Relaxed Adaptive Lasso for Classification on High-Dimensional Sparse Data with Multicollinearity

High-dimensional sparse data with multicollinearity is frequently found in medical data. This problem can lead to poor predictive accuracy when applied to a new data set. The Least Absolute Shrinkage and Selection Operator (Lasso) is a popular machine-learning algorithm for variable selection and parameter estimation. Additionally, the adaptive Lasso method was developed using the adaptive weight on the l1-norm penalty. This adaptive weight is related to the power order of the estimators. Thus, we focus on 1) the power of adaptive weight on the penalty function, and 2) the two-stage variable selection method. This study aimed to propose the relaxed adaptive Lasso sparse logistic regression. Moreover, we compared the performances of the different penalty functions by using the mean of the predicted mean squared error (MPMSE) for the simulation study and the accuracy of classification for a real-data application. The results showed that the proposed method performed best on high-dimensional sparse data with multicollinearity. Along with, for classifier with the support vector machine, this proposed method was also the best option for the variable selection process

Adaptive Elastic Net on High-Dimensional Sparse Data with Multicollinearity: Application to Lipomatous Tumor Classification

Predictive models can experience instabilities because of the combination of high-dimensional sparse data and multicollinearity problems. The adaptive Least Absolute Shrinkage and Selection Operator (adaptive Lasso) and adaptive elastic net were developed using the adaptive weight on penalty term. These adaptive weights are related to the power order of the estimators. Therefore, we concentrate on the power of adaptive weight on these penalty functions. This study purposed to compare the performances of the power of the adaptive Lasso and adaptive elastic net methods under high-dimensional sparse data with multicollinearity. Moreover, we compared the performances of the ridge, Lasso, elastic net, adaptive Lasso, and adaptive elastic net in terms of the mean of the predicted mean squared error (MPMSE) for the simulation study and the classification accuracy for a real-data application. The results of the simulation and the real-data application showed that the square root of the adaptive elastic net performed best on high-dimensional sparse data with multicollinearity

The Management of Sacral Schwannoma: Report of Four Cases and Review of Literature

Sacral schwannoma is a rare retrorectal tumor in adults. Postoperative sacral neurological deficit is difficult to avoid. Currently, there is no established consensus regarding best treatment options. We present the management and outcomes of sacral schwannoma in 4 patients treated with intralesional curettage and postoperative radiation. There were 3 women and one man (average age: 45.5 years) with long duration of lumbosacral pain with or without radiculopathy. Intralesional curettage was performed by posterior approach and adjuvant radiation therapy with dosage of 5000–6600 cGy was given after surgery. The mean follow-up time was 18 months (range 4–23 months). Symptoms of radiculopathy had decreased in all patients. The recent radiographic findings show evidence of sclerosis at the sacrum one year postoperatively, but the size was unchanged. Intralesional curettage and adjuvant radiation therapy can be used in the treatment of sacral schwannoma to relieve symptoms and preserve neurological function

MAPK/ERK Signaling in Osteosarcomas, Ewing Sarcomas and Chondrosarcomas: Therapeutic Implications and Future Directions

The introduction of cytotoxic chemotherapeutic drugs in the 1970's improved the survival rate of patients with bone sarcomas and allowed limb salvage surgeries. However, since the turn of the century, survival data has plateaued for a subset of metastatic, nonresponding osteo, and/or Ewing sarcomas. In addition, most high-grade chondrosarcoma does not respond to current chemotherapy. With an increased understanding of molecular pathways governing oncogenesis, modern targeted therapy regimens may enhance the efficacy of current therapeutic modalities. Mitogen-Activated Protein Kinases (MAPK)/Extracellular-Signal-Regulated Kinases (ERK) are key regulators of oncogenic phenotypes such as proliferation, invasion, angiogenesis, and inflammatory responses; which are the hallmarks of cancer. Consequently, MAPK/ERK inhibitors have emerged as promising therapeutic targets for certain types of cancers, but there have been sparse reports in bone sarcomas. Scattered papers suggest that MAPK targeting inhibits proliferation, local invasiveness, metastasis, and drug resistance in bone sarcomas. A recent clinical trial showed some clinical benefits in patients with unresectable or metastatic osteosarcomas following MAPK/ERK targeting therapy. Despite in vitro proof of therapeutic concept, there are no sufficient in vivo or clinical data available for Ewing sarcomas or chondrosarcomas. Further experimental and clinical trials are awaited in order to bring MAPK targeting into a clinical arena

