Using patient-reported outcomes to assess and improve prostate cancer brachytherapy

Comparison of quality of life, symptom and functional outcomes following surgical treatment for colorectal neoplasia

BACKGROUND

Colorectal surgical procedures can have a significant impact on quality-of-life (QoL), functional and symptom outcomes. This retrospective study conducted in a tertiary care center evaluated the influence of four colorectal surgical procedures on patient-reported outcome measures (PROMs).

METHODS

512 patients undergoing colorectal neoplasia surgery between June 2015 and December 2017 were identified via the Cabrini Monash Colorectal Neoplasia database. Primary outcomes measured were the mean changes in PROMs following surgery utilizing the International Consortium of Health Outcome Measures colorectal cancer (CRC) PROMs.

RESULTS

242 patients from 483 eligible patients responded (50% participation rate). Responders and non-responders were comparable in median age (72 vs. 70 years), gender (48% vs. 52% male), time from surgery (<1 and >1 year), overall stage at diagnosis and type of surgery. Respondents underwent either a right hemicolectomy, ultra-low anterior resection, abdominoperineal resection or a transanal endoscopic microsurgery/transanal minimally invasive surgery. Right hemicolectomy patients reported the best post-operative function and reduced symptoms, significantly better (P < 0.01) than ultra-low anterior resection patients who reported the worst outcomes in multiple areas (body image, embarrassment, flatulence, diarrhoea, stool frequency). Furthermore, patients undergoing an abdominoperineal resection reported the worst scores for body image, urinary frequency, urinary incontinence, buttock pain, faecal incontinence and male impotence.

CONCLUSIONS

The differences in PROMs in CRC surgical procedures is demonstrable. The worst post-operative functional and symptom scores were reported after either an ultra-low anterior resection or an abdominoperineal resection. Implementation of PROMs will identify and aid early patient referral to allied health and support services.

Detecting prostate cancer using deep learning convolution neural network with transfer learning approach

Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

Patient reported outcomes - experiences with implementation in a University Health Care setting

AIM

Patient-reported outcomes (PROs) have traditionally been implemented through a manual process of paper and pencil with little standardization throughout a Healthcare System. Each practice has asked patients specific questions to understand the patient's health as it pertains to their specialty. These data were rarely shared and there has not been a comparison of patient's health across different specialty domains. We sought to leverage interoperable electronic systems to provide a standardization of PRO assessments across sites of care.

METHODS

University of Utah Health is comprised of four hospitals, 12 community clinics, over 400,000 unique annual patients, and more than 5000 providers. The enterprise wide implementation of PROs started in November of 2015. Patients can complete an assessment at home via email, or within the clinic on a tablet. Each specialty has the opportunity to add additional specialty-specific instruments. We customized the interval with which the patient answers the assessments based on specialty preference in order to minimize patient burden, while maximizing relevant data for clinicians.

RESULTS

Barriers and facilitators were identified in three phases: Pre-implementation, Implementation, and Post-implementation. Each phase was further broken down into technical challenges, content inclusion and exclusion, and organizational strategy. These phases are unique and require collaboration between several groups throughout the organization with support from executive leadership.

DISCUSSION

We are deploying system-wide standard and customized PRO collection with the goals of providing better patient care, improving physician-patient communication, and ultimately improving the value of the care given. Standardized assessment provides any clinician with information to quickly evaluate the overall, physical and mental health of a patient. This information is available real time to aid in patient communication for the clinician.

The use of EORTC measures in daily clinical practice-A synopsis of a newly developed manual

Cancer has increasingly become a chronic condition and the routine collection of patient-reported outcomes (PROs) like quality of life is widely recommended for clinical practice. Nonetheless, the successful implementation of PROs is still a major challenge, although common barriers to and facilitators of their beneficial use are well known. To support health care professionals and other stakeholders in the implementation of the EORTC PRO measures, the EORTC Quality of Life Group provides guidance on issues considered important for their use in daily clinical practice. Herein, we present an outline of the newly developed "'Manual for the use of EORTC measures in daily clinical practice", covering the following issues: * a rationale for using EORTC measures in routine care *selection of EORTC measures, timing of assessments, scoring and presentation of results * aspects of a strategic implementation * electronic data assessment and telemonitoring, and * further use of EORTC measures and ethical considerations. Next to an extensive overview of currently available literature, the manual specifically focuses on knowledge about EORTC measures to give evidence-based recommendations whenever possible and to encourage readers and end-users of EORTC measures to contribute to further needed high-quality research. The manual will be accessible on the EORTC Quality of Life Group website's homepage and will be periodically updated to take into account any new knowledge due to medical, technical, regulatory and scientific advances.

Patient-reported outcome after treatment of urinary incontinence in a multidisciplinary pelvic care clinic

OBJECTIVES

To assess patient-reported outcome and satisfaction regarding urinary incontinence 1 year after the end of treatment in a multidisciplinary pelvic care clinic.

METHODS

A total of 647 patients with urinary incontinence seen in an academic multidisciplinary pelvic care clinic were prospectively included in a specific triage system. Patient-reported improvement of urinary incontinence and patient satisfaction were assessed by telephone survey 1 year after the end of treatment.

RESULTS

Just 15.6% of patients presented with one single pelvic floor problem, most had two or three pelvic functional disorders simultaneously, such as urinary incontinence, pelvic organ prolapse and constipation or fecal incontinence. One year after the end of treatment, of 440 responders (68%), 18.2% reported no remaining complaints, and 33.4% reported only one complaint. Patients reported a significant improvement of the mean severity (scale 0-10) of urinary incontinence from 7.2 ±1.6 pretreatment (=T0) to 4.3 ± 3.0 1 year after the end of treatment (P < 0.001). A total of 20.6% of patients reported no urinary incontinence after 1 year (P < 0.001), and 27.6% (P < 0.001) stopped using incontinence pads. Patients claimed high satisfaction with the clinic and care received, with 35.8% being "satisfied" and 45.5% "very satisfied."

CONCLUSION

At 1 year after the end of treatment, one out of five patients with urinary incontinence recovered completely, and more than one out of four stopped using incontinence absorption pads. One out of three patients went from having a multifactorial health problem to a monofactorial health problem. High satisfaction rates were reported by more than four out of five patients.

Defining a standard set of patient-centered outcomes for men with localized prostate cancer

BACKGROUND

Value-based health care has been proposed as a unifying force to drive improved outcomes and cost containment.

OBJECTIVE

To develop a standard set of multidimensional patient-centered health outcomes for tracking, comparing, and improving localized prostate cancer (PCa) treatment value.

DESIGN, SETTING, AND PARTICIPANTS

We convened an international working group of patients, registry experts, urologists, and radiation oncologists to review existing data and practices.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The group defined a recommended standard set representing who should be tracked, what should be measured and at what time points, and what data are necessary to make meaningful comparisons. Using a modified Delphi method over a series of teleconferences, the group reached consensus for the Standard Set.

RESULTS AND LIMITATIONS

We recommend that the Standard Set apply to men with newly diagnosed localized PCa treated with active surveillance, surgery, radiation, or other methods. The Standard Set includes acute toxicities occurring within 6 mo of treatment as well as patient-reported outcomes tracked regularly out to 10 yr. Patient-reported domains of urinary incontinence and irritation, bowel symptoms, sexual symptoms, and hormonal symptoms are included, and the recommended measurement tool is the Expanded Prostate Cancer Index Composite Short Form. Disease control outcomes include overall, cause-specific, metastasis-free, and biochemical relapse-free survival. Baseline clinical, pathologic, and comorbidity information is included to improve the interpretability of comparisons.

CONCLUSIONS

We have defined a simple, easily implemented set of outcomes that we believe should be measured in all men with localized PCa as a crucial first step in improving the value of care.

PATIENT SUMMARY

Measuring, reporting, and comparing identical outcomes across treatments and treatment centers will provide patients and providers with information to make informed treatment decisions. We defined a set of outcomes that we recommend being tracked for every man being treated for localized prostate cancer.