The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study.

Objectives: Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility - Core 10 Dimensions (QLU-C10D), derived from the EORTC QLQ-30 questionnaire, the Patient-Reported Outcome Measurement Information System (PROMIS) preference score (PROPr), and the EuroQoL-5-Dimensions-5-Levels (EQ-5D-5L), yield different HSU values due to different modeling and different underlying descriptive scales. For example the QLU-C10D includes cancer-relevant dimensions such as nausea. This study aimed to investigate how these differences in descriptive scales contribute to differences in HSU scores by comparing scores of cancer patients receiving chemotherapy to those of the general population.

Development of a PROMIS multidimensional cancer-related fatigue (mCRF) form using modern psychometric techniques.

Purpose: To develop a PRO assessment of multidimensional cancer-related fatigue based on the PROMIS fatigue assessments.

Method: Cancer patients reporting fatigue were recruited from a comprehensive cancer care center and completed a survey including 39 items from the PROMIS Cancer Item Bank-Fatigue. Component and factor structures of the fatigue items were explored with Monte Carlo parallel factor and Mokken analyses, respectively.

FACE-Q for Measuring Patient-reported Outcomes after Facial Skin Cancer Surgery: Cross-cultural Validation.

Background: Facial skin cancer and its surgical treatment can affect health-related quality of life. The FACE-Q Skin Cancer Module is a patient-reported outcome measure that measures different aspects of health-related quality of life and has recently been translated into Dutch. This study aimed to evaluate the performance of the translated version in a Dutch cohort using modern psychometric measurement theory (Rasch).

Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study.

Objectives: Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis.

Methods: We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST).

Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?

Objectives: Response assessment to neoadjuvant systemic treatment (NAST) to guide individualized treatment in breast cancer is a clinical research priority. We aimed to develop an intelligent algorithm using multi-modal pretreatment ultrasound and tomosynthesis radiomics features in addition to clinical variables to predict pathologic complete response (pCR) prior to the initiation of therapy.

Methods: We used retrospective data on patients who underwent ultrasound and tomosynthesis before starting NAST.

Development and Validation of a Novel Literature-Based Method to Identify Disparity-Sensitive Surgical Quality Metrics.

Background: Disparity in surgical care impedes the delivery of uniformly high-quality care. Metrics that quantify disparity in care can help identify areas for needed intervention. A literature-based Disparity-Sensitive Score (DSS) system for surgical care was adapted by the Metrics for Equitable Access and Care in Surgery (MEASUR) group.

Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004).

Background: We sought to predict clinically meaningful changes in physical, sexual, and psychosocial well-being for women undergoing cancer-related mastectomy and breast reconstruction 2 years after surgery using machine learning (ML) algorithms trained on clinical and patient-reported outcomes data.

Patients And Methods: We used data from women undergoing mastectomy and reconstruction at 11 study sites in North America to develop three distinct ML models. We used data of ten sites to predict clinically meaningful improvement or worsening by comparing pre-surgical scores with 2 year follow-up data measured by validated Breast-Q domains.

Physical well-being recovery trajectories by reconstruction modality in women undergoing mastectomy and breast reconstruction: Significant predictors and health-related quality of life outcomes.

Objectives: We sought to identify trajectories of patient-reported outcomes, specifically physical well-being of the chest (PWBC), in patients who underwent postmastectomy breast reconstruction, and further assessed its significant predictors, and its relationship with health-related quality of life (HRQOL).

Methods: We used data collected as part of the Mastectomy Reconstruction Outcomes Consortium study within a 2-year follow-up in 2012-2017, with 1422, 1218,1199, and 1417 repeated measures at assessment timepoints of 0,3,12, and 24 months, respectively. We performed latent class growth analysis (LCGA) in the implant group (IMPG) and autologous group (AUTOG) to identify longitudinal change trajectories, and then assessed its significant predictors, and its relationship with HRQOL by conducting multinomial logistic regression.

Ensuring fair, safe, and interpretable artificial intelligence-based prediction tools in a real-world oncological setting.

