◽  
Beneficial Impact ◽  
Aggressive Tumors ◽  
Validation Set

In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.

scholarly journals Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 959
Author(s):  
Jasper J. Twilt ◽  
Kicky G. van Leeuwen ◽  
Henkjan J. Huisman ◽  
Jurgen J. Fütterer ◽  
Maarten de Rooij
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
External Validation ◽  
Lesion Detection ◽  
Resonance Imaging ◽  
Clinical Workflow ◽  
The Impact ◽  
Lesion Classification

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

Nomogram for Overall Survival of Patients With Progressive Metastatic Prostate Cancer After Castration

2002 ◽  
Vol 20 (19) ◽  
pp. 3972-3982 ◽  
Author(s):  
Oren Smaletz ◽  
Howard I. Scher ◽  
Eric J. Small ◽  
David A. Verbel ◽  
Alex McMillan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Metastatic Prostate Cancer ◽  
Failure Time ◽  
Karnofsky Performance Status ◽  
External Validation ◽  
Multivariable Analysis ◽  
Specific Antigen ◽  
Entire Group ◽  
Validation Data ◽  
Data Set

PURPOSE: To develop a pretreatment prognostic model for survival of patients with progressive metastatic prostate cancer after castration using parameters that are measured during routine clinical management. PATIENTS AND METHODS: Pretreatment clinical and biochemical determinants from 409 patients enrolled onto 19 consecutive therapeutic protocols from June 1989 through January 2000 were evaluated. The factors selected were age, Karnofsky performance status (KPS), hemoglobin (HGB), prostate-specific antigen (PSA), lactate dehydrogenase (LDH), alkaline phosphatase (ALK), and albumin. These factors were combined in an accelerated failure time regression model to produce a nomogram to predict median, 1-year, and 2-year survival. The nomogram was validated internally and externally using data from a multicenter randomized trial of suramin plus hydrocortisone versus hydrocortisone alone. RESULTS: The median survival of the entire group was 15.8 months (range, 0.9 to 77.8 months); 87% have died. In multivariable analysis, KPS, HGB, ALK, albumin, and LDH were significantly associated with survival (P < .05), whereas age and PSA were not. All seven factors were included in the nomogram. When applied to the external validation data set, the nomogram achieved a concordance index of 0.67. Calibration plots suggested that the nomogram was well calibrated for all predictions. CONCLUSION: A nomogram derived from pretreatment parameters that are measured on a routine basis was constructed. It can be used to predict the median, 1-year, and 2-year survival of patients with progressive castrate metastatic disease with reasonable accuracy. The information is useful to assess prognosis, guide treatment selection, and design clinical trials.

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