High-dose chemotherapy followed by autologous stem cell transplantation in pediatric patients with relapsed osteosarcoma.

BACKGROUND: Patients with relapsed osteosarcoma have poor treatment outcomes. High-dose chemotherapy with autologous stem cell transplantation (HDCT/ASCT) has been used in several high-risk malignant solid tumors; however, few studies have evaluated their role in treating osteosarcoma. We evaluated the effectiveness of HDCT/ASCT in relapsed pediatric osteosarcoma cases. PROCEDURE: We retrospectively reviewed the medical records of 40 patients diagnosed with and treated for relapsed osteosarcoma at Asan Medical Center and Samsung Medical Center from January 1996 to July 2019. RESULTS: The median age of this cohort was 13.4 years (range: 6.1-18.2). The cohort's 5-year overall survival (OS) was 51.0% ± 0.1% during a median follow-up period of 67.5 months. Twenty-five patients (62.5%) achieved complete remission (CR) with salvage treatment, and the 5-year OS was 82.4% ± 0.1%, whereas none of the remaining 15 patients who did not achieve CR survived (p < .0001). Of the 25 CR cases, 15 underwent subsequent HDCT/ASCT. We compared the effect of HDCT/ASCT among patients who achieved CR. There were no significant differences in the 5-year OS outcomes between patients who did and did not receive HDCT/ASCT (83.9% ± 0.1%, 13/15 vs. 80.0% ± 0.1%, 8/10, respectively; p = .923). CONCLUSION: To our knowledge, we report the first comparative cohort study that proved HDCT/ASCT does not significantly improve survival outcomes in relapsed osteosarcoma. Achievement of CR remains the most crucial factor for good survival outcomes.

Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update.

BACKGROUND: The recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment. METHODS: The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center. RESULTS: Simulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 data instances. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes is associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation. CONCLUSIONS: Our C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple log-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.

Soft tissue sarcoma: DWI and DCE-MRI parameters correlate with Ki-67 labeling index.

OBJECTIVES: To examine the correlation of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging (MRI) parameters with Ki-67 labeling index (LI) in soft tissue sarcoma (STS). METHODS: The institutional review board approved this retrospective study, and the requirement for informed consent was waived. Thirty-six patients with STS who underwent 3.0-T MRI, including diffusion-weighted and dynamic contrast-enhanced MRI, between July 2011 and February 2018, were included in this study. The mean and minimum apparent diffusion coefficients (ADCs) (ADCmean and ADCmin, respectively), volume transfer constant, reflux rate, and volume fraction of the extravascular extracellular matrix of each lesion were independently analyzed by two readers. Their relationship with the Ki-67 LI was examined using Spearman's correlation analyses. Differences between low- and high-proliferation groups based on Ki-67 LI were evaluated statistically. Optimal cut-off points were determined using the area under the curve analysis for significant parameters. Interobserver agreement was assessed with the intraclass correlation coefficient. RESULTS: ADCmean (ρ = - 0.333, p = 0.047) was significantly and inversely correlated with Ki-67 LI. The high-proliferation group showed a significantly lower ADCmean than did the low-proliferation group (median, 1.08 vs. 1.20; p = 0.048). When a cut-off ADCmean value of 1.16 × 10-3 mm2/s was used, the sensitivity, specificity, and area under the curve for differentiating low- and high-proliferation groups were 75.0%, 60.0%, and 0.712, respectively. Interobserver agreements between the two readers were almost perfect for all parameters. CONCLUSIONS: ADCmean was correlated with Ki-67 LI and could help differentiate between STS with low and high proliferation potential. KEY POINTS: • ADC meanwas significantly and inversely correlated with Ki-67 labeling index in soft tissue sarcoma. • In the high-proliferation group, ADC meanvalues were significantly lower than those of the low-proliferation group.

Evaluation of conditional treatment effects of adjuvant treatments on patients with synovial sarcoma using Bayesian subgroup analysis.

BACKGROUND: The impact of adjuvant chemotherapy or radiation therapy on the survival of patients with synovial sarcoma (SS), which is a rare soft-tissue sarcoma, remains controversial. Bayesian statistical approaches and propensity score matching can be employed to infer treatment effects using observational data. Thus, this study aimed to identify the individual treatment effects of adjuvant therapies on the overall survival of SS patients and recognize subgroups of patients who can benefit from specific treatments using Bayesian subgroup analyses. METHODS: We analyzed data from patients with SS obtained from the surveillance, epidemiology, and end results (SEER) public database. These data were collected between 1984 and 2014. The treatment effects of chemotherapy and radiation therapy on overall survival were evaluated using propensity score matching. Subgroups that could benefit from radiation therapy or chemotherapy were identified using Bayesian subgroup analyses. RESULTS: Based on a stratified Kaplan-Meier curve, chemotherapy exhibited a positive average causal effect on survival in patients with SS, whereas radiation therapy did not. The optimal subgroup for chemotherapy includes the following covariates: older than 20 years, male, large tumor (longest diameter > 5 cm), advanced stage (SEER 3), extremity location, and spindle cell type. The optimal subgroup for radiation therapy includes the following covariates: older than 20 years, male, large tumor (longest diameter > 5 cm), early stage (SEER 1), extremity location, and biphasic type. CONCLUSION: In this study, we identified high-risk patients whose variables include age (age > 20 years), gender, tumor size, tumor location, and poor prognosis without adjuvant treatment. Radiation therapy should be considered in the early stages for high-risk patients with biphasic types. Conversely, chemotherapy should be considered for late-stage high-risk SS patients with spindle cell types.

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks.

BACKGROUND: We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML) algorithms to predict survival following a spino-pelvic chondrosarcoma diagnosis. METHODS: The SEER 18 registries were used to apply the Risk Estimate Distance Survival Neural Network (RED_SNN) in the model. Our model was evaluated at each time window with receiver operating characteristic curves and areas under the curves (AUCs), as was the concordance index (c-index). RESULTS: The subjects (n = 1088) were separated into training (80%, n = 870) and test sets (20%, n = 218). The training data were randomly sorted into training and validation sets using 5-fold cross validation. The median c-index of the five validation sets was 0.84 (95% confidence interval 0.79-0.87). The median AUC of the five validation subsets was 0.84. This model was evaluated with the previously separated test set. The c-index was 0.82 and the mean AUC of the 30 different time windows was 0.85 (standard deviation 0.02). According to the estimated survival probability (by 62 months), we divided the test group into five subgroups. The survival curves of the subgroups showed statistically significant separation (p < 0.001). CONCLUSIONS: This study is the first to analyze population-level data using artificial neural network ML algorithms for the role and outcomes of surgical resection and radiation therapy in spino-pelvic chondrosarcoma.

Artificial Intelligence in Health Care: Current Applications and Issues.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

Prediction of Overall Survival and Novel Classification of Patients with Gastric Cancer Using the Survival Recurrent Network.

BACKGROUND: Artificial neural networks (ANNs) have been applied to many prediction and classification problems, and could also be used to develop a prediction model of survival outcomes for cancer patients. OBJECTIVE: The aim of this study is to develop a prediction model of survival outcomes for patients with gastric cancer using an ANN. METHODS: This study enrolled 1243 patients with stage IIA-IV gastric cancer who underwent D2 gastrectomy from January 2007 to June 2010. We used a recurrent neural network (RNN) to make the survival recurrent network (SRN), and patients were randomly sorted into a training set (80%) and a test set (20%). Fivefold cross-validation was performed with the training set, and the optimized model was evaluated with the test set. Receiver operating characteristic (ROC) curves and area under the curves (AUCs) were evaluated, and we compared the survival curves of the American Joint Committee on Cancer (AJCC) 8th stage groups with those of the groups classified by the SRN-predicted survival probability. RESULTS: The test data showed that the ROC AUC of the SRN was 0.81 at the fifth year. The SRN-predicted survival corresponded closely with the actual survival in the calibration curve, and the survival outcome could be more discriminately classified by using the SRN than by using the AJCC staging system. CONCLUSION: SRN was a more powerful tool for predicting the survival rates of gastric cancer patients than conventional TNM staging, and may also provide a more flexible and expandable method when compared with fixed prediction models such as nomograms.

Deep learning approach for survival prediction for patients with synovial sarcoma.

Synovial sarcoma is a rare disease with diverse progression characteristics. We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. The purpose of this study is to evaluate the performance of the proposed prediction model and demonstrate its clinical usage. The study involved 242 patients who were diagnosed with synovial sarcoma in three institutions between March 2001 and February 2013. The patients were randomly divided into a training set (80%) and a testing set (20%). Fivefold cross validation was performed utilizing the training set. The test set was retained for the final testing. A Cox proportional hazard model, simple neural network, and the proposed survival neural network were all trained utilizing the same training set, and fivefold cross validation was performed. The final testing was performed utilizing the isolated test data to determine the best prediction model. The multivariate Cox proportional hazard regression analysis revealed that size, initial metastasis, and margin were independent prognostic factors. In fivefold cross validation, the median value of the receiver-operating characteristic curve (area under the curve) was 0.87 in the survival neural network, which is significantly higher compared to the area under the curve of 0.792 for the simple neural network (p = 0.043). In the final test, survival neural network model showed the better performance (area under the curve: 0.814) compared to the Cox proportional hazard model (area under the curve: 0.629; p = 0.0001). The survival neural network model predicted survival of synovial sarcoma patients more accurately compared to Cox proportional hazard model.

TP53-dependence on the effect of doxorubicin and Src inhibitor combination therapy.

The anticancer effects of Src kinase inhibitors are controversial. This study found an association between alterations in the TP53 gene and the synergy score for combination treatment with doxorubicin and an Src kinase inhibitor using human osteosarcoma cell lines (MG63 and U2OS) and human colon cancer cell line. Doxorubicin was found to activate signal transducer and activator of transcription 3 via Src kinase in cancer cells harboring alterations in TP53. A drug combination study using patient-derived cells confirmed that an Src kinase inhibitor synergizes with doxorubicin in cancer cells harboring alterations in TP53, while antagonizing its effect in cancer cells expressing wild-type TP53. Our findings suggest that genetic alterations in TP53 are a critical factor in determining the use of a combination treatment of doxorubicin and Src inhibitors.

What Factors Are Associated With Early Mortality in Patients Undergoing Femur Surgery for Metastatic Lung Cancer?

BACKGROUND: Pathologic fractures of the femur resulting from metastasis severely increase mortality in patients with nonsmall cell lung cancer (NSCLC). However, factors associated with early mortality after surgery have not been elucidated. QUESTIONS/PURPOSES: The purpose of this study was to identify clinical and laboratory factors available to surgeons before surgery for a metastatic femur in patients with metastatic lung cancer that might be associated with mortality at 1 and 3 months. METHODS: Between 2010 and 2014 we treated 126 patients for pathologic fracture of the femur caused by NSCLC. Of those, complete data sets for the parameters of interest (including clinical factors, laboratory factors, and survivorship) were available in 105 (83%). The factors we considered included sex, age, fracture location, surgical procedure, postoperative complications, blood cell counts, serum biomarkers, genetic alterations of primary cancer, chemotherapeutic agents, preoperative radiation therapy, pleural effusion, bone and internal organ metastasis, performance scores, and medical center where the treatment was performed. Multivariate logistic regression was performed to identify factors associated with mortality at 1 and 3 months. RESULTS: Intertrochanteric location was associated with a higher risk of death (odds ratio [OR], 17.0; 95% confidence interval [CI], 2.65-109.5), lower serum albumin level was associated with an increased risk of death (OR, 0.13; 95% CI, 0.028-0.60), and availability of a suitable chemotherapeutic target agent was associated with a lower risk of death (OR, 0.28; 95% CI, 0.08-0.91) within 3 months of surgery. Undergoing reconstruction with an endoprosthesis was associated with a higher risk of death (OR, 48.3; 95% CI, 1.7-1329) and elevated serum leukocyte count (OR, 1.2; 95% CI, 1.0-1.4) and elevated alanine aminotransferase (ALT) were associated with a higher risk of death (OR, 1.1; 95% CI, 1.0-1.2) within 1 month of surgery. CONCLUSIONS: Although the risk factors for early mortality need to be validated by prospective studies, surgical options need to be reconsidered in patients with femoral metastases from NSCLS showing high ALT or leukocytosis on the preoperative blood test. LEVEL OF EVIDENCE: Level III, prognostic study.

TP53 alteration determines the combinational cytotoxic effect of doxorubicin and an antioxidant NAC.

The anticancer effect of doxorubicin is closely related to the generation of reactive oxygen species. On the contrary, doxorubicin-induced reactive oxygen species induces heart failure, a critical side effect of doxorubicin. Antioxidant supplementation has been proposed to reduce the side effects. However, the use of antioxidants may hamper the anticancer effect of doxorubicin. In this study, doxorubicin-induced reactive oxygen species was shown to differentially affect cancer cells based on their TP53 genetic status; doxorubicin-induced apoptosis was attenuated by an antioxidant, N-acetylcysteine, in TP53 wild cells; however, N-acetylcysteine caused a synergistic increase in the apoptosis rate in TP53-altered cells. N-acetylcysteine prevented phosphorylation of P53 protein that had been induced by doxorubicin. However, N-acetylcysteine increased the cleavage of poly (ADP-ribose) polymerase in the presence of doxorubicin. Synergy score of 26 patient-derived cells were evaluated after the combination treatment of doxorubicin and N-acetylcysteine. The synergy score was significantly higher in TP53-altered group compared with those in TP53 wild group. In conclusion, TP53 genetic alteration is a critical factor that determines the use of antioxidant supplements during doxorubicin treatment.

Oxidative stress induced by low-dose doxorubicin promotes the invasiveness of osteosarcoma cell line U2OS in vitro.

Reactive oxygen species (ROS) are known to mediate doxorubicin (DOX)-induced apoptosis and are the major cause of DOX toxicity. We introduce a novel in vitro phenomenon of osteosarcoma (OS) cell line caused by low-dose DOX-induced oxidative stress. Human osteosarcoma cell line U2OS was used for the experiments. Hydrogen peroxide (H2O2) and the antioxidant compound N-acetylcysteine (NAC) were used to investigate the involvement of oxidative stress. In proliferation assays, low dose of DOX (below 200 nM) did not affect U2OS proliferation significantly for up to 48 h. In Matrigel(TM) invasion assay, DOX increased the invasiveness of U2OS at around 100 nM, which is a subclinical concentration. Quantitative real-time polymerase chain reaction and gelatin zymography showed increased MMP-9 expression and increased MMP-9 enzymatic activity, respectively, in the presence of DOX doses that increased the invasiveness of U2OS. H2O2, a representative source of ROS, also increased the invasiveness of U2OS as DOX did, with similar patterns. However, when the cells were pre-treated with NAC, no DOX- or H2O2-mediated increase of invasiveness or MMP-9 expression was evident. The results suggest that oxidative stress induced by low-dose DOX promotes the invasiveness of osteosarcoma cell line U2OS in vitro, through MMP-9 induction.

RRP12 is a crucial nucleolar protein that regulates p53 activity in osteosarcoma cells.

RRP12 (ribosomal RNA processing 12 homolog), a nucleolar protein, plays important roles in cell cycle progression and the response to deoxyribonucleic acid (DNA) damage in yeast cells. However, its role has not been investigated in mammalian cells that possess p53, which has close functional association to nucleolus. We explored the role of RRP12 in nucleolar stress condition using an osteosarcoma cell line, U2OS. To induce DNA damage and nucleolar disruption, two cytotoxic drugs, doxorubicin and actinomycin D were used. Cytotoxic stress resulted nucleolar disruption induced cell cycle arrest and apoptosis in U2OS cells. However, RRP12 overexpression promoted resistance to cytotoxic stress. In contrast, RRP12 silencing enhanced susceptibility to cytotoxic stress. During drug treatment, p53 activity and cell death were suppressed by RRP12 overexpression but promoted by RRP12 silencing. This study demonstrated that RRP12 was crucial for cell survival during cytotoxic stress via the repression of p53 stability. Thus, targeting RRP12 may enhance chemotherapeutic effect in cancers.

Prognostic value of 18F-FDG PET/CT in patients with soft tissue sarcoma: comparisons between metabolic parameters.

OBJECTIVES: To investigate the relationship between volume-based PET parameters and prognosis in patients with soft tissue sarcoma (STS). METHODS: We retrospectively reviewed 55 patients with pathologically proven STS who underwent pretreatment with (18) F-Fluorodeoxyglucose ((18)F-FDG) PET/CT. The maximum standardized uptake value (SUVmax), average SUV (SUVavg), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of primary tumors were measured using a threshold SUV as liver activity for determining the boundary of tumors. Univariate and multivariate survival analyses for overall survival were performed according to the metabolic parameters and other clinical variables. RESULTS: Cancer-related death occurred in 19 of 55 patients (35 %) during the follow-up period (29 ± 23 months). On univariate analysis, AJCC stage (stage IV vs. I-III, hazard ratio (HR) = 2.837, p = 0.028), necrosis (G2 vs. G0-G1, HR = 3.890, p = 0.004), SUVmax (1 unit - increase, HR = 1.146, p = 0.008), SUVavg (1 unit - increase, HR = 1.469, p = 0.032) and treatment modality (non-surgical therapy vs. surgery, HR = 4.467, p = 0.002) were significant predictors for overall survival. On multivariate analyses, SUVmax (HR = 1.274, p = 0.015), treatment modality (HR = 3.353, p = 0.019) and necrosis (HR = 5.985, p = 0.006) were identified as significant independent prognostic factors associated with decreased overall survival. CONCLUSIONS: The SUVmax of the primary tumor is a significant independent metabolic prognostic factor for overall survival in patients with STS. Volume-based PET parameters may not add prognostic information outside of the SUVmax.