Background: Cancer patients often experience treatment-related symptoms which, if uncontrolled, may require emergency department admission. We developed models identifying breast or genitourinary cancer patients at the risk of attending emergency department (ED) within 30-days and demonstrated the development, validation, and proactive approach to in-production monitoring of an artificial intelligence-based predictive model during a 3-month simulated deployment at a cancer hospital in the United States.

Methods: We used routinely-collected electronic health record data to develop our predictive models.

Effectiveness of routine provision of feedback from patient-reported outcome measurements for cancer care improvement: a systematic review and meta-analysis.

Background: Research shows that feeding back patient-reported outcome information to clinicians and/or patients could be associated with improved care processes and patient outcomes. Quantitative syntheses of intervention effects on oncology patient outcomes are lacking.

Objective: To determine the effects of patient-reported outcome measure (PROM) feedback intervention on oncology patient outcomes.

Machine learning and patient-reported outcomes for longitudinal monitoring of disease progression in metastatic breast cancer: a multicenter, retrospective analysis.

Background: Assessments of health-related quality of life (HRQoL) play an important role in transition to palliative care for women with metastatic breast cancer. We developed machine learning (ML) algorithms to analyse longitudinal HRQoL data and identify patients who may benefit from palliative care due to disease progression.

Methods: We recruited patients from two institutions and administered the EuroQoL Visual Analog Scale (EQ-VAS) via an online platform over a 6-month period.

The Development, Deployment, and Evaluation of the CLEFT-Q Computerized Adaptive Test: A Multimethods Approach Contributing to Personalized, Person-Centered Health Assessments in Plastic Surgery.

Background: Routine use of patient-reported outcome measures (PROMs) and computerized adaptive tests (CATs) may improve care in a range of surgical conditions. However, most available CATs are neither condition-specific nor coproduced with patients and lack clinically relevant score interpretation. Recently, a PROM called the CLEFT-Q has been developed for use in the treatment of cleft lip or palate (CL/P), but the assessment burden may be limiting its uptake into clinical practice.

Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients.

Purpose: Clinical management of patients receiving immune checkpoint inhibitors (ICIs) could be informed using accurate predictive tools to identify patients at risk of short-term acute care utilization (ACU). We used routinely collected data to develop and assess machine learning (ML) algorithms to predict unplanned ACU within 90 days of ICI treatment initiation.

Methods: We used aggregated electronic health record data from 7,960 patients receiving ICI treatments to train and assess eight ML algorithms.

Real-Time Student Feedback on the Surgical Learning Environment: Use of a Mobile Application.

Objective: Improvements to the medical student surgical learning environment are limited by lack of granular data and recall bias on end-of-clerkship evaluations. The purpose of this study was to identify specific areas for intervention using a novel real-time mobile application.

Design: An application was designed to obtain real-time feedback from medical students regarding the learning environment on their surgical clerkship.

On the importance of interpretable machine learning predictions to inform clinical decision making in oncology.

Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient's future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions.

The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction.

Background: Machine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction.

Methods: We conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018.

Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data.

Contrary to national guidelines, women with ovarian cancer often receive treatment at the end of life, potentially due to the difficulty in accurately estimating prognosis. We trained machine learning algorithms to guide prognosis by predicting 180-day mortality for women with ovarian cancer using patient-reported outcomes (PRO) data. We collected data from a single academic cancer institution in the United States.

Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison.

Background: There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical example of techniques that facilitate the development of high-quality ML systems including data pre-processing, hyperparameter tuning, and model comparison using open-source software and data.

Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data.

Purpose: The objective of the current study was to develop and test the performances of different ML algorithms which were trained using patient-reported symptom severity data to predict mortality within 180 days for patients with advanced cancer.

Methods: We randomly selected 630 of 689 patients with advanced cancer at our institution who completed symptom PRO measures as part of routine care between 2009 and 2020. Using clinical, demographic, and PRO data, we trained and tested four ML algorithms: generalized regression with elastic net regularization (GLM), extreme gradient boosting (XGBoost) trees, support vector machines (SVM), and a single hidden layer neural network (NNET).

Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international, multicentre analysis.

Background: Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.

Methods: We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses.