BACKGROUND: The correlation between the morphological structure of the patellofemoral joint (PFJ) and the severity of knee joint osteoarthritis (KOA) remains uncertain. This study aims to investigate the correlation between the severity of knee joint osteoarthritis and the alignment of patellofemoral and patellar height on radiographs. METHODS: This multi-center, retrospective study analyzed the magnetic resonance imaging (MRI) scans and anteroposterior radiographs of 534 adult outpatients with KOA. To evaluate the radiographic severity of KOA, anteroposterior radiographs of the knee and the Kellgren-Lawrence (K-L) grade were used. Knee MRI scans were used to measure the patellar length ratio (PLR), sulcus angle (SA), lateral patellar tilt angle (LPTA), and the distance between tibial tuberosity and trochlear groove (TT-TG). We examined the association between the configuration of the PFJ, arrangement, and harshness of the KOA. Information on participants' demographics, such as age, sex, side, height, and weight, was collected. A chi-squared test was used for the correlation of radiographic severity of KOA with sex and the affected side. Spearman correlation was used for patellofemoral alignment or morphology and the radiographic severity of lateral KOA. Multiple linear regression models were used for the association between LPTA, SA, TT-TG, and severity of KOA after accounting for demographic variables. RESULTS: The study comprised of 534 patients; of these, 339 (63%) were female. A total of 586 knees were evaluated in this study. Age showed a strong positive correlation with KOA severity ( r  = 0.516, P <0.01), whereas LPTA showed a strong negative correlation ( r  = -0.662, P <0.01). Additionally, SA ( r  = 0.616, P <0.05), and TT-TG showed a strong positive correlation ( r  = 0.770, P <0.01) with tibiofemoral osteoarthritis (TFOA) severity. Multiple linear regression analysis indicated that knee osteoarthritis severity (β = -2.946, P <0.001) and side (β = -0.839, P  = 0.001) was associated with LPTA; knee osteoarthritis severity (β = 5.032, P <0.001) and age (β = -0.095, P <0.001) was associated with SA; knee osteoarthritis severity (β = 2.445, P <0.001), sex (β = -0.326, P  = 0.041), body mass index (β = -0.061, P  = 0.017) and age (β = -0.025, P <0.001) was associated with TT-TG. CONCLUSION Radiographic severity of KOA was positively associated with age, SA, and TT-TG but negatively associated with LPTA.
Humans ; Female ; Male ; Osteoarthritis, Knee/pathology* ; Middle Aged ; Retrospective Studies ; Aged ; Patellofemoral Joint/pathology* ; Magnetic Resonance Imaging ; Adult ; Patella/pathology* ; Radiography

Objective To investigate the application value of clinical-radiomics nomogram in predicting the expression of liver kinase B1(LKB1)in non-small cell lung cancer(NSCLC)before surgery.Methods A total of 140 NSCLC patients were randomized into training group(n=106)and validation group(n=34)according to the ratio of 7∶3.The training group was used as the study cohort to screen the clinically independent predictors and radiomics characteristics related to LKB1 expression,and the clinical model,radiomics model and clinical-radiomics nomogram model were constructed,respectively.The predictive performance of the three models was analyzed using the receiver operating characteristic(ROC)curve in the training group,and validated in the validation group.The calibration curve was used to assess the consistency between the predicted results of nomogram model and the actual observations,and the decision curve was used to evaluate the clinical benefit of the nomogram model.Results The clinical model consisted of pathological type and hilal/mediastinal lymphadenopathy,the radiomics model consisted of Radiomics score(Radscore),and the nomogram model consisted of Radscore,pathological type and hilal/mediastinal lymphadenopathy.In the training group,the area under the curve(AUC)of the nomogram model,radiomics model and clinical model was 0.884,0.843 and 0.788,respectively.In the validation group,the AUC of the three models were 0.976,0.851,and 0.912,respectively.The calibration curve analysis showed good consis-tency between the predicted results of nomogram model and the actual observations,and the decision curve showed that the model had good clinical benefit.Conclusion Radiomics combined with clinical risk factors can effectively predict the expression of LKB1 in NSCLC patients before surgery,so as to contribute to the formulation of therapeutic strategies in clinical practice.

Objective To investigate the application value of clinical-radiomics nomogram in predicting the expression of liver kinase B1(LKB1)in non-small cell lung cancer(NSCLC)before surgery.Methods A total of 140 NSCLC patients were randomized into training group(n=106)and validation group(n=34)according to the ratio of 7∶3.The training group was used as the study cohort to screen the clinically independent predictors and radiomics characteristics related to LKB1 expression,and the clinical model,radiomics model and clinical-radiomics nomogram model were constructed,respectively.The predictive performance of the three models was analyzed using the receiver operating characteristic(ROC)curve in the training group,and validated in the validation group.The calibration curve was used to assess the consistency between the predicted results of nomogram model and the actual observations,and the decision curve was used to evaluate the clinical benefit of the nomogram model.Results The clinical model consisted of pathological type and hilal/mediastinal lymphadenopathy,the radiomics model consisted of Radiomics score(Radscore),and the nomogram model consisted of Radscore,pathological type and hilal/mediastinal lymphadenopathy.In the training group,the area under the curve(AUC)of the nomogram model,radiomics model and clinical model was 0.884,0.843 and 0.788,respectively.In the validation group,the AUC of the three models were 0.976,0.851,and 0.912,respectively.The calibration curve analysis showed good consis-tency between the predicted results of nomogram model and the actual observations,and the decision curve showed that the model had good clinical benefit.Conclusion Radiomics combined with clinical risk factors can effectively predict the expression of LKB1 in NSCLC patients before surgery,so as to contribute to the formulation of therapeutic strategies in clinical practice.