Microsurgical pedicle lengthening for pedicled thoracodorsal artery perforator flap transfer.

The pedicled thoracodorsal artery perforator flap has been shown to be an effective option for reconstruction of various regions, including the breast, axilla, upper arm, lateral chest, and shoulder area. However, the original length of the pedicle limits the flap's reach to remote locations. We devised a technique that involves microsurgical lengthening of the pedicle to extend the arc of rotation of the pedicled perforator flap. After exposure of the subscapular vascular tree, we divided the thoracodorsal vessel at the point of bifurcation to the serratus branch and then the pedicle was reconnected to the distal end of the serratus branch. Here, we present 2 cases in which this technique was effectively applied to reconstruct a defect of the elbow using a thoracodorsal artery perforator flap with a pedicle lengthening procedure.

Massive intercalary reconstruction of lower limb after wide excision of malignant tumors: an alternative to amputation or rotationplasty.

In this study, we present the feasibility of intercalary limb resection and massive reconstruction for malignant tumors of lower extremity. Ten cases of lower extremity malignancies that had undergone concomitant bone (and/or joint) and soft-tissue reconstruction after wide excision exceeding two-thirds of the cross-sectional area of the affected limb were reviewed. All cases were indicated for amputation because of an expansive tumor, hematoma from a pathologic fracture, or previous unplanned excision, with or without critical structure involvement. Bone was reconstructed with either an allograft or a tumor prosthesis. Soft-tissue reconstruction was performed to achieve critical structure and coverage, which was required in all cases. The resection margin was clear in all cases, and no soft-tissue graft failure was encountered. During a mean follow-up of 26 months (range, 9-42 months), no patient developed local recurrence in the resection-reconstruction site. Of the 10 patients, 8 patients were able to walk independently, and two were ambulatory but needed crutch support outdoors. Massive intercalary resection and reconstruction can be an effective treatment option for locally progressed or complicated lower extremity malignancies. Considering patient preference and the fair functional outcomes observed, it may be a useful alternative to amputation or rotationplasty.

Novel MFSD7-ATP5I fusion promotes migration and invasion of human sarcoma.

Fusion genes have been implicated in the development and progression of several types of sarcomas, serving as valuable diagnostic and prognostic markers, as well as potential therapeutic targets. We discovered a novel major facilitator superfamily domain-containing 7 (MFSD7) and adenosine triphosphate 5I (ATP5I) gene fusion from sarcomas. In this study, the MFSD7-ATP5I fusion transcript was screened using RNA sequencing in 55 sarcoma samples and sixteen normal samples. The MFSD7-ATP5I fusion transcript was detected in 58% of sarcoma samples. The correlation between the expression of MFSD7-ATP5I fusion transcript and clinicopathological information was analyzed, and MFSD7-ATP5I expression is associated with marked pleomorphism and lower tumor necrosis. Cell migration and invasion was significantly reduced by knock-down of MFSD7-ATP5I. Cell migration and invasion was increased by overexpression of MFSD7-ATP5I. A phosphokinase assay demonstrated that MFSD7-ATP5I is involved in the GSK-3 pathway. The current study found that MFSD7-ATP5I is associated with increasing pleomorphism and decreasing necrosis of tumors. And our gain and loss of function experiments prove that MFSD7-ATP5I promotes the invasiveness of tumor cells.

Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools.

PURPOSE: Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea. METHOD: An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online questionnaire survey was carried out with 1,955 participants (1,174 students and 781 professors) from medical schools across South Korea, utilizing the list of 42 competencies developed from the first Delphi round. A subsequent Delphi survey was conducted with 33 medical educators from 21 medical schools to differentiate the essential AI competencies from the optional ones. RESULTS: The study identified 6 domains encompassing 36 AI competencies essential for medical graduates: (1) understanding digital health and changes driven by AI; (2) fundamental knowledge and skills in medical AI; (3) ethics and legal aspects in the use of medical AI; (4) medical AI application in clinical practice; (5) processing, analyzing, and evaluating medical data; and (6) research and development of medical AI, as well as subcompetencies within each domain. While numerous competencies within the first 4 domains were deemed essential, a higher percentage of experts indicated responses in the last 2 domains, data science and medical AI research and development, were optional. CONCLUSIONS: This medical AI framework of 6 competencies and their subcompetencies for medical graduates exhibits promising potential for guiding the integration of AI into medical curricula. Further studies conducted in diverse contexts and countries are necessary to validate and confirm the applicability of these findings. Additional research is imperative for developing specific and feasible educational models to integrate these proposed competencies into pre-existing curricula.