Iva Halilaj

Background: Making informed decisions about clinical trial participation can be overwhelming for patients due to the complexity of trial information, potential risks and benefits, and the emotional burden of a recent diagnosis. Patient decision aids (PDAs) simplify this process by providing clear information on treatment options, empowering patients to actively participate in shared decision-making with their doctors. While PDAs have shown promise in various health care contexts, their use in clinical trials, particularly in the form of trial-specific patient decision aids (tPDAs), remains underused. Objective: This study aims to address the challenge of patient comprehension of traditional clinical trial materials. We developed a freely accessible, user-friendly tPDA within the context of the ImmunoSABR phase 2 trial. The tPDA aimed to enhance informed decision-making regarding trial participation. The primary endpoint was usability, quantitatively measured by the System Usability Scale (SUS). Secondary endpoints included time spent on the tPDA, patient satisfaction ratings, and participants’ self-reported level of understanding of the trial. Methods: We developed the tPDA following the International Patient Decision Aid Standards and validated it through a structured, 3-phase iterative evaluation process. An initial evaluation was performed with 17 computer scientists who had expertise in biomedical applications, ensuring technical robustness. The content and usability were further refined through evaluations involving 10 clinicians and 8 medical students, focusing on clinical accuracy and user-friendliness. Finally, the tool was tested by 6 patients eligible for the ImmunoSABR trial to assess real-world applicability and patient-centered design. Results: Evaluations demonstrated the tPDA’s effectiveness in enhancing informed decision-making, directly addressing our primary end point of usability with an overall mean SUS score of 79.4 (SD 15.9), indicative of good usability. Addressing our secondary endpoints, patients completed the tPDA efficiently, with the majority (4/6) finishing in under 30 minutes, and all but 1 within 60 minutes. Qualitative feedback highlighted significant improvements in patients’ understanding of the trial details, reinforcing the tPDA’s role in facilitating better patient engagement and comprehension. Conclusions: Our study demonstrates the feasibility and potential of tPDAs to enhance patient comprehension and engagement in clinical trials. Integrating tPDAs offers a valuable addition to traditional paper-based and verbal communication methods, promoting informed decision-making and patient-centered care.

Background: Making informed decisions about clinical trial participation can be overwhelming for patients due to the complexity of trial information, potential risks and benefits, and the emotional burden of a recent diagnosis. Patient decision aids (PDAs) simplify this process by providing clear information on treatment options, empowering patients to actively participate in shared decision-making (SDM) with their doctors. While PDAs have shown promise in various healthcare contexts, their use in clinical trials, particularly in the form of trial-specific PDAs (tPDAs), remains underutilized. Objective: To address the challenge of patient comprehension of traditional clinical trial materials, we developed a freely accessible, user-friendly tPDA within the context of the ImmunoSABR phase 2 trial. The tPDA aimed to enhance informed decision-making regarding trial participation. The primary endpoint was usability, quantitatively measured by the System Usability Scale (SUS). Secondary endpoints included time spent on the tPDA, patient satisfaction ratings, and participants’ self-reported level of understanding of the trial. Methods: We developed the tPDA following the International Patient Decision Aid Standards (IPDAS) and validated through a structured, three-phase iterative evaluation process. Initial evaluation was performed with 17 computer scientists who had expertise in biomedical applications, ensuring technical robustness. The content and usability were further refined through evaluations involving 10 clinicians and 8 medical students, focusing on clinical accuracy and user-friendliness. Lastly, the tool was tested by six patients eligible for the ImmunoSABR trial, to assess real-world applicability and patient-centered design. Results: Evaluations demonstrated the tPDA’s effectiveness in enhancing informed decision-making, directly addressing our primary endpoint of usability with an overall mean SUS score of 79.4, indicative of good usability. Addressing our secondary endpoints, patients completed the tPDA efficiently, with the majority (four out of six) finishing in under 30 minutes, and all but one within 60 minutes. The mean satisfaction rating was 9.5 out of 10. Qualitative feedback highlighted significant improvements in patients’ understanding of the trial details, reinforcing the tPDA’s role in facilitating better patient engagement and comprehension. Conclusion: Our study demonstrates the feasibility and potential of tPDAs to enhance patient comprehension and engagement in clinical trials. Integrating tPDAs offers a valuable addition to traditional paper-based and verbal communication methods, promoting informed decision-making and patient-centered care. Trial registration: ImmunoSABR Protocol Code: NL67629.068.18; EudraCT: 2018–002583-11; Clinicaltrials.gov: NCT03705403 and NCT04604470 ; ISRCTN ID: ISRCTN49817477 ; Date of registration: 03-April-2019.

Background/Objectives: Radiomics has seen substantial growth in medical imaging; however, its potential in optical coherence tomography (OCT) has not been widely explored. We systematically evaluate the repeatability and reproducibility of handcrafted radiomics features (HRFs) from OCT scans of benign nevi and examine the impact of bin width (BW) selection on HRF stability. The effect of using stable features on a radiomics classification model was also assessed. Methods: In this prospective study, 20 volunteers underwent test–retest OCT imaging of 40 benign nevi, resulting in 80 scans. The repeatability and reproducibility of HRFs extracted from manually delineated regions of interest (ROIs) were assessed using concordance correlation coefficients (CCCs) across BWs ranging from 5 to 50. A unique set of stable HRFs was identified at each BW after removing highly correlated features to eliminate redundancy. These robust features were incorporated into a multiclass radiomics classifier trained to distinguish benign nevi, basal cell carcinoma (BCC), and Bowen’s disease. Results: Six stable HRFs were identified across all BWs, with a BW of 25 emerging as the optimal choice, balancing repeatability and the ability to capture meaningful textural details. Additionally, intermediate BWs (20–25) yielded 53 reproducible features. A classifier trained with six stable features achieved a 90% accuracy and AUCs of 0.96 and 0.94 for BCC and Bowen’s disease, respectively, compared to a 76% accuracy and AUCs of 0.86 and 0.80 for a conventional feature selection approach. Conclusions: This study highlights the critical role of BW selection in enhancing HRF stability and provides a methodological framework for optimizing preprocessing in OCT radiomics. By demonstrating the integration of stable HRFs into diagnostic models, we establish OCT radiomics as a promising tool to aid non-invasive diagnosis in dermatology.

Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.

(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models.

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Background: The current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice. Methods: In this pilot study, we performed a literature search in the PubMed and WHO databases to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and were published online together with supporting documentation and links to the associated articles. Results: The open source website currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work. Conclusion: We have successfully demonstrated in this pilot study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.

Objective The current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice. Methods In this proof-of-principle study, we performed a literature search in PubMed and WHO database to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and published online together with supporting documentation and links to the associated articles. Results The open source website https://covid19risk.ai/ currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work. Conclusion We have successfully demonstrated in this proof-of-principle study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally-validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.