Outcomes and Prognostic Factors in Patients with Malignant Peripheral Nerve Sheath Tumor

Objective: To investigate and report the clinical profiles, treatment patterns, and oncologic outcomes in MPNST patients, and to identify the prognostic factors that significantly affect survival. Materials and Methods: Patients diagnosed with and treated for histologically confirmed MPNST at our institute during the January 1997 to June 2018 study period were included. Patient medical records and surgical specimens were reviewed, and study-related data was extracted and analyzed. Results: There were 27 males and 32 females with a mean age of 44 years. Most patients presented with mass and most patients were AJCC stage III. Twenty-nine percent of patients had MPNST that was associated with NF-1. At a median follow-up time, 18 patients (30.51%) suffered from local disease recurrence. Two-year and 5-year overall survival was 72% and 46%, respectively. In univariate analysis, chemotherapy treatment and positive tumor margin were adverse prognostic factors for disease-free survival. In multivariate analysis, chemotherapy treatment (hazard ratio (HR): 3.415, 95% CI: 1.367-16.021; p=0.013) and positive tumor margin (HR: 4.680, 95% CI 1.828-10.314; p=0.014) were found to be independent prognostic factors for disease-free. Conclusion: Chemotherapy treatment and positive tumor margin were identified as independent adverse prognostic factors for disease-free and overall survival, respectively. Accordingly, early detection and appropriate treatment are essential for improved patient outcome

Outcomes of Proximal Femoral Locking-plate Fixation for Pathological Fractures of the Proximal Femur

Objective: To study the treatment outcomes of proximal femoral locking-plate fixation of pathological fractures of the proximal femur relative to clinical results, implant failure, and surgical complications. Materials and Methods: From 2007 to 2018, 17 patients (18 femurs) with a diagnosis of impending or existing pathological fracture of the proximal femur were treated with proximal femoral locking-plate fixation. Data collected included operative duration, estimated blood loss, ambulatory status, hardware failure events, and postoperative complications. Results: Of the 18 femurs that were included, 13 were existing pathological fractures and 5 were impending fractures. The mean age of patients was 53.7 years (range: 28-89), and 12 of them were female. The mean follow-up was 11.3 months (range: 1-67). Ten of 17 patients (62.5%) had progressive lung disease from pulmonary metastasis or from lung primary. No patient developed oxygen desaturation or cardiac arrest during the intraoperative or postoperative period. Thirteen of 17 patients (76.5%) could walk with or without an assistive device at the time of final follow-up. Two patients required close postoperative monitoring in the intensive care unit due to poor preoperative status, and both of those patients died within one month after surgery from other medical problems. No hardware failure occurred. Conclusion: For pathological fracture of the proximal femur, proximal femoral locking-plate fixation is a treatment option that results in fewer perioperative and postoperative cardiopulmonary events and surgical complications. Most patients can ambulate with or without an assistive device at the final follow-up

Outcomes of Proximal Femoral Locking-plate Fixation for Pathological Fractures of the Proximal Femur

Objective: To study the treatment outcomes of proximal femoral locking-plate fixation of pathological fractures of the proximal femur relative to clinical results, implant failure, and surgical complications. Materials and Methods: From 2007 to 2018, 17 patients (18 femurs) with a diagnosis of impending or existing pathological fracture of the proximal femur were treated with proximal femoral locking-plate fixation. Data collected included operative duration, estimated blood loss, ambulatory status, hardware failure events, and postoperative complications. Results: Of the 18 femurs that were included, 13 were existing pathological fractures and 5 were impending fractures. The mean age of patients was 53.7 years (range: 28-89), and 12 of them were female. The mean follow-up time was 11.3 months (range: 1-67). Ten of 17 patients (62.5%) had progressive lung disease from pulmonary metastasis or from lung primary. No patient developed oxygen desaturation or cardiac arrest during the intraoperative or postoperative period. Thirteen of 17 patients (76.5%) could walk with or without an assistive device at the time of final follow-up. Two patients required close postoperative monitoring in the intensive care unit due to poor preoperative status, and both of those patients died within one month after surgery from other medical problems. No hardware failure occurred. Conclusion: For pathological fracture of the proximal femur, proximal femoral locking-plate fixation is a treatment option that results in fewer perioperative and postoperative cardiopulmonary events and surgical complications. Most patients can ambulate with or without an assistive device at the final follow-up

