Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: To analyze the predictive factors for neck pain and cervical spine function after laminoplasty for degenerative cervical myelopathy (DCM) using K-means for longitudinal data (KML). Methods: In this prospective cohort study, we collected clinical and radiographic data from patients with DCM who underwent cervical laminoplasty. A novel index of surgical outcome, “neck function,” which comprises neck pain and cervical spine function according to the Japanese Orthopedic Association Cervical Myelopathy Evaluation Questionnaire, was proposed. We treated surgical outcomes as longitudinal rather than cross-sectional data and used KML for analysis. Patients were categorized as having good or poor outcomes based on the KML graph of neck pain and cervical spine function. Results: From 2016 to 2020, 104 patients underwent laminoplasty for DCM; however, 35 patients were excluded because of loss to follow-up or incomplete data. The authors found that central canal stenosis (odds ratio [OR], 17.93; 95% confidence interval [CI], 1.26–254.73; p=0.03) and preoperative neck pain (OR per 1 point increase=1.49; 95% CI, 1.12–1.99; p=0.006) were 2 negative predictive factors and that a positive K-line during flexion was a positive predictive factor (OR, 0.11; 95% CI, 0.01–0.87; p=0.036) for neck function after laminoplasty. Conclusion Central canal stenosis, preoperative neck pain and a K-line during flexion were found to be predictive of postoperative neck pain and cervical spine function after laminoplasty. To achieve better surgical outcomes for neck function, the authors suggest the utilization of these determinants as a guiding framework for the selection of surgical approaches for DCM.

Objective: To analyze the predictive factors for neck pain and cervical spine function after laminoplasty for degenerative cervical myelopathy (DCM) using K-means for longitudinal data (KML). Methods: In this prospective cohort study, we collected clinical and radiographic data from patients with DCM who underwent cervical laminoplasty. A novel index of surgical outcome, “neck function,” which comprises neck pain and cervical spine function according to the Japanese Orthopedic Association Cervical Myelopathy Evaluation Questionnaire, was proposed. We treated surgical outcomes as longitudinal rather than cross-sectional data and used KML for analysis. Patients were categorized as having good or poor outcomes based on the KML graph of neck pain and cervical spine function. Results: From 2016 to 2020, 104 patients underwent laminoplasty for DCM; however, 35 patients were excluded because of loss to follow-up or incomplete data. The authors found that central canal stenosis (odds ratio [OR], 17.93; 95% confidence interval [CI], 1.26–254.73; p=0.03) and preoperative neck pain (OR per 1 point increase=1.49; 95% CI, 1.12–1.99; p=0.006) were 2 negative predictive factors and that a positive K-line during flexion was a positive predictive factor (OR, 0.11; 95% CI, 0.01–0.87; p=0.036) for neck function after laminoplasty. Conclusion Central canal stenosis, preoperative neck pain and a K-line during flexion were found to be predictive of postoperative neck pain and cervical spine function after laminoplasty. To achieve better surgical outcomes for neck function, the authors suggest the utilization of these determinants as a guiding framework for the selection of surgical approaches for DCM.

Objective: To analyze the predictive factors for neck pain and cervical spine function after laminoplasty for degenerative cervical myelopathy (DCM) using K-means for longitudinal data (KML). Methods: In this prospective cohort study, we collected clinical and radiographic data from patients with DCM who underwent cervical laminoplasty. A novel index of surgical outcome, “neck function,” which comprises neck pain and cervical spine function according to the Japanese Orthopedic Association Cervical Myelopathy Evaluation Questionnaire, was proposed. We treated surgical outcomes as longitudinal rather than cross-sectional data and used KML for analysis. Patients were categorized as having good or poor outcomes based on the KML graph of neck pain and cervical spine function. Results: From 2016 to 2020, 104 patients underwent laminoplasty for DCM; however, 35 patients were excluded because of loss to follow-up or incomplete data. The authors found that central canal stenosis (odds ratio [OR], 17.93; 95% confidence interval [CI], 1.26–254.73; p=0.03) and preoperative neck pain (OR per 1 point increase=1.49; 95% CI, 1.12–1.99; p=0.006) were 2 negative predictive factors and that a positive K-line during flexion was a positive predictive factor (OR, 0.11; 95% CI, 0.01–0.87; p=0.036) for neck function after laminoplasty. Conclusion Central canal stenosis, preoperative neck pain and a K-line during flexion were found to be predictive of postoperative neck pain and cervical spine function after laminoplasty. To achieve better surgical outcomes for neck function, the authors suggest the utilization of these determinants as a guiding framework for the selection of surgical approaches for DCM.

Objective: To analyze the predictive factors for neck pain and cervical spine function after laminoplasty for degenerative cervical myelopathy (DCM) using K-means for longitudinal data (KML). Methods: In this prospective cohort study, we collected clinical and radiographic data from patients with DCM who underwent cervical laminoplasty. A novel index of surgical outcome, “neck function,” which comprises neck pain and cervical spine function according to the Japanese Orthopedic Association Cervical Myelopathy Evaluation Questionnaire, was proposed. We treated surgical outcomes as longitudinal rather than cross-sectional data and used KML for analysis. Patients were categorized as having good or poor outcomes based on the KML graph of neck pain and cervical spine function. Results: From 2016 to 2020, 104 patients underwent laminoplasty for DCM; however, 35 patients were excluded because of loss to follow-up or incomplete data. The authors found that central canal stenosis (odds ratio [OR], 17.93; 95% confidence interval [CI], 1.26–254.73; p=0.03) and preoperative neck pain (OR per 1 point increase=1.49; 95% CI, 1.12–1.99; p=0.006) were 2 negative predictive factors and that a positive K-line during flexion was a positive predictive factor (OR, 0.11; 95% CI, 0.01–0.87; p=0.036) for neck function after laminoplasty. Conclusion Central canal stenosis, preoperative neck pain and a K-line during flexion were found to be predictive of postoperative neck pain and cervical spine function after laminoplasty. To achieve better surgical outcomes for neck function, the authors suggest the utilization of these determinants as a guiding framework for the selection of surgical approaches for DCM.

Objective: To analyze the predictive factors for neck pain and cervical spine function after laminoplasty for degenerative cervical myelopathy (DCM) using K-means for longitudinal data (KML). Methods: In this prospective cohort study, we collected clinical and radiographic data from patients with DCM who underwent cervical laminoplasty. A novel index of surgical outcome, “neck function,” which comprises neck pain and cervical spine function according to the Japanese Orthopedic Association Cervical Myelopathy Evaluation Questionnaire, was proposed. We treated surgical outcomes as longitudinal rather than cross-sectional data and used KML for analysis. Patients were categorized as having good or poor outcomes based on the KML graph of neck pain and cervical spine function. Results: From 2016 to 2020, 104 patients underwent laminoplasty for DCM; however, 35 patients were excluded because of loss to follow-up or incomplete data. The authors found that central canal stenosis (odds ratio [OR], 17.93; 95% confidence interval [CI], 1.26–254.73; p=0.03) and preoperative neck pain (OR per 1 point increase=1.49; 95% CI, 1.12–1.99; p=0.006) were 2 negative predictive factors and that a positive K-line during flexion was a positive predictive factor (OR, 0.11; 95% CI, 0.01–0.87; p=0.036) for neck function after laminoplasty. Conclusion Central canal stenosis, preoperative neck pain and a K-line during flexion were found to be predictive of postoperative neck pain and cervical spine function after laminoplasty. To achieve better surgical outcomes for neck function, the authors suggest the utilization of these determinants as a guiding framework for the selection of surgical approaches for DCM.