A novel method for continuous measurements of clinical practice guideline adherence.

Introduction: Clinical practice guidelines (hereafter 'guidelines') are crucial in providing evidence-based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real-world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real-world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web-based dashboard that enables interactive usage and supports various analyses. Method: The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real-world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates. Results: CDTs revealed 68 data items for recording in an information standard. Thirty-two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data. Conclusion: The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision-making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real-world data with external knowledge.

A federated approach to identify women with early-stage cervical cancer at low risk of lymph node metastases.

OBJECTIVE: Lymph node metastases (pN+) in presumed early-stage cervical cancer negatively impact prognosis. Using federated learning, we aimed to develop a tool to identify a group of women at low risk of pN+, to guide the shared decision-making process concerning the extent of lymph node dissection. METHODS: Women with cervical cancer between 2005 and 2020 were identified retrospectively from population-based registries: the Danish Gynaecological Cancer Database, Swedish Quality Registry for Gynaecologic Cancer and Netherlands Cancer Registry. Inclusion criteria were: squamous cell carcinoma, adenocarcinoma or adenosquamous carcinoma; The International Federation of Gynecology and Obstetrics 2009 IA2, IB1 and IIA1; treatment with radical hysterectomy and pelvic lymph node assessment. We applied privacy-preserving federated logistic regression to identify risk factors of pN+. Significant factors were used to stratify the risk of pN+. RESULTS: We included 3606 women (pN+ 11%). The most important risk factors of pN+ were lymphovascular space invasion (LVSI) (odds ratio [OR] 5.16, 95% confidence interval [CI], 4.59-5.79), tumour size 21-40 mm (OR 2.14, 95% CI, 1.89-2.43) and depth of invasion>10 mm (OR 1.81, 95% CI, 1.59-2.08). A group of 1469 women (41%)-with tumours without LVSI, tumour size ≤20 mm, and depth of invasion ≤10 mm-had a very low risk of pN+ (2.4%, 95% CI, 1.7-3.3%). CONCLUSION: Early-stage cervical cancer without LVSI, a tumour size ≤20 mm and depth of invasion ≤10 mm, confers a low risk of pN+. Based on an international privacy-preserving analysis, we developed a useful tool to guide the shared decision-making process regarding lymph node dissection.

An Improved Infrastructure for Privacy-Preserving Analysis of Patient Data.

Incorporating healthcare data from different sources is crucial for a better understanding of patient (sub)populations. However, data centralization raises concerns about data privacy and governance. In this work, we present an improved infrastructure that allows privacy-preserving analysis of patient data: vantage6 v3. For this new version, we describe its architecture and upgraded functionality, which allows algorithms running at each party to communicate with one another through a virtual private network (while still being isolated from the public internet to reduce the risk of data leakage). This allows the execution of different types of algorithms (e.g., multi-party computation) that were practically infeasible before, as showcased by the included examples. The (continuous) development of this type of infrastructure is fundamental to meet the current and future demands of healthcare research with a strong emphasis on preserving the privacy of sensitive patient data.

Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival.

Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the [Formula: see text]-index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ([Formula: see text]-index [Formula: see text]), and in the case of XGB even better ([Formula: see text]-index [Formula: see text]). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models' predictions. We concluded that the difference in performance can be attributed to XGB's ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models' predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

Heterogeneity in Quality of Life of Long-Term Colon Cancer Survivors: A Latent Class Analysis of the Population-Based PROFILES Registry.

BACKGROUND: Long-term colon cancer survivors present heterogeneous health-related quality of life (HRQOL) outcomes. We determined unobserved subgroups (classes) of survivors with similar HRQOL patterns and investigated their stability over time and the association of clinical covariates with these classes. MATERIALS AND METHODS: Data from the population-based PROFILES registry were used. Included were survivors with nonmetastatic (TNM stage I-III) colon cancer (n = 1,489). HRQOL was assessed with the Dutch translation of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 version 3.0. Based on survivors' HRQOL, latent class analysis (LCA) was used to identify unobserved classes of survivors. Moreover, latent transition analysis (LTA) was used to investigate changes in class membership over time. Furthermore, the effect of covariates on class membership was assessed using multinomial logistic regression. RESULTS: LCA identified five classes at baseline: class 1, excellent HRQOL (n = 555, 37.3%); class 2, good HRQOL with prevalence of insomnia (n = 464, 31.2%); class 3, moderate HRQOL with prevalence of fatigue (n = 213, 14.3%); class 4, good HRQOL with physical limitations (n = 134, 9.0%); and class 5, poor HRQOL (n = 123, 8.3%). All classes were stable with high self-transition probabilities. Longer time since the diagnosis, no comorbid conditions, and male sex were associated with class 1, whereas older age was associated with class 4. Clinical covariates were not associated with class membership. CONCLUSION: The identified classes are characterized by distinct patterns of HRQOL and can support patient-centered care. LCA and LTA are powerful tools for investigating HRQOL in cancer survivors. IMPLICATIONS FOR PRACTICE: Long-term colon cancer survivors show great heterogeneity in their health-related quality of life. This study identified five distinct clusters of survivors with similar patterns of health-related quality of life and showed that these clusters remain stable over time. It was also shown that these clusters do not significantly differ in tumor characteristics or received treatment. Cluster membership of long-term survivors can be identified by sociodemographic characteristics but is not predetermined by diagnosis and treatment.

Machine Learning Explainability in Breast Cancer Survival.

Machine Learning (ML) can improve the diagnosis, treatment decisions, and understanding of cancer. However, the low explainability of how "black box" ML methods produce their output hinders their clinical adoption. In this paper, we used data from the Netherlands Cancer Registry to generate a ML-based model to predict 10-year overall survival of breast cancer patients. Then, we used Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to interpret the model's predictions. We found that, overall, LIME and SHAP tend to be consistent when explaining the contribution of different features. Nevertheless, the feature ranges where they have a mismatch can also be of interest, since they can help us identifying "turning points" where features go from favoring survived to favoring deceased (or vice versa). Explainability techniques can pave the way for better acceptance of ML techniques. However, their evaluation and translation to real-life scenarios need to be researched further.

VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.

Answering many of the research questions in the field of cancer informatics requires incorporating and centralizing data that are hosted by different parties. Federated Learning (FL) has emerged as a new approach in which a global model can be generated without disclosing private patient data by keeping them at their original location. Flexible, user-friendly, and robust infrastructures are crucial for bringing FL solutions to the day-to-day work of the cancer epidemiologist. In this paper, we present an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange, VANTAGE6. We provide a detailed description of its conceptual design, modular architecture, and components. We also show a few examples where VANTAGE6 has been successfully used in research on observational cancer data. Developing and deploying technology to support federated analyses - such as VANTAGE6 - will pave the way for the adoption and mainstream practice of this new approach for analyzing decentralized data.

Speech Envelope Enhancement Instantaneously Effaces Atypical Speech Perception in Dyslexia.

OBJECTIVES: Increasing evidence exists that poor speech perception abilities precede the phonological deficits typically observed in dyslexia, a developmental disorder in learning to read. Impaired processing of dynamic features of speech, such as slow amplitude fluctuations and transient acoustic cues, disrupts effortless tracking of the speech envelope and constrains the development of adequate phonological skills. In this study, a speech envelope enhancement (EE) strategy was implemented to reduce speech perception deficits by students with dyslexia. The EE emphasizes onset cues and reinforces the temporal structure of the speech envelope specifically. DESIGN: Speech perception was assessed in 42 students with and without dyslexia using a sentence repetition task in a speech-weighted background noise. Both natural and vocoded speech were used to assess the contribution of the temporal envelope on the speech perception deficit. Their envelope-enhanced counterparts were added to each baseline condition to administer the effect of the EE algorithm. In addition to speech-in-noise perception, general cognitive abilities were assessed. RESULTS: Results demonstrated that students with dyslexia not only benefit from EE but benefit more from it than typical readers. Hence, EE completely normalized speech reception thresholds for students with dyslexia under adverse listening conditions. In addition, a correlation between speech perception deficits and phonological processing was found for students with dyslexia, further supporting the relation between speech perception abilities and reading skills. Similar results and relations were found for conditions with natural and vocoded speech, providing evidence that speech perception deficits in dyslexia stem from difficulties in processing the temporal envelope. CONCLUSIONS: Using speech EE, speech perception skills in students with dyslexia were improved passively and instantaneously, without requiring any explicit learning. In addition, the observed positive relationship between speech processing and advanced phonological skills opens new avenues for specific intervention strategies that directly target the potential core deficit in dyslexia.

The effect of presentation level on spectrotemporal modulation detection.

The understanding of speech in noise relies (at least partially) on spectrotemporal modulation sensitivity. This sensitivity can be measured by spectral ripple tests, which can be administered at different presentation levels. However, it is not known how presentation level affects spectrotemporal modulation thresholds. In this work, we present behavioral data for normal-hearing adults which show that at higher ripple densities (2 and 4 ripples/oct), increasing presentation level led to worse discrimination thresholds. Results of a computational model suggested that the higher thresholds could be explained by a worsening of the spectrotemporal representation in the auditory nerve due to broadening of cochlear filters and neural activity saturation. Our results demonstrate the importance of taking presentation level into account when administering spectrotemporal modulation detection tests.

A framework for computational modelling of interaural time difference discrimination of normal and hearing-impaired listeners.

Different computational models have been developed to study the interaural time difference (ITD) perception. However, only few have used a physiologically inspired architecture to study ITD discrimination. Furthermore, they do not include aspects of hearing impairment. In this work, a framework was developed to predict ITD thresholds in listeners with normal and impaired hearing. It combines the physiologically inspired model of the auditory periphery proposed by Zilany, Bruce, Nelson, and Carney [(2009). J. Acoust. Soc. Am. 126(5), 2390-2412] as a front end with a coincidence detection stage and a neurometric decision device as a back end. It was validated by comparing its predictions against behavioral data for narrowband stimuli from literature. The framework is able to model ITD discrimination of normal-hearing and hearing-impaired listeners at a group level. Additionally, it was used to explore the effect of different proportions of outer- and inner-hair cell impairment on ITD discrimination.

Functional modelling of interaural time difference discrimination in acoustical and electrical hearing.

OBJECTIVE: Interaural time differences (ITDs) are important for sound source localisation. We present a model to predict the just noticeable differences (JNDs) in ITD discrimination for normal hearing and electric stimulation through a cochlear implant. APPROACH: We combined periphery models of acoustic and electric stimulation with a novel JND in the ITD estimation stage, which consists of a shuffled cross correlogram and a binary classifier characterisation method. Furthermore, an evaluation framework is presented based on a large behavioural dataset. MAIN RESULTS: The model correctly predicts behavioural observations for unmodulated stimuli (such as pure tones and electric pulse trains) and modulated stimuli for modulation frequencies below 30 Hz. For higher modulation frequencies, the model predicts the observed behavioural trends, but tends to estimate higher ITD sensitivity. SIGNIFICANCE: The presented model can be used to investigate the implications of modifying the stimulus waveform on ITD sensitivity, and as such be applied in investigating sound encoding strategies.

Predicting phoneme and word recognition in noise using a computational model of the auditory periphery.

Several filterbank-based metrics have been proposed to predict speech intelligibility (SI). However, these metrics incorporate little knowledge of the auditory periphery. Neurogram-based metrics provide an alternative, incorporating knowledge of the physiology of hearing by using a mathematical model of the auditory nerve response. In this work, SI was assessed utilizing different filterbank-based metrics (the speech intelligibility index and the speech-based envelope power spectrum model) and neurogram-based metrics, using the biologically inspired model of the auditory nerve proposed by Zilany, Bruce, Nelson, and Carney [(2009), J. Acoust. Soc. Am. 126(5), 2390-2412] as a front-end and the neurogram similarity metric and spectro temporal modulation index as a back-end. Then, the correlations with behavioural scores were computed. Results showed that neurogram-based metrics representing the speech envelope showed higher correlations with the behavioural scores at a word level. At a per-phoneme level, it was found that phoneme transitions contribute to higher correlations between objective measures that use speech envelope information at the auditory periphery level and behavioural data. The presented framework could function as a useful tool for the validation and tuning of speech materials, as well as a benchmark for the development of speech processing algorithms.

Quantification of long cane usage characteristics with the constant contact technique.

While a number of Electronic Travel Aids (ETAs) have been developed over the past decades, the conventional long cane remains the most widely utilized navigation tool for people with visual impairments. Understanding the characteristics of long cane usage is crucial for the development and acceptance of ETAs. Using optical tracking, cameras and inertial measurement units, we investigated grasp type, cane orientation and sweeping characteristics of the long cane with the constant contact technique. The mean cane tilt angle, sweeping angle, and grip rotation deviation were measured. Grasp type varied among subjects, but was maintained throughout the experiments, with thumb and index finger in contact with the cane handle over 90% of the time. We found large inter-subject differences in sweeping range and frequency, while the sweeping frequency showed low intra-subject variability. These findings give insights into long cane usage characteristics and provide critical information for the development of effective ETAs.