HRM implementation levers:a multiple case study of the implementability of HRM tools

A qualitative study of HRM programmes in eight different organizations was set up in order to identify factors, called implementability levers, that contributed to the implementability of those programmes. Three types of those levers were found, related to, respectively, the proces of the programme implementation (example: the involvement of line managers in the programme development), the content of the programme (example: the adaptibility of the programme) and the programme’s context (example: the accessability of the HRM department for involved line managers). Levers in each of the categories appeared to have, as regards their impact on the programme’s implementability, a bright as well as a dark side: they tended to promote, in some specific way, as well as to hamper, in another specific way, the implementation of programmes. Taking care of programme implementability thus shows up as a doable, but puzzling, change management-like task of HR managers

Assessment of Diagnostic Accuracy of Dynamic Contrast-Enhanced Ultrasound and Multiparametric MRI Techniques By Comparing Imaging With Radical Prostatectomy Specimens.

Purpose: To evaluate the accuracy of Dynamic Contrast-Enhanced UltraSound imaging (DCE-US) in comparison with multiparametric Magnetic Resonance Imaging (mpMRI) for the detection of prostate cancer, using whole mount radical prostatectomy specimens as reference standard. Materials and Method: For this exploratory cohort study, thirty-eight patients were included with biopsy-proven prostate cancer, that underwent both DCE-US and mpMRI imaging procedures before radical prostatectomy. Two observers for both imaging modalities performed interpretation. The presence of suspicious nodules on imaging was recorded for each of the standardized regions of interest(ROI) and correlated with pathological findings using various methods of correlation. Sensitivity and specificity were determined for each of the imaging observers. The extended McNemar’s test was used to analyse differences in the diagnostic performance between the observers. Results: Between 284 and 316 ROI’s were included in the different analyses. In the most liberal correlation between imaging and pathology the DCE-US observers attained sensitivities of 41-59% with a specificity of 82-87%. The mpMRI results under the same conditions varied between 44-45% for sensitivity and 90-93% for specificity. Under less liberal conditions the first DCE-US observer attained a sensitivity of 46%, which was significantly better than all other observers ranging between 29-33%. Although one of the mpMRI observers reached significantly higher specificity under those same conditions, 89% versus 74%. Conclusion: MpMRI performance in our study is lower than generally reported and the performance of DCE-US varies markedly between observers. Our results show that, under the same conditions, DCE-US imaging can achieve a higher sensitivity than mpMRI imaging. With mpMRI imaging it is possible to achieve a higher specificity.

Multiparametric ultrasound in the detection of prostate cancer: a systematic review

PURPOSE: To investigate the advances and clinical results of the different ultrasound modalities and the progress in combining them into multiparametric UltraSound (mpUS). METHODS: A systematic literature search on mpUS and the different ultrasound modalities included: greyscale ultrasound, computerized transrectal ultrasound, Doppler and power Doppler techniques, dynamic contrast-enhanced ultrasound and (shear wave) elastography. RESULTS: Limited research available on combining ultrasound modalities has presented improvement in diagnostic performance. The data of two studies suggest that even adding a lower performing ultrasound modality to a better performing modality using crude methods can already improve the sensitivity by 13-51 %. The different modalities detect different tumours. No study has tried to combine ultrasound modalities employing a system similar to the PIRADS system used for mpMRI or more advanced classifying algorithms. CONCLUSION: Available evidence confirms that combining different ultrasound modalities significantly improves diagnostic performance

Ultrasound modalities and quantification: developments of multiparametric ultrasonography, a new modality to detect, localize and target prostatic tumors

PURPOSE OF REVIEW: An imaging tool providing reliable prostate cancer (PCa) detection and localization is necessary to improve the diagnostic pathway with imaging targeted biopsies. This review presents the latest developments in existing and novel ultrasound modalities for the detection and localization of PCa. RECENT FINDINGS: The ultrasound modalities that were very promising on introduction (HistoScanning and Doppler) have shown a wane in performance when tested in larger patient populations. In the meantime, novel ultrasound modalities have emerged in the field of PCa detection. Modalities, such as shear wave elastography (SWE) and contrast-enhanced ultrasound (CEUS) show very promising results. SWE produces an absolute elasticity measure and removes the need for manual compression of the tissue. The former allows comparison between scans and patients, the latter reduces the interoperator variability. Quantification of CEUS enables easily interpretable and accurate imaging of the microvascular changes associated with clinically significant prostate tumors. SUMMARY: The novel ultrasound modalities of SWE and CEUS imaging open the door for taking targeted biopsies based on the detection and localization of PCa by these novel modalities. This potentially improves PCa detection wherein significantly reducing the number of biopsy cores

Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound

Introduction and hypothesis: The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately. Design: This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs). Protocol overview: Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported. Statistical analysis: The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ?2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors. Discussion and summary: This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa

Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound

Introduction and hypothesis: The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately. Design: This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs). Protocol overview: Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported. Statistical analysis: The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors. Discussion and summary: This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa