Treatment Decision-making Process Research Articles

Factors Influencing Patient Decision-Making in the Treatment of Muscle-Invasive Bladder Cancer

BACKGROUND: In 2023, an estimated 82,290 individuals were diagnosed with bladder cancer in the United States. For muscle-invasive bladder cancer (MIBC), the American Urological Association recommends offering radical cystectomy with cisplatin-based neoadjuvant chemotherapy. However, patients are increasingly requesting alternative treatments. OBJECTIVE: To describe factors influencing selection of radical cystectomy with cisplatin-based neoadjuvant chemotherapy (NAC + RC), radical cystectomy monotherapy (RC), or tri-modality therapy (TMT) among patients with MIBC. METHODS: Individual, semi-structured phone interviews were conducted with 18 adults who underwent MIBC treatment at the University of North Carolina, recruiting six patients each from three treatment groups: 1) NAC + RC, 2) RC, and 3) TMT. Interview transcriptions were qualitatively analyzed using QSR NVivo, with major themes and sub-themes extracted. Patients also completed the Shared Decision-Making Questionnaire (SDM-Q-9; range 0–100). RESULTS: Concern for survival and risks, quality of life, and varied patient preferences for involvement influenced the decision-making process. Concern surrounding sexual function, bladder preservation, and urostomy bags drove patients towards TMT. High levels of shared decision-making were observed overall, with a median SDM-Q-9 score of 95 (IQR 89–100). Patients undergoing TMT reported the highest median SDM-Q-9 score (97, IQR 94–100), while those receiving radical cystectomy alone had the lowest (66, IQR 37–96). CONCLUSIONS: Patients with MIBC described a multifaceted treatment decision-making process, highlighting key influences, concerns, and unmet needs. Understanding this process can help address misconceptions and align treatment choices with patient goals. Physicians can use these insights to engage in shared decision-making, ultimately improving patient experiences and outcomes.

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis

Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans.

Iterative Development of an Interactive Website to Support Shared Decision-Making in Metastatic Breast Cancer.

Recent treatment advances have resulted in significantly increased survival times following metastatic breast cancer (MBC) diagnosis. Novel treatment approaches-and their related side effects-have changed the landscape of MBC treatment decision-making. We developed a prototype of an online educational tool to prepare patients with MBC for shared decision-making with their oncologists. We describe the five phases of tool development: (1) in-depth, semi-structured qualitative interviews and (2) feedback on storyboards of initial content with patients with MBC and oncology providers. This was followed by three phases of iterative feedback with patients in which they responded to (3) initial, non-navigable website content and (4) a beta version of the full website. In the final phase (5), patients newly diagnosed with MBC (N = 6) used the website prototype for 1week and completed surveys assessing acceptability, feasibility, treatment knowledge, preparation for decision-making, and self-efficacy for decision-making. Participants in Phase 1 characterized a cyclical process of MBC treatment decision-making and identified key information needs. Website content and structure was iteratively developed in Phases 2-4. Most participants in Phase 5 (n = 4) accessed the website 2-5 times. All participants who accessed the website at least once (n = 5) felt they learned new information from the website prototype and would recommend it to others newly-diagnosed with MBC. After using the website prototype, participants reported high preparation and self-efficacy for decision-making. This multiphase, iterative process resulted in a prototype intervention designed to support decision-making for MBC patients.

The association between renal pelvis urine density and the risk of severe infectious complications in patient with symptom-free hydronephrosis after shock wave lithotripsy: a multi-center prospective study.

Finding reliable and easy-to-obtain predictors of severe infectious complications after shock wave lithotripsy (SWL) is a major clinical need, particular in symptom-free hydronephrosis. Therefore, we aim to prospectively investigate the predictive value of Hounsfield units (HU) in renal pelvis urine for the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL. This multi-center prospective study was conducted from June 2020 to December 2023. The HU of renal pelvis urine was measured by non-enhanced computed tomography. The severe infectious complications included systemic inflammatory response syndrome, sepsis, and septic shock. Binary logistic regression models assessed the odds ratios (ORs) and 95% confidence intervals (CIs). Finally, 1,436 patients with ureteral stones were enrolled in this study. 8.9% (128/1,436) of patients experienced severe infectious complications after SWL treatment. After adjusting confounding variables, compared with the patients in the lowest renal pelvis urine density quartile, the OR (95% CI) for the highest quartile was 32.36 (13.32, 78.60). There was a positive linear association between the HU value of renal pelvis urine and the risk of severe infectious complications after SWL (P for trend < 0.001). Furthermore, this association was also observed stratified by age, gender, BMI, stone size, stone location and hydronephrosis grade (all P for interaction > 0.05). Additionally, the nonlinear association employed by restricted cubic splines is not statistically significant (nonlinear P = 0.256). The AUROC and 95%CI of renal pelvis urine density were 0.895 (0.862 to 0.927, P value < 0.001). The cut-off value was 12.0 HU with 78.59% sensitivity and 85.94% specificity. This multi-center prospective study demonstrated a positive linear association between HU in renal pelvis urine and the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL, regardless of age, gender, BMI, stone size, stone location, and hydronephrosis grade. These findings might be helpful in the SWL treatment decision-making process.

Grading of glioma tumors using digital holographic microscopy

Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists’ experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.

Patients' experiences of cancer immunotherapy with immune checkpoint inhibitors: A systematic review and thematic synthesis.

To obtain a deeper understanding of peoples' experiences of cancer treatments with immune checkpoint inhibitors (ICIs). ICIs are transforming survival outcomes for many with certain advanced cancers. Given the possibility of unique immune-related adverse events (irAEs), understanding treatment experiences is crucial to identify support needs and provide safe and effective person-centred care. A systematic review of qualitative research and thematic synthesis. To report this review, the Preferred Reporting Items for Systematic Analysis and Meta Analysis (PRISMA) checklist and Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidance have been used. MEDLINE, EMBASE, PsycINFO, CINAHL and Web of Science databases were searched in January 2022 for eligible studies published in English from database inception. Two reviewers independently screened records, identified papers for inclusion and appraised methodological quality using the Critical Appraisal Skills Programme checklist. Themes were developed using thematic synthesis. Eighteen papers were included and three analytical themes developed: immune checkpoint inhibitor treatment decision-making; the experience and impact of immune checkpoint inhibitor treatments; and appraising and responding to irAEs. The synthesis renders visible individuals' unmet information, psychological and practical support needs. It identifies shortcomings in immune checkpoint inhibitor treatment decision-making processes and highlights the need for healthcare professionals to recognise and sensitively handle individuals' treatment expectations. Individuals' understandings of and responses to irAEs are also illustrated, and attention drawn to patients' concerns about healthcare professionals' checkpoint inhibitor and irAEs knowledge. To sensitively manage treatment expectations and uncertainties, and optimise health outcomes, there are distinct points in treatment trajectories where care and support might require adapting and enhancing. This review addresses people's experiences of immune checkpoint inhibitor treatments. The core findings reveal unmet information, psychological and practical support needs. Insights derived from this review will enhance individuals' experiences and outcomes and healthcare professionals' practice. No patient or public involvement.

Role of the systemic immune-inflammation index in predicting spontaneous stone passage in patients with renal colic

ABSTRACT Objectives Renal colic (RC) is one of the most frequent reasons for presentation to the emergency department (ED) and creates a high economic and medical burden. Management strategies for RC range from waiting for spontaneous passage to surgical intervention. However, factors determining spontaneous stone passage (SSP) are still poorly understood. Therefore, in this study, we aimed to investigate the role of the systemic immune-inflammatory index (SII) in predicting SSP. Methods We retrospectively analyzed the data of 924 patients aged over 18 years, who were diagnosed with RC in our clinic between 1 January 2019, and 30 May 2022, and had ureteral stones of ≤ 10 mm. The patients were divided into two groups according to whether they had SSP. The clinical and laboratory characteristics of the patients in the ED were evaluated. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and SII (neutrophil count x platelet count/lymphocyte count) values of the patients were calculated. Stone size and location were also recorded. Results In the univariate analysis of SSP, a ureteral stone size of ≤ 5 mm (p < 0.001), distal ureteral location (p < 0.001), SII (p < 0.001), NLR (p < 0.001), and PLR (p = 0.036) were significantly correlated with SSP. ROC analysis showed that an SII level < 721.8 (Sensitivity %82.6, Specificity %74.7, p < 0.001) was an independent predictor of SSP. Conclusion Our findings showed that a low SII level was associated with SSP and could be used as a predictive marker of SSP as a more valuable parameter than NLR. SII and NLR, together with other indicators, are inflammatory markers that can be used in the clinical decision-making process for ureteral stone treatment.

Nomogram model for predicting cancer-specific mortality in patients with early-onset colorectal cancer: a competing risk analysis insight from the SEER database and an external validation cohort.

Early-onset colorectal cancer (EOCRC) is increasing in incidence and poses a growing threat. Urgent research is needed, especially in survival analysis, to enhance comprehension and treatment strategies. This study aimed to explore the risk factors associated with cancer-specific mortality (CSM) and other-cause mortality (OCM) in patients with EOCRC. Additionally, the study aimed to develop a nomogram predicting CSM using a competitive risk model and validate its accuracy through the use of training, using internal and external cohorts. Data from EOCRC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2008-2017). EOCRC patients who were treated at a tertiary hospital in northeast China between 2014 and 2020 were also included in the study. The SEER data were divided into the training and validation sets at a 7:3 ratio. A univariate Cox regression model was employed to identify prognostic factors. Subsequently, multivariate Cox regression models were applied to ascertain the presence of independent risk factors. A nomogram was generated to visualize the results, which were evaluated using the concordance index (C-index), area under the curve (AUC), and calibration curves. The clinical utility was assessed via decision curve analysis (DCA). Multivariable Cox regression analysis demonstrated that factors such as race, tumor differentiation, levels of carcinoembryonic antigen (CEA), marital status, histological type, American Joint Committee on Cancer (AJCC) stage, and surgical status were independent risk factors for CSM in EOCRC patients. In addition, age, gender, chemotherapy details, CEA levels, marital status, and AJCC stage were established as independent risk factors for OCM in individuals diagnosed with EOCRC. A nomogram was developed using the identified independent risk factors, demonstrating excellent performance with a C-index of 0.806, 0.801, and 0.810 for the training, internal validation, and external validation cohorts, respectively. The calibration curves and AUC further confirmed the accuracy and discriminative ability of the nomogram. Furthermore, the DCA results indicated that the model had good clinical value. In this study, a competing risk model for CSM was developed in EOCRC patients. The model demonstrates a high level of predictive accuracy, providing valuable insights into the treatment decision-making process.

Clinical Implementation of a Noninvasive, Multi-Analyte Droplet Digital PCR Test to Screen for Androgen Receptor Alterations

Recent studies have shown that alterations of the androgen receptor (AR) are associated with resistance to AR-directed therapy in prostate cancer. Thus, it is crucial to develop robust detection methods for AR alterations as predictive biomarkers to enable applicability in clinical practice. We designed and validated five multiplex droplet digital PCR (ddPCR) assays for reliable detection of 12 AR targets including AR amplification, AR-V7 and 10 AR hotspot mutations as well as AR and KLK3 gene expression from plasma derived cell-free (cf)DNA and cfRNA.The assays demonstrated excellent analytical sensitivity and specificity ranging from 95% to 100% (95% CI: 75-100%). Intra- and inter-run variation analyses revealed a high level of repeatability and reproducibility. The developed assays were further applied in peripheral blood samples from 77 patients with advanced prostate cancer to assess their feasibility in a real-world scenario. Optimizing the reverse transcription of RNA increased the yield of plasma-derived cfRNA by 30-fold. Among 23 patients with castration-resistant prostate cancer (CRPC), 6 patients (26.1%) had one or a combination of several AR alterations, while only 2 out of 54 patients (3.7%) in the hormone-sensitive stage showed AR alterations. These findings were consistent with other studies and suggest that implementation of a comprehensive AR status detection in clinical practice is feasible and can support the treatment decision-making process.

How BRCA and homologous recombination deficiency change therapeutic strategies in ovarian cancer: a review of literature.

About 50% of High Grade Serous Ovarian Cancer exhibit a high degree of genomic instability due to mutation of genes involved in Homologous Recombination (HRD) and such defect accounts for synthetic lethality mechanism of PARP inhibitors (PARP-i). Several clinical trials have shown how BRCA and HRD mutational status profoundly affect first line chemotherapy as well as response to maintenance therapy with PARP-i, hence Progression Free Survival and Overall Survival. Consequently, there is urgent need for the development of increasingly reliable HRD tests, overcoming present limitations, as they play a key role in the diagnostic and therapeutic process as well as have a prognostic and predictive value. In this review we offer an overview of the state of the art regarding the actual knowledge about BRCA and HRD mutational status, the rationale of PARPi use and HRD testing (current and in development assays) and their implications in clinical practice and in the treatment decision process, in order to optimize and choose the best tailored therapy in patients with ovarian cancer.

GC-CDSS: Personalized gastric cancer treatment recommendations system based on knowledge graph

BackgroundGastric cancer (GC) is one of the most common malignant tumors in the world, posing a serious threat to human health. Currently, gastric cancer treatment strategies emphasize a multidisciplinary team (MDT) consultation approach. However, there are numerous treatment guidelines and insights from clinical trials. The application of AI-based Clinical Decision Support System (CDSS) in tumor diagnosis and screening is increasing rapidly. ObjectiveThe purpose of this study is to (1) summarize the treatment decision process for GC according to the treatment guidelines in China, and then create a knowledge graph (KG) for GC, (2) based on aforementioned KG, built a CDSS and conducted an initial feasibility evaluation for the current system. MethodsFirstly, we summarized the decision-making process for treatment of GC. Then, we extracted relevant decision nodes and relationships and utilized Neo4j to create the KG. After obtaining the initial node features for building the graph embedding model, graph embedding algorithm, such as Node2Vec and GraphSAGE, were used to construct the GC-CDSS. At last, a retrospective cohort study was used to compare the consistency between GC-CDSS and MDT in treatment decision making. ResultsIn current study, we introduce a GC-CDSS, which is constructed based on Chinese GC treatment guidelines knowledge graph (KG). In the KG, we define four types of nodes and four types of relationships, and it comprise a total of 207 nodes and 300 relationships. Regarding GC-CDSS, the system is capable of providing dynamic and personalized diagnostic and treatment recommendations based on the patient's condition. Furthermore, a retrospective cohort study is conducted to compare GC-CDSS recommendations with those of the MDT group, the overall consistency rate of treatment recommendations between the auxiliary decision system and MDT team is 92.96%. ConclusionsWe construct a GC treatment support system, GC-CDSS, based on KG. The GC-CDSS may help oncologists make treatment decisions more efficient and promote standardization in primary healthcare settings.

Predictors of 2-Year Post-Discharge Mortality in Hospitalized Older Patients.

Understanding prognostic factors for adverse health outcomes is clinically relevant for improving treatment decision-making processes, potentially leading to enhanced patient prognosis. This secondary analysis of a prospective observational study aimed to identify independent factors associated with 2-year post-discharge mortality in acutely hospitalized older patients. All-cause mortality and date of death of 115 patients (83.3 ± 6.3 years, females: n = 75, 65.2%) admitted to acute geriatric wards were determined two years after hospital discharge through telephone interviews. Potential prognostic factors measured at hospital admission included demographic and clinical characteristics, nutritional, cognitive, and psychological status, Fried frailty phenotype, functioning in activities of daily living, locomotor capacity, and 24 h in-hospital mobility and objectively measured physical activity (PA) behaviors. The 2-year mortality rate was 36.7% (n = 41). Univariate and multivariate Cox proportional hazards regression models revealed that mean daily PA level (hazards ratio (HR) = 0.59, 95% confidence interval (CI) 0.90-1.00; p = 0.042), frailty (HR = 3.39, 95% CI 1.20-9.51; p = 0.020), and underweight, in contrast to overweight (HR = 3.10, 95% CI 1.07-9.01; p = 0.038), at hospital admission were independently predictive of post-discharge mortality. PA, frailty, and underweight at hospital admission should be considered when evaluating long-term survival prognosis, establishing risk profiles, and developing personalized care pathways in acute hospital care of older adults.

Personalized decision-making for aneurysm treatment of aneurysmal subarachnoid hemorrhage: development and validation of a clinical prediction tool

BackgroundIn patients with aneurysmal subarachnoid hemorrhage suitable for endovascular coiling and neurosurgical clip-reconstruction, the aneurysm treatment decision-making process could be improved by considering heterogeneity of treatment effect and durability of treatment. We aimed to develop and validate a tool to predict individualized treatment benefit of endovascular coiling compared to neurosurgical clip-reconstruction.MethodsWe used randomized data (International Subarachnoid Aneurysm Trial, n = 2143) to develop models to predict 2-month functional outcome and to predict time-to-rebleed-or-retreatment. We modeled for heterogeneity of treatment effect by adding interaction terms of treatment with prespecified predictors and with baseline risk of the outcome. We predicted outcome with both treatments and calculated absolute treatment benefit. We described the patient characteristics of patients with ≥ 5% point difference in the predicted probability of favorable functional outcome (modified Rankin Score 0–2) and of no rebleed or retreatment within 10 years. Model performance was expressed with the c-statistic and calibration plots. We performed bootstrapping and leave-one-cluster-out cross-validation and pooled cluster-specific c-statistics with random effects meta-analysis.ResultsThe pooled c-statistics were 0.72 (95% CI: 0.69–0.75) for the prediction of 2-month favorable functional outcome and 0.67 (95% CI: 0.63–0.71) for prediction of no rebleed or retreatment within 10 years. We found no significant interaction between predictors and treatment. The average predicted benefit in favorable functional outcome was 6% (95% CI: 3–10%) in favor of coiling, but 11% (95% CI: 9–13%) for no rebleed or retreatment in favor of clip-reconstruction. 134 patients (6%), young and in favorable clinical condition, had negligible functional outcome benefit of coiling but had a ≥ 5% point benefit of clip-reconstruction in terms of durability of treatment.ConclusionsWe show that young patients in favorable clinical condition and without extensive vasospasm have a negligible benefit in functional outcome of endovascular coiling – compared to neurosurgical clip-reconstruction – while at the same time having a substantially lower probability of retreatment or rebleeding from neurosurgical clip-reconstruction – compared to endovascular coiling. The SHARP prediction tool (https://sharpmodels.shinyapps.io/sharpmodels/) could support and incentivize a multidisciplinary discussion about aneurysm treatment decision-making by providing individualized treatment benefit estimates.

Varying (preferred) levels of involvement in treatment decision-making in the intensive care unit before and during the COVID-19 pandemic: a mixed-methods study among relatives

BackgroundIn the intensive care unit (ICU) relatives play a crucial role as surrogate decision-makers, since most patients cannot communicate due to their illness and treatment. Their level of involvement in decision-making can affect their psychological well-being. During the COVID-19 pandemic, relatives’ involvement probably changed. We aim to investigate relatives’ involvement in decision-making in the ICU before and during the pandemic and their experiences and preferences in this regard.MethodsA mixed-methods study among relatives of ICU patients admitted to an ICU before or during the COVID-19 pandemic. Relatives in six ICUs completed a questionnaire (n = 329), including two items on decision-making. These were analysed using descriptive statistics and logistic regression analyses. Subsequently, relatives (n = 24) were interviewed about their experiences and preferences regarding decision-making. Thematic analysis was used for analysing the qualitative data.ResultsNearly 55% of the relatives indicated they were at least occasionally asked to be involved in important treatment decisions and of these relatives 97.1% reported to have had enough time to discuss questions and concerns when important decisions were to be made. During the first COVID-19 wave relatives were significantly less likely to be involved in decision-making compared to relatives from pre-COVID-19. The interviews showed that involvement varied from being informed about an already made decision to deliberation about the best treatment option. Preferences for involvement also varied, with some relatives preferring no involvement due to a perceived lack of expertise and others preferring an active role as the patient’s advocate. Discussing a patient’s quality of life was appreciated by relatives, and according to relatives healthcare professionals also found this valuable. In some cases the preferred and actual involvement was in discordance, preferring either a larger or a smaller role.ConclusionsAs treatment alignment with a patient’s values and preferences and accordance between preferred and actual involvement in decision-making is very important, we suggest that the treatment decision-making process should start with discussions about a patient’s quality of life, followed by tailoring the process to relatives’ preferences as much as possible. Healthcare professionals should be aware of relatives’ heterogeneous and possibly changing preferences regarding the decision-making process.

Comorbid Dementia and Cancer Therapy Decision-Making: A Scoping Review.

Comorbid dementia complicates cancer therapy decision-making in older adults. We aimed to synthesize the recent literature (<5years) on the challenges associated with cancer therapy decision-making among older people living with dementia (PLWD) and their caregivers. Of the 20,763 references, 8767 had their title and abstract screened, and eight met the inclusion criteria. Six studies were qualitative, one study employed mixed methods, and one study was quasi-experimental. Most studies were conducted in the UK (89%) and reported homogeneity in race and geography. Breast (56%) and prostate (45%) were the most frequent reported cancers. Five studies (56%) reported multiple types of dementia, with two (22%) indicating stages. The studies indicated that communication between patients, caregivers, and clinical teams might alleviate stress caused by worsening health prospects and potential ethical concerns. Information from this review can lead to better-informed, patient-centered treatment decision processes among older PLWD and cancer, their caregivers, and clinicians.

Assessment of AI Models in Predicting Treatment Outcomes in Orthodontics

ABSTRACT Background: In the realm of orthodontics, the evaluation of treatment outcomes is a pivotal aspect. In recent times, artificial intelligence (AI) models have garnered attention as potential tools for predicting these outcomes. These AI models have the potential to enhance treatment planning and decision-making processes. However, a comprehensive assessment of their effectiveness and accuracy is essential before their widespread integration. Materials and Methods: In this study, we assessed the capability of AI models to predict treatment outcomes in orthodontics. A sample of 30 patients undergoing orthodontic treatment was selected. Various patient-specific parameters, including age, initial malocclusion severity, and treatment approach, were collected. The AI model was trained using a dataset comprising historical treatment cases and their respective outcomes. Subsequently, the trained AI model was applied to predict the treatment outcomes for the selected patients. Results: The results of this study indicated a moderate level of accuracy in the predictions made by the AI model. Out of the 30 patients, the model accurately predicted treatment outcomes for 22 patients, yielding a success rate of approximately 73%. However, the model exhibited limitations in accurately predicting outcomes for cases involving complex malocclusions or those requiring non-standard treatment approaches. Conclusion: In conclusion, this study underscores the potential of AI models in predicting treatment outcomes in orthodontics. While the AI model demonstrated promising accuracy in the majority of cases, its efficacy was diminished in complex and non-standard cases. Therefore, while AI models can serve as valuable tools to aid orthodontists in treatment planning, they should be utilized in conjunction with clinical expertise to ensure optimal decision-making and patient care.

A video-based decision aid improved knowledge and decreased decisional conflict for asymptomatic persistent endodontic lesions.

This prospective cohort study aimed to (i) evaluate patients' treatment decisions, decisional conflict and knowledge levels before and after use of a video-based decision aid (DA) on asymptomatic persistent endodontic lesions (APEL) and to (ii) elicit important decision factors for patients in the decision-making process. A video DA modelled after the International Patient Decision Aids Standards (IPDAS) was designed. The video was pilot-tested, revised and used on 50 patients referred to the graduate endodontic clinic and presenting with an incidental finding of APEL detected on radiographs, with informed consent (DSRB 2020/00133). Recruitment was conducted from June 2020 to November 2021. Patients' treatment decisions and decision-making process were studied. Patients also completed a questionnaire on knowledge gain, decisional conflict and confidence levels. Forty-three patients (48 teeth) out of 50 patients chose long-term monitoring of APEL while 7 patients (7 teeth) decided on endodontic intervention. Median knowledge scores increased from 58.3 (IQR: 41.7, 75.0) to 87.5 (IQR: 75.0, 100.0) (p < .001); median decisional conflict scale (DCS) scores decreased from 25.0 (IQR: 12.5, 34.4) to 3.1 (IQR: 0, 23.8) (p < .001), and 44 (88%) patients were confident or very confident with their treatment decisions. Statistical analyses showed that asymptomatic condition of tooth was the sole decision factor with statistical significance while socioeconomic factors (age, gender, race, education and income levels) showed no significant trend. The majority of patients presenting with APEL in this study chose long-term monitoring over immediate intervention. Using the video-based DA in the decision-making process significantly improved patients' knowledge and decreased their decisional conflict levels.

Enhancing Patient Selection in Stage IIIA-IIIB NSCLC: Invasive Lymph Node Restaging after Neoadjuvant Therapy.

Restaging of mediastinal lymph nodes plays a crucial role in the multimodal treatment of stage IIIA Non-Small-Cell Lung Cancer (NSCLC). This study aimed to assess the impact of restaging using endobronchial ultrasound (EBUS), endoesophageal ultrasound (EUS), and transcervical extended mediastinal lymphadenectomy (TEMLA) after neoadjuvant chemotherapy (CHT) or chemoradiotherapy (CRT) on the 5-year overall survival (OS) of patients with NSCLC diagnosed with clinical stage IIIA-IIIB and metastatic ipsilateral mediastinal nodes (N2) who underwent radical pulmonary resections. Patients diagnosed with stage IIIA-IIIB NSCLC and N2 mediastinal nodes were included in this study. Restaging of mediastinal lymph nodes was performed using EBUS, EUS, and TEMLA. The patients were divided into two groups based on the restaging method: the TEMLA restaging group and the chest CT scan-only group. The primary outcome measure was the 5-year OS rate, and the secondary outcome measures included median OS and survival percentages. Statistical analysis, including the log-rank test, was conducted to assess the differences between the two groups. The TEMLA restaging group demonstrated significantly better overall survival compared to the chest CT scan-only group (log-rank test, p = 0.02). This was evident through a four-fold increase in median OS (59 vs. 14 months) and a higher 5-year OS rate of 55.9% (95% CI: 40.6-71.1) compared to 25.0% (95% CI: 13.7-36.3) in the chest CT scan-only group (p = 0.003). Invasive restaging of mediastinal lymph nodes improves the selection of patients with stage IIIA-IIIB (N2) NSCLC after neoadjuvant therapy. The use of EBUS, EUS, and TEMLA provides valuable information for identifying patients who may benefit from surgery by identifying N2 to N0-1 downstaging. These findings emphasize the importance of incorporating restaging procedures into the treatment decision-making process for NSCLC patients with mediastinal lymph node involvement.

Proton Therapy in Breast Cancer: A Review of Potential Approaches for Patient Selection.

Proton radiotherapy may be a compelling technical option for the treatment of breast cancer due to its unique physical property known as the "Bragg peak." This feature offers distinct advantages, promising superior dose conformity within the tumor area and reduced radiation exposure to surrounding healthy tissues, enhancing the potential for better treatment outcomes. However, proton therapy is accompanied by inherent challenges, primarily higher costs and limited accessibility when compared to well-developed photon irradiation. Thus, in clinical practice, it is important for radiation oncologists to carefully select patients before recommendation of proton therapy to ensure the transformation of dosimetric benefits into tangible clinical benefits. Yet, the optimal indications for proton therapy in breast cancer patients remain uncertain. While there is no widely recognized methodology for patient selection, numerous attempts have been made in this direction. In this review, we intended to present an inspiring summarization and discussion about the current practices and exploration on the approaches of this treatment decision-making process in terms of treatment-related side-effects, tumor control, and cost-efficiency, including the normal tissue complication probability (NTCP) model, the tumor control probability (TCP) model, genomic biomarkers, cost-effectiveness analyses (CEAs), and so on. Additionally, we conducted an evaluation of the eligibility criteria in ongoing randomized controlled trials and analyzed their reference value in patient selection. We evaluated the pros and cons of various potential patient selection approaches and proposed possible directions for further optimization and exploration. In summary, while proton therapy holds significant promise in breast cancer treatment, its integration into clinical practice calls for a thoughtful, evidence-driven strategy. By continuously refining the patient selection criteria, we can harness the full potential of proton radiotherapy while ensuring maximum benefit for breast cancer patients.

The development of a web-based patient decision aid for type 2 diabetes to support patients and their healthcare professionals in the treatment decision-making process.

Introduction: Patient decision aids (PDAs) are useful tools in facilitating shared decision-making (SDM) between patients and healthcare professionals. The International Patient Decision Aids Standards (IPDAS) Collaboration developed a model for the systematic development of PDAs. However, there is still some uncertainty about which approach is most suitable for each step of the development process and questions remain on how to best involve patients and healthcare professionals in the development process. We aim to provide insights into how the IPDAS model for the development of a PDA for type 2 diabetes could be used.&#x0D; Target audience: Our developed PDA is for individuals with type 2 diabetes, who need to decide on a new or additional glucose-lowering treatment (lifestyle and/or medical treatment), and their healthcare professionals in primary care.&#x0D; Methods: The PDA was systematically developed in the Netherlands, following five steps: 1) define scope; 2) establishment of expert group; 3) design of PDA (determine, among others, the preferences of patients and healthcare professionals); 4) draft prototype; and 5) iterative process of review and redraft. The preferences of patients and healthcare professionals, in combination with input from the expert group and the available evidence of the treatment options for type 2 diabetes, were used to determine the content of the PDA and develop a prototype. Content was written by a scientific writer. The prototype of the PDA was reviewed by the research team and expert group.&#x0D; Patient and public involvement: A multidisciplinary expert group was established at the beginning of the study. The expert group consisted of patients with type 2 diabetes (N=2), a representative from the Dutch Diabetes Association, a representative from the Netherlands Diabetes Federation and healthcare professionals representing the broad range of disciplines involved in type 2 diabetes care (i.e., general practitioner, diabetes nurse, practice nurse, pharmacist, endocrinologist and dietician). The expert group met twice a year (online) and advised on the development process and content of the PDA. The expert group meetings were interactive and small-group discussions were held using statements about different aspects of the PDA, such as the format of the PDA.&#x0D; Results: Individuals with type 2 diabetes wanted to compare multiple treatment options for both their clinical aspects and their impact on daily life. Following the systematic development process, a web-based PDA was developed consisting of five sections: 1) information about type 2 diabetes and the different treatment options; 2) comparison of treatment options based on, for example, clinical outcomes, effect on weight and risk of cardiovascular diseases; 3) questions to test patients’ knowledge; 4) value-clarification exercise; and 5) summary of the patient's answers and notes. &#x0D; Conclusions: We provided insights into how you could use the IPDAS systematic development process for the development of a PDA. Involving patients and healthcare professionals throughout the development process is valuable for the development of the PDA for type 2 diabetes. They were involved in an early and iterative way, which ensured that the PDA is compatible with the needs and preferences of all relevant stakeholders in Dutch diabetes care.