scholarly journals Negative mpMRI Rules Out Extra-Prostatic Extension in Prostate Cancer Before Robot-Assisted Radical Prostatectomy

Author(s):  
Eoin Dinneen ◽  
Clare Allen ◽  
Tom Strange ◽  
Daniel Heffernan-Ho ◽  
Jelena Banjeglav ◽  
...  

The accuracy of multi-parametric MRI (mpMRI) in pre-operative staging of prostate cancer (PCa) remains controversial. Objective: To evaluate the ability of mpMRI to accurately predict PCa extra-prostatic extension (EPE) on a side-specific basis using a risk-stratified 5-point Likert scale. This study also aimed to assess the influence of mpMRI scan quality on diagnostic accuracy. Patients and Methods: We included 124 men who underwent robot-assisted RP (RARP) as part of the NeuroSAFE PROOF study at our centre. Three radiologists retrospectively reviewed mpMRI blinded to RP pathology and assigned a Likert score (1-5) for EPE on each side of the prostate. Each scan was also ascribed a Prostate Imaging Quality (PI-QUAL) score for assessing the quality of the mpMRI scan, where 1 represents poorest and 5 represents best diagnostic quality. Outcome measurements and statistical analyses: Diagnostic performance is presented for binary classification of EPE including 95% confidence intervals and area under the receiver operating characteristic curve (AUC). Results: A total of 231 lobes from 121 men (mean age 56.9 years) were evaluated. 39 men (32.2%), or 43 lobes (18.6%) had EPE. Likert score ≥3 had sensitivity (SE), specificity (SP), NPV, PPV of 90.4%, 52.3%, 96%, 29.9%, respectively, and AUC was 0.82 (95% CI: 0.77-0.86). AUC was 0.63 (95% CI: 0.37-0.9), 0.77 (0.71-0.84) and 0.92 (0.88-0.96) for biparametric scans, PI-QUAL 1-3 and PI-QUAL 4-5 scans, respectively. Conclusions: MRI can be used effectively by genitourinary radiologists to rule out EPE and help inform surgical planning for men undergoing RARP. EPE prediction was more reliable when the MRI scan was a) multi-parametric and b) of a higher image quality according to the PI-QUAL scoring system.

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Francesco Giganti ◽  
Alex Kirkham ◽  
Veeru Kasivisvanathan ◽  
Marianthi-Vasiliki Papoutsaki ◽  
Shonit Punwani ◽  
...  

AbstractProstate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.


2017 ◽  
Vol 121 (4) ◽  
pp. 540-548 ◽  
Author(s):  
Pierre Blanchard ◽  
John W. Davis ◽  
Steven J. Frank ◽  
Jeri Kim ◽  
Curtis A. Pettaway ◽  
...  

2012 ◽  
Vol 53 (1) ◽  
pp. 119-126 ◽  
Author(s):  
Yan Zhang ◽  
Jie Tang ◽  
Yan-mi Li ◽  
Xiang Fei ◽  
En-hui He ◽  
...  

Background Elasticity is an important characteristic of tissue. During an elastography examination, various strain images of lesions are observed, and a suitable classification of strain patterns (SP) may provide vital diagnostic information about lesions. Numerous studies have shown that ultrasound elastography can improve the detection of prostate cancer, but the diagnostic value of SP classification has not yet been fully evaluated. Purpose To investigate the contribution of SP on the characterization of prostate peripheral zone lesions by transrectal real-time tissue elastography (TRTE) in combination with conventional transrectal ultrasonography (TRUS). Material and Methods One hundred and seventy-one patients with suspected prostate cancer underwent TRUS and TRTE examinations. The SPs of the suspicious lesions were classified into five scores by TRTE according to the degree and distribution of strain. All findings were confirmed by transrectal systematic 12-core biopsies and targeted biopsies for suspicious areas detecting by TRUS and/or TRTE. Results One hundred and forty-eight of 171 patients had high-quality TRTE imaging and were included into the study. When a cut-off point of SP score III was used, the area under the receiver-operating characteristic curve (AUC) was, respectively, 0.75 (95% CI: 0.67–0.83), 0.85 (95% CI: 0.78–0.91) and 0.84 (95% CI: 0.77–0.91) for the diagnosis of prostate cancer by TRUS, TRTE and TRTE + TRUS. A linear tendency of SP and Gleason scores was observed in scores III-V. The detection rate of prostate cancer using TRTE-targeted biopsy (75.8%) was significantly higher than that of systematic 12-core biopsy plus TRUS-targeted biopsy (14.5%) ( P = 0.00). Conclusion This study suggests the significant contribution of SP on characterization of prostate peripheral zone lesions and the improvement of TRTE-targeted biopsy on detection of prostate cancer.


Brachytherapy ◽  
2019 ◽  
Vol 18 (3) ◽  
pp. S70
Author(s):  
Ken Nakamura ◽  
Shin Koike ◽  
Noriaki Santo ◽  
Ryo Yabusaki ◽  
Keisuke Aoki ◽  
...  

2019 ◽  
Vol 16 (2) ◽  
pp. 341-350
Author(s):  
Artur Bernardo Silva Reis ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Marcelo Gattass

Prostate cancer is the second most prevalent type of cancer in the male population worldwide. Prostate imaging tests have adopted for the prevention, diagnosis, and treatment. It is known that early detection increases the chances of an effective treatment, improving the prognosis of the disease. This paper proposes an automatic methodology for prostate lesions detection. It consists of the following steps: Extracting candidates for lesions with Wolff algorithm; feature extraction using the Ising model measures and finally the uses support vector machine in the classification of a lesion or healthy tissue. The methodology was validated using a set of 28 exams containing the lesion markings and obtained a sensitivity of 95.92%, specificity of 93.89% and accuracy of 94.16%. These are promising since they were more significant than other methods compared.


2021 ◽  
Author(s):  
Sebastião Rogério da Silva Neto ◽  
Thomás Tabosa Oliveira ◽  
Igor Vitor Teixeira ◽  
Samuel Benjamin Aguiar de Oliveira ◽  
Vanderson Souza Sampaio ◽  
...  

Abstract Background: NTDs primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms, inaccurate serologic tests resulting from cross-reaction and co-infection with other arboviruses. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating the automatic classification of arboviral diseases to support clinical diagnosis based on ML and DL models. Method: We carried out a SLR in which Google Scholar was searched to identify key papers on the topic. From an initial 963 records (956 from string-based search and 7 from single backward snowballing technique), only 15 relevant papers were identified. Results: Results show that current research is focused on the binary classification of Dengue, primarily using Tree based ML algorithms and only one paper was identified using DL. Five papers presented solutions for multi-class problems, covering Dengue (and its levels) and Chikungunya. No papers were identified that investigated models to differentiate between Dengue, Chikungunya, and Zika. Conclusions: The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient's quality of life.


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