Sarcopenia in Thai community-dwelling older adults: a national, cross-sectional, epidemiological study of prevalence and risk factors

Abstract Background Sarcopenia is an age-related condition characterized by a progressive loss of skeletal muscle mass. It leads to declining physical performance, potentially culminating in a diminished quality of life or death. This study investigated the prevalence of sarcopenia and its associated risk factors among Thai community-dwelling individuals of advanced age. Methods Between March 2021 and August 2022, we conducted a nationwide community-based epidemiological survey across all six major regions of Thailand. Participants with sarcopenia were identified according to the 2019 criteria of the Asian Working Group for Sarcopenia (AWGS). The risk factors were examined using multivariable logistic regression. Results Of the 2456 participants, the overall prevalence of sarcopenia was 18.1%, with nearly two-thirds (66.9%) classified as having severe sarcopenia. Multivariate analysis identified six associated risk factors for sarcopenia. They are a lower body mass index (odds ratio [OR] = 11.7, 95% confidence interval [CI] = 7.8–17.4), suboptimal leg calf circumference (OR = 6.3, 95% CI = 4.3–9.5), male sex (OR = 2.8, 95% CI = 2.2–3.7), a history of chronic obstructive pulmonary disease (OR = 2.3, 95% CI = 2.3–5.0), advanced age (OR = 2.1, 95% CI = 1.3–3.3), and an increasing time in the timed up-and-go test (OR = 1.1, 95% CI = 1.0–1.1). Conclusions This is the first large-scale national study to represent the prevalence and risk factors for sarcopenia in Thai community-dwelling individuals of advanced age using the AWGS 2019 criteria. Interventions such as lifestyle modifications and appropriate nutrition should be promoted throughout adulthood to maintain muscle strength and delay the onset of sarcopenia, particularly in males. Trial registration The Central Research Ethics Committee of the National Research Council of Thailand authorized the study protocol (approval number COA-CREC023/2021)

Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors

This retrospective study aimed to compare the intra- and inter-observer manual-segmentation variability in the feature reproducibility between two-dimensional (2D) and three-dimensional (3D) magnetic-resonance imaging (MRI)-based radiomic features. The study included patients with lipomatous soft-tissue tumors that were diagnosed with histopathology and underwent MRI scans. Tumor segmentation based on the 2D and 3D MRI images was performed by two observers to assess the intra- and inter-observer variability. In both the 2D and the 3D segmentations, the radiomic features were extracted from the normalized images. Regarding the stability of the features, the intraclass correlation coefficient (ICC) was used to evaluate the intra- and inter-observer segmentation variability. Features with ICC > 0.75 were considered reproducible. The degree of feature robustness was classified as low, moderate, or high. Additionally, we compared the efficacy of 2D and 3D contour-focused segmentation in terms of the effects of the stable feature rate, sensitivity, specificity, and diagnostic accuracy of machine learning on the reproducible features. In total, 93 and 107 features were extracted from the 2D and 3D images, respectively. Only 35 features from the 2D images and 63 features from the 3D images were reproducible. The stable feature rate for the 3D segmentation was more significant than for the 2D segmentation (58.9% vs. 37.6%, p = 0.002). The majority of the features for the 3D segmentation had moderate-to-high robustness, while 40.9% of the features for the 2D segmentation had low robustness. The diagnostic accuracy of the machine-learning model for the 2D segmentation was close to that for the 3D segmentation (88% vs. 90%). In both the 2D and the 3D segmentation, the specificity values were equal to 100%. However, the sensitivity for the 2D segmentation was lower than for the 3D segmentation (75% vs. 83%). For the 2D + 3D radiomic features, the model achieved a diagnostic accuracy of 87% (sensitivity, 100%, and specificity, 80%). Both 2D and 3D MRI-based radiomic features of lipomatous soft-tissue tumors are reproducible. With a higher stable feature rate, 3D contour-focused segmentation should be selected for the feature-extraction process

Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

Abstract Background To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists. Methods The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test. Results There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05). Conclusions The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance