Objective: To evaluate upstaging, lymph node (LN) metastasis, and recurrence in patients with presumed stage I endometrial cancer using preoperative magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT). Methods: Retrospective review of 422 patients with presumed clinical stage I endometrial cancer diagnosed via MRI and PET-CT (July 2014–June 2023). Surgical staging included pelvic lymph nodes (PLNs) and para-aortic lymph nodes (PALNs), classifying patients as low/ intermediate- or high-risk groups. Results: Post-operative upstaging rate was 14.5% (8.8% low/intermediate-risk vs. 22.8% high-risk, p<0.001). LN metastasis occurred in 5.5% of patients (2.0% low/intermediaterisk vs. 10.5% high-risk, p<0.001), with a dual imaging negative predictive value of 0.945.PLN metastasis was 4.5% (2.0% low/intermediate vs. 8.2% high-risk, p=0.003), and PALN metastasis was 2.6% (0.4% low/intermediate-risk vs. 5.8% high-risk, p=0.001). In low/ intermediate-risk group: tumors ≤2cm had 1.1% LN metastasis rate, endometrium-limited 0.8%, and ≤2cm with endometrium-limited 0.9%. Deep myometrial invasion (odds ratio [OR]=4.4; 95% confidence intervals [CIs]=1.6–12.4) and tumor size >2 cm on MRI (OR=2.9;95% CI=0.8–9.9) increased LN metastasis risk. Median 48.5-month follow-up showed an 8.1% overall recurrence rate (4.0% low/intermediate-risk vs. 14.0% high-risk, p<0.001), with 2.4% nodal recurrences (1.2% low/intermediate-risk vs. 4.1% high-risk). Conclusion High-risk patients had significant upstaging, LN metastasis, and recurrence rates. Even in low/intermediate-risk groups, some patients exhibited LN metastasis and nodal recurrence, underscoring the importance of comprehensive surgical staging, including PALN evaluation, for precise diagnosis and treatment.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.