Radiologic Evaluation of Small Renal Masses (II): Posttreatment Management

The increase in the detection of small renal masses (SRMs) and their best knowledge leads to a change in the therapeutic management of these lesions. The use of a less aggressive surgical technique or even an expectant attitude is the current tendency, in order to preserve as much renal function as possible. Imaging techniques are essential in the followup of these lesions. It allows us to know the postsurgical changes and possible complications due to treatment and the presence of local recurrence and metastases. Furthermore, a close radiological followup of SRM related to ablative treatments is mandatory. The purpose of this article is to reveal the imaging features of complications due to surgical or ablative treatments, local recurrence and metastasis, as well as their followup

Small Renal Masses: Incidental Diagnosis, Clinical Symptoms, and Prognostic Factors

Introduction. The small renal masses (SRMs) have increased over the past two decades due to more liberal use of imaging techniques. SRMs have allowed discussions regarding their prognostic, diagnosis, and therapeutic approach. Materials and methods. Clinical presentation, incidental diagnosis, and prognosis factors of SRMs are discussed in this review. Results. SRMs are defined as lesions less than 4 cm in diameter. SRM could be benign, and most malignant SMRs are low stage and low grade. Clinical symptoms like hematuria are very rare, being diagnosed by chance (incidental) in most cases. Size, stage, and grade are still the most consistent prognosis factors in (RCC). An enhanced contrast SRM that grows during active surveillance is clearly malignant, and its aggressive potential increases in those greater than 3 cm. Clear cell carcinoma is the most frequent cellular type of malign SRM. Conclusions. Only some SRMs are benign. The great majority of malign SRMs have good prognosis (low stage and grade, no metastasis) with open or laparoscopic surgical treatment (nephron sparing techniques). Active surveillance is an accepted attitude in selected cases

Active surveillance in prostate cancer: role of available biomarkers in daily practice

Prostate cancer (PCa) is the most commonly diagnosed cancer in men. The diagnosis is currently based on PSA levels, which are associated with overdiagnosis and overtreatment. Moreover, most PCas are localized tumours; hence, many patients with low-/very low-risk PCa could benefit from active surveillance (AS) programs instead of more aggressive, active treatments. Heterogeneity within inclusion criteria and follow-up strategies are the main controversial issues that AS presently faces. Many biomarkers are currently under investigation in this setting; however, none has yet demonstrated enough diagnostic ability as an independent predictor of pathological or clinical progression. This work aims to review the currently available literature on tissue, blood and urine biomarkers validated in clinical practice for the management of AS patients

Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy

Background: PCA3 has been included in a nomogram outperforming previous clinical models for the prediction of any prostate cancer (PCa) and high grade PCa (HGPCa) at the initial prostate biopsy (IBx). Our objective is to validate such IBx-specific PCA3-based nomogram. We also aim to optimize the use of this nomogram in clinical practice through the definition of risk groups. Methods: Independent external validation. Clinical and biopsy data from a contemporary cohort of 401 men with the same inclusion criteria to those used to build up the reference’s nomogram in IBx. The predictive value of the nomogram was assessed by means of calibration curves and discrimination ability through the area under the curve (AUC). Clinical utility of the nomogram was analyzed by choosing thresholds points that minimize the overlapping between probability density functions (PDF) in PCa and no PCa and HGPCa and no HGPCa groups, and net benefit was assessed by decision curves. Results: We detect 28 % of PCa and 11 % of HGPCa in IBx, contrasting to the 46 and 20 % at the reference series. Due to this, there is an overestimation of the nomogram probabilities shown in the calibration curve for PCa. The AUC values are 0.736 for PCa (C.I.95 %:0.68–0.79) and 0.786 for HGPCa (C.I.95 %:0.71–0.87) showing an adequate discrimination ability. PDF show differences in the distributions of nomogram probabilities in PCa and not PCa patient groups. A minimization of the overlapping between these curves confirms the threshold probability of harboring PCa >30 % proposed by Hansen is useful to indicate a IBx, but a cut-off > 40 % could be better in series of opportunistic screening like ours. Similar results appear in HGPCa analysis. The decision curve also shows a net benefit of 6.31 % for the threshold probability of 40 %. Conclusions: PCA3 is an useful tool to select patients for IBx. Patients with a calculated probability of having PCa over 40 % should be counseled to undergo an IBx if opportunistic screening is required

Variabilidad dentro del Registro Nacional multicéntrico en Vigilancia Activa; cuestionario a urólogos

Introducción: Nuestro objetivo principal es describir la utilización actual en España de la vigilancia activa (VA) identificando áreas de potencial mejora. Métodos: Un cuestionario generado en AEU/PIEM/2014/0001 (NCT02865330) fue remitido a todos los investigadores asociados (IA) durante los meses de enero-marzo del 2016. Incluía 7 dominios diferentes cubriendo diferentes aspectos en VA. Resultados: Treinta y tres de cuarenta y un IA respondieron el cuestionario. La VA es principalmente controlada por los Servicios de Urología (87,9%). Hubo una gran heterogeneidad en las clásicas variables clínico-patológicas como criterios de selección. La densidad de antígeno prostático específico (PSAd) solo se usaba en el 36,4% IA. La RMmp era claramente infrautilizada como estadificación inicial (6%). Solo el 27,3% reconocía un alto nivel de experiencia en RMmp de sus colegas radiólogos. Con relación a la biopsia de confirmación, la mayoría de los centros utilizaban la vía transrectal y solo 2/33 la vía transperineal/software de fusión. La mitad de los IA entrevistados pasaron a tratamiento activo ante progresión patológica a Gleason 7 (3 + 4). No existió consenso en cuanto a cuándo pasar a estrategia de observación. Conclusiones: El estudio demostró la infrautilización del consentimiento informado y de los cuestionarios de calidad de vida. El PSAd no se incluía como elemento decisor en la estrategia inicial en la mayoría. Se plasmó una desconfianza en la experiencia de los urólogos con la RMmp y una infrautilización de la vía transperineal, así como la no existencia de consenso en los protocolos de seguimiento y en los criterios de tratamiento activo., confirmando la necesidad de estudios prospectivos analizando el papel de la RMmp y los biomarcadores. Background: Our main objective was to report the current use of active surveillance in Spain and to identify areas for potential improvement. Methods: A questionnaire generated by the Platform for Multicentre Studies of the Spanish Urology Association (AEU/PIEM/2014/0001, NCT02865330) was sent to all associate researchers from January to March 2016. The questionnaire included 7 domains covering various aspects of active surveillance. Results: Thirty-three of the 41 associate researchers responded to the questionnaire. Active surveillance is mainly controlled by the urology departments (87.9%). There was considerable heterogeneity in the classical clinical-pathological variables as selection criteria. Only 36.4% of the associate researchers used prostate-specific antigen density (PSAd). Multiparametric magnetic resonance imaging (mpMRI) was clearly underused as initial staging (6%). Only 27.3% of the researchers stated that their radiology colleagues had a high level of experience in mpMRI. In terms of the confirmation biopsy, most of the centres used the transrectal pathway, and only 2 out of 33 used the transperineal pathway or fusion software. Half of the researchers interviewed applied active treatment when faced with disease progression to Gleason 7 (3+4). There was no consensus on when to transition to an observation strategy. Conclusions: The study showed the underutilisation of informed consent and quality-of-life questionnaires. PSAd was not included as a decisive element in the initial strategy for most researchers. There was a lack of confidence in the urologists’ mpMRI experience and an underutilisation of the transperineal pathway. There was also no consensus on the follow-up protocols and active treatment criteria, confirming the need for prospective studies to analyse the role of mpMRI and biomarkers

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks. Applied Sciences. 11(2):1-17. https://doi.org/10.3390/app11020844S117112Marra, G., Ploussard, G., Futterer, J., & Valerio, M. (2019). Controversies in MR targeted biopsy: alone or combined, cognitive versus software-based fusion, transrectal versus transperineal approach? World Journal of Urology, 37(2), 277-287. doi:10.1007/s00345-018-02622-5Ahdoot, M., Lebastchi, A. H., Turkbey, B., Wood, B., & Pinto, P. A. (2019). Contemporary treatments in prostate cancer focal therapy. Current Opinion in Oncology, 31(3), 200-206. doi:10.1097/cco.0000000000000515Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Allen, P. D., Graham, J., Williamson, D. C., & Hutchinson, C. E. (s. f.). Differential Segmentation of the Prostate in MR Images Using Combined 3D Shape Modelling and Voxel Classification. 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006. doi:10.1109/isbi.2006.1624940Freedman, D., Radke, R. J., Tao Zhang, Yongwon Jeong, Lovelock, D. M., & Chen, G. T. Y. (2005). Model-based segmentation of medical imagery by matching distributions. IEEE Transactions on Medical Imaging, 24(3), 281-292. doi:10.1109/tmi.2004.841228Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407-1417. doi:10.1118/1.2842076Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234-241. doi:10.1007/978-3-319-24574-4_28He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.322Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi:10.1109/tpami.2016.2572683He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2016.90Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV). doi:10.1109/3dv.2016.79Zhu, Q., Du, B., Turkbey, B., Choyke, P. L., & Yan, P. (2017). Deeply-supervised CNN for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2017.7965852To, M. N. N., Vu, D. Q., Turkbey, B., Choyke, P. L., & Kwak, J. T. (2018). Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery, 13(11), 1687-1696. doi:10.1007/s11548-018-1841-4Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.243Zhu, Y., Wei, R., Gao, G., Ding, L., Zhang, X., Wang, X., & Zhang, J. (2018). Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. Journal of Magnetic Resonance Imaging, 49(4), 1149-1156. doi:10.1002/jmri.26337Wang, Y., Ni, D., Dou, H., Hu, X., Zhu, L., Yang, X., … Wang, T. (2019). Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound. IEEE Transactions on Medical Imaging, 38(12), 2768-2778. doi:10.1109/tmi.2019.2913184Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., Walker, P. M., & Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, 60, 8-31. doi:10.1016/j.compbiomed.2015.02.009Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359-373. doi:10.1016/j.media.2013.12.002Zhu, Q., Du, B., & Yan, P. (2020). Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Transactions on Medical Imaging, 39(3), 753-763. doi:10.1109/tmi.2019.2935018He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.123Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. doi:10.1109/tkde.2009.191Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.58Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759329Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., … Yang, X. (2019). Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net. Medical Physics, 46(7), 3194-3206. doi:10.1002/mp.13577Orlando, N., Gillies, D. J., Gyacskov, I., Romagnoli, C., D’Souza, D., & Fenster, A. (2020). Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Medical Physics, 47(6), 2413-2426. doi:10.1002/mp.14134Karimi, D., Zeng, Q., Mathur, P., Avinash, A., Mahdavi, S., Spadinger, I., … Salcudean, S. E. (2019). Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Medical Image Analysis, 57, 186-196. doi:10.1016/j.media.2019.07.005PROMISE12 Resultshttps://promise12.grand-challenge.org/Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203-211. doi:10.1038/s41592-020-01008-

Prostate Cancer Patients Under Active Surveillance with a Suspicious Magnetic Resonance Imaging Finding Are at Increased Risk of Needing Treatment: Results of the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) Consortium

Background: The inclusion criterion for active surveillance (AS) is low- or intermediate-risk prostate cancer. The predictive value of the presence of a suspicious lesion at magnetic resonance imaging (MRI) at the time of inclusion is insufficiently known. Objective: To evaluate the percentage of patients needing active treatment stratified by the presence or absence of a suspicious lesion at baseline MRI. Design, setting, and participants: A retrospective analysis of the data from the multicentric AS GAP3 Consortium database was conducted. The inclusion criteria were men with grade group (GG) 1 or GG 2 prostate cancer combined with prostate-specific antigen <20 ng/ml. We selected a subgroup of patients who had MRI at baseline and for whom MRI results and targeted biopsies were used for AS eligibility. Suspicious MRI was defined as an MRI lesion with Prostate Imaging Reporting and Data System (PI-RADS)/Likert ≥3 and for which targeted biopsies did not exclude the patient for AS. Outcome measurements and statistical analysis: The primary outcome was treatment free survival (FS). The secondary outcomes were histological GG progression FS and continuation of AS (discontinuation FS). Results and limitations: The study cohort included 2119 patients (1035 men with nonsuspicious MRI and 1084 with suspicious MRI) with a median follow-up of 23 (12–43) mo. For the whole cohort, 3-yr treatment FS was 71% (95% confidence interval [CI]: 69–74). For nonsuspicious MRI and suspicious MRI groups, 3-yr treatment FS rates were, respectively, 80% (95% CI: 77–83) and 63% (95% CI: 59–66). Active treatment (hazard ratio [HR] = 2.0, p < 0.001), grade progression (HR = 1.9, p < 0.001), and discontinuation of AS (HR = 1.7, p < 0.001) were significantly higher in the suspicious MRI group than in the nonsuspicious MRI group. Conclusions: The risks of switching to treatment, histological progression, and AS discontinuation are higher in cases of suspicious MRI at inclusion. Patient summary: Among men with low- or intermediate-risk prostate cancer who choose active surveillance, those with suspicious magnetic resonance imaging (MRI) at the time of inclusion in active surveillance are more likely to show switch to treatment than men with nonsuspicious MRI

Strain balanced quantum posts for intermediate band solar cells

In this work we present strain balanced InAs quantum post of exceptional length in the context of photovoltaics. We discuss the general properties of these nanostructures and their impact in the practical implementation of an intermediate band solar cell. We have studied the photocurrent generated by strain balanced quantum posts embedded in a GaAs single crystal, and compared our results with quantum dot based devices. The incorporation of phosphorous in the matrix to partially compensate the accumulated stress enables a significant increase of the quantum post maximum length. The relative importance of tunneling and thermal escape processes is found to depend strongly on the geometry of the nanostructures. tunneling and thermal escape processes is found to depend strongly on the geometry of the nanostructures