内側半月板根部断裂の内側形態学的危険因子：放射線学的および解剖学的分析

Medial meniscus posterior root tears (MMRTs) disrupt hoop stress transmission and biomechanically resemble total meniscectomy, accelerating medial compartment overload and degenerative progression. While demographic risk factors are well documented, the contribution of specific knee morphological characteristics remains incompletely understood.

This retrospective study included 44 patients with isolated MMRT confirmed by MRI and 79 controls with normal MRI findings. Morphological parameters were measured on knee MRI and standing full-length radiographs. Between-group comparisons were performed using independent t-tests after outlier exclusion. Variables with p < 0.10 in univariate logistic regression were entered into multivariable modelling, and receiver operating characteristic (ROC) analysis assessed diagnostic performance.

MMRT patients were significantly older and demonstrated greater medial meniscus extrusion (MMEM), posterior femoral offset (PFO), medial coronal tibial slope (MKTS), and anatomical lateral distal femoral angle (aLDFA), while lateral coronal tibial slope (LKTS) was lower compared with controls (all p < 0.05). In univariate analysis, PFO showed the strongest association with MMRT (OR = 257.4), whereas MMEM, MKTS, aLDFA, LKTS, and age also demonstrated significant associations. Multivariable logistic regression identified age (OR = 1.09), MKTS (OR = 2.18), LKTS (OR = 0.59), MMEM (OR = 2.42), and aLDFA (OR = 1.47) as independent predictors of MMRT.ROC analysis showed excellent discriminative ability for age (AUC = 0.88) and MMEM (AUC = 0.85), with moderate performance for MKTS, LKTS, and aLDFA.

Distinct morphological features-including increased medial meniscus extrusion, steeper medial tibial slope, reduced lateral tibial slope, and greater distal femoral valgus alignment-are independently associated with MMRTs. Age and MMEM provide the highest diagnostic accuracy. Recognition of these anatomical risk factors may facilitate earlier detection of MMRT and support risk-stratified clinical decision-making.