<p>Prostate cancer (PCa) is the most common non-cutaneous cancer in men in the United States. Early detection of PCa is essential for improving treatment outcomes and survival rates. However, diagnosis of PCa at an early stage is challenged by the lack of an imaging method that can accurately visualize PCas. Because pathological processes change the mechanical properties of the tissue, elasticity imaging methods have the potential to differentiate PCas from other prostatic tissues. Acoustic radiation force impulse (ARFI) imaging is a relatively new elasticity imaging method that visualizes the local stiffness variations inside soft tissue.</p><p>The work presented in this dissertation investigates the feasibility of prostate ARFI imaging. Volumetric ARFI data acquisition and display methods were developed to visualize anatomic structures and pathologies in <italic>ex vivo </italic>human prostates. The characteristic appearances of various prostatic tissues in ARFI images were identified by correlating ARFI images with McNeal's zonal anatomy and the correlated histological slides, in which prostatic pathologies were delineated by a pathologist blinded to the ARFI images. The results suggest ARFI imaging is able to differentiate anatomic structures and identify suspicious PCa regions in the prostate.</p><p>To investigate the correlation between ARFI displacement amplitudes and the underlying tissue stiffness in the prostate ARFI images, the mechanical properties of prostatic tissues were characterized using a quantitative method, based upon shear wave elasticity imaging (SWEI). Co-registered ARFI and SWEI datasets were acquired in excised prostate specimens to reconstruct the shear moduli of prostatic tissues. The results demonstrated that variations in ARFI displacement amplitudes were inversely related to the underlying tissue stiffness; and the reconstructed shear moduli of prostatic tissues had good agreements with those reported in literature. The study suggests the matched ARFI and SWEI datasets provide complementary</p><p> information about tissue's elasticity. </p><p>To increase the efficiency of the data acquisition, a novel imaging sequence was developed to acquired matched ARFI-SWEI datasets without increasing the number of excitations compared to a conventional ARFI imaging sequence. Imaging parameters were analyzed both theoretically and experimentally. An analytical model was derived to quantify the fundamental accuracy limit in the reconstructed shear modulus, and demonstrated good agreement with the experimental data. The novel sequence was demonstrated in tissue-mimicking phantoms.</p><p>Finally, ARFI imaging sequences were developed in a transrectal probe, and ARFI images were presented from <italic>in vivo</italic> data acquired in patients under radical prostatectomy. The <italic>in vivo</italic> ARFI images demonstrated decreased contrast and resolution as compared to the matched <italic>ex vivo</italic> ARFI data. However, prostate anatomy and some PCa were successfully visualized in the <italic>in vivo</italic> ARFI images. Thus, we conclude that ARFI imaging has the potential to provide image guidance for locating cancerous regions during PCa diagnosis and treatment.</p> / Dissertation

Submitted by PPG Medicina e Ci?ncias da Sa?de (medicina-pg@pucrs.br) on 2018-07-20T19:52:55Z No. of bitstreams: 1 M?RCIA_CRISTINA_GON?ALVES_DE_OLIVEIRA_MARAES.pdf: 3458644 bytes, checksum: 005c22fed45246220ed1f2e0de9490a9 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-07-30T12:47:18Z (GMT) No. of bitstreams: 1 M?RCIA_CRISTINA_GON?ALVES_DE_OLIVEIRA_MARAES.pdf: 3458644 bytes, checksum: 005c22fed45246220ed1f2e0de9490a9 (MD5) / Made available in DSpace on 2018-07-30T12:58:07Z (GMT). No. of bitstreams: 1 M?RCIA_CRISTINA_GON?ALVES_DE_OLIVEIRA_MARAES.pdf: 3458644 bytes, checksum: 005c22fed45246220ed1f2e0de9490a9 (MD5) Previous issue date: 2017-08-30 / Abstract: Today, the incidence of prostate cancer is considered high, however, unlike other malignant tumours, there is an expressive number of cases in which prostate cancer does not progress to clinical disease. The management of patients with prostate cancer should be individually fitted due to the broad behaviour spectrum of this cancer, ranging from low grade tumours with low aggressive biological characteristics to high grade tumours with metastatic capacity. The possibility of predicting the future behavior of the disease allows the selection of the most appropriate conduct for each case. Studies have shown that mpMRI (multiparametric Magnetic Resonance Imaging) has a high negative predictive value for clinically significant prostate cancer, indicating that its application as a screening method and as assessment method of disease progression is promising. To standardize the protocols and reports of prostate mpMRI, the PI-RADS v2 (Prostate Imaging Reporting and Data System version 2) was launched in 2015. Multiparametric Magnetic Resonance Imaging standardized by PI-RADSv2 has been taking a prominent place in the management of prostate cancer, but the specificity and positive predictive value still need to be improved. Purpose: To assess whether the ADC (Apparent diffusion coefficient) value and tumour ADC ratio associated with PI-RADS v2 may increase accuracy in predicting clinically significant prostate cancer. Materials and methods: 91 individuals with suspected prostate cancer were retrospectively studied through mpMRI imaging standardized by PI-RADS v2, obtaining the ADC value from the tumour and the contralateral tissue. The findings were correlated to anatomopathological study (biopsy, prostatectomy or transurethral resection). Results: Accuracy, sensitivity, specificity, positive predictive value and negative predictive value for the consensus between the two reviewers using PI-RADS v2, category 3 associated with categories 4 and 5 for the detection of clinically significant cancer were 70.3%, 97.4%, 50.9%, 58.7% and 96.4% (p <0.001), respectively. The association of the ADC value (<0.795x10-3) to categories 3, 4 and 5 of the PI-RADSv2, in turn, demonstrated accuracy, specificity and positive predictive value of 78.9%, 84.9% and 76.5%; and the association with the tumour ADC ratio (<0.62) presented 77.5%, 86.5% and 77.4% of accuracy, specificity and positive predictive value, respectively. Conclusion: The association of the ADC value and the tumour ADC ratio to the PI-RADS v2 in mpMRI increases the accuracy, specificity and positive predictive value in the detection of aggressive prostate cancer, and may help in the screening of individuals who would undergo invasive procedures and radical therapy, or conservative management, as active surveillance or watchful waiting. / Introdu??o: ? considerada alta a incid?ncia de c?ncer de pr?stata na atualidade, contudo, diferentemente de outras neoplasias, existe um n?mero expressivo de casos em que o c?ncer de pr?stata n?o evolui para a doen?a cl?nica. Por este motivo, o manejo dos pacientes com neoplasia prost?tica deve ser moldado individualmente face ao amplo espectro que varia desde tumores de baixo grau, com caracter?sticas biol?gicas de baixa agressividade, a tumores de alto grau, com capacidade metast?tica. A possibilidade de prever o comportamento futuro da doen?a permite a sele??o da conduta mais adequada para cada caso. Estudos vem comprovando que a Resson?ncia Magn?tica multiparam?trica (RMmp) apresenta um alto valor preditivo negativo para o c?ncer de pr?stata com signific?ncia cl?nica, indicando que sua aplica??o como m?todo de triagem e na avalia??o da progress?o da doen?a ? promissora. Para padronizar os protocolos e os relat?rios da RMmp da pr?stata foi lan?ado em 2015 o PI-RADS v2 (?Prostate Imaging Reporting and Data System? vers?o 2). A RMmp padronizada pelo PI-RADS v2 vem assumindo um lugar de destaque no manejo do c?ncer de pr?stata, contudo, ainda s?o considerados baixos a Especificidade e o Valor Preditivo Positivo. Objetivos: Avaliar se o valor de ADC (?Apparent diffusion coefficient? = Coeficiente de Difus?o Aparente) e a raz?o tumoral do ADC associados ao PI-RADS v2 podem aumentar a acur?cia da RMmp na predi??o do c?ncer de pr?stata com signific?ncia clinica. Materiais e m?todos: Foram estudados retrospectivamente 91 indiv?duos com suspeita de c?ncer de pr?stata, submetidos a RMmp padronizada pelo PI-RADS v2, obtendo-se o ADC quantitativo da les?o e do tecido contralateral. Os achados foram correlacionados ao estudo anatomopatol?gico (bi?psia, prostatectomia ou ressec??o transuretral). Resultados: A acur?cia, sensibilidade, especificidade, valor preditivo positivo e valor preditivo negativo para o consenso entre os dois avaliadores utilizando a RMmp padronizada pelo PI-RADS v2, com a categoria 3 associada as categorias 4 e 5 para a detec??o do c?ncer com signific?ncia cl?nica foram 70,3%; 97,4%; 50,9%; 58,7% e 96,4% (p<0,001), respectivamente. A associa??o do valor do ADC (<0,795x10-3) ?s categorias 3, 4 e 5 do PI-RADS v2, por sua vez, demonstrou acur?cia, especificidade e valor preditivo positivo de 78,9%; 84,9% e 76,5%; e a associa??o com a raz?o tumoral do ADC (< 0,62), apresentou 77,5%; 86,5% e 77,4% de acur?cia, especificidade e valor preditivo positivo, respectivamente. Conclus?o: A associa??o do valor do ADC e da raz?o tumoral do ADC ao PI-RADS v2 na RMmp aumenta a acur?cia, especificidade e valor preditivo positivo na detec??o do c?ncer agressivo da pr?stata, podendo auxiliar na triagem dos indiv?duos e na decis?o entre a conduta agressiva, com procedimentos invasivos e terapia radical, ou a conduta conservadora, com vigil?ncia ativa ou observa??o.

Objectives: The study aimed to evaluate the prediction of Prostate Imaging Reporting and Data System (PI-RADS) with respect to the prostate cancer (PCa) detection rate and tumor aggressiveness in magnetic resonance imaging (MRI)/ultrasound-fusion-biopsy (fusPbx) and in systematic biopsy (sysPbx). Materials and Methods: Six hundred and twenty five patients undergoing multiparametric MRI were investigated. MRI findings were classified using PI-RADS v1 or v2. All patients underwent fusPbx combined with sysPbx (comPbx). The lesion with the highest PI-RADS was defined as maximum PI-RADS (maxPI-RADS). Gleason Score ≥ 7 (3 + 4) was defined as significant PCa. Results: The overall PCa detection rate was 51% ( n = 321; 39% significant PCa). The detection rate was 43% in fusPbx ( n = 267; 34% significant PCa) and 36% in sysPbx ( n = 223; 27% significant PCa). Nine percentage of significant PCa were detected by sysPbx alone. A total of 1,162 lesions were investigated. The detection rate of significant PCa in lesions with PI-RADS 2, 3, 4, and 5 were 9% (18/206), 12% (56/450), 27% (98/358), and 61% (90/148) respectively. maxPI-RADS ≥ 4 was the strongest predictor for the detection of significant PCa in comPbx (OR 2.77; 95% CI 1.81–4.24; p < 0.005). Conclusions: maxPI-RADS is the strongest predictor for the detection of significant PCa in comPbx. Due to a high detection rate of additional significant PCa in sysPbx, fusPbx should still be combined with sysPbx.