Dataset Information

Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.

ABSTRACT: Background: It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relationship between the thinning pattern of the cartilages and the increasing ages. Methods: In this retrospective study, 2,481 healthy knees (subjects ranging from 15 to 64 years old, mean age: 35 ± 10 years) were recruited. With magnetic resonance images of knees acquired on a 3-T superconducting scanner, we automatically and precisely segmented the cartilage via deep learning and calculated the cartilage thickness in 14 anatomical regions. The thickness readings were compared using ANOVA by considering the factors of age, sex, and side. We further tracked the relationship between the thinning pattern of the cartilage thickness and the increasing ages by regression analysis. Results: The cartilage thickness was always thicker in the femur than corresponding regions in the tibia (p < 0.05). Regression analysis suggested cartilage thinning alongside ages in all regions (p < 0.05) except for medial and lateral anterior tibia in both females and males (p > 0.05). The thinning speed of men was faster than women in medial anterior and lateral anterior femur, yet slower in the medial patella (p < 0.05). Conclusion: We established the calculation method of cartilage thickness using big data and deep learning. We demonstrated that cartilage thickness differed across individual regions in the knee joint. Cartilage thinning alongside ages was identified, and the thinning pattern was consistent in the tibia while inconsistent in patellar and femoral between sexes. These findings provide a potential reference to detect cartilage anomaly.

SUBMITTER: Si L

PROVIDER: S-EPMC7900571 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.

Si Liping L Xuan Kai K Zhong Jingyu J Huo Jiayu J Xing Yue Y Geng Jia J Hu Yangfan Y Zhang Huan H Wang Qian Q Yao Weiwu W

Frontiers in medicine 20210209

<b>Background:</b> It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relation ...[more]

PMID: 33634142

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Project description:BackgroundCartilage morphometry based on magnetic resonance images (MRIs) is an emerging outcome measure for clinical trials among patients with knee osteoarthritis (KOA). However, current methods for cartilage morphometry take many hours per knee and require extensive training on the use of the associated software. In this study we tested the feasibility, reliability, and construct validity of a novel osteoarthritis cartilage damage quantification method (Cartilage Damage Index [CDI]) that utilizes informative locations on knee MRIs.MethodsWe selected 102 knee MRIs from the Osteoarthritis Initiative that represented a range of KOA structural severity (Kellgren Lawrence [KL] Grade 0 - 4). We tested the intra- and inter-tester reliability of the CDI and compared the CDI scores against different measures of severity (radiographic joint space narrowing [JSN] grade, KL score, joint space width [JSW]) and static knee alignment, both cross-sectionally and longitudinally.ResultsDetermination of the CDI took on average14.4 minutes (s.d. 2.1) per knee pair (baseline and follow-up of one knee). Repeatability was good (intra- and inter-tester reliability: intraclass correlation coefficient >0.86). The mean CDI scores related to all four measures of osteoarthritis severity (JSN grade, KL score, JSW, and knee alignment; all p values < 0.05). Baseline JSN grade and knee alignment also predicted subsequent 24-month longitudinal change in the CDI (p trends <0.05). During 24 months, knees with worsening in JSN or KL grade (i.e. progressors) had greater change in CDI score.ConclusionsThe CDI is a novel knee cartilage quantification method that is rapid, reliable, and has construct validity for assessment of medial tibiofemoral osteoarthritis structural severity and its progression. It has the potential to addresses the barriers inherent to studies requiring assessment of cartilage damage on large numbers of knees, and as a biomarker for knee osteoarthritis progression.

Project description:ObjectiveSince the first introduction of the MOCART (Magnetic Resonance Observation of Cartilage Repair Tissue) score, significant progress has been made with regard to surgical treatment options for cartilage defects, as well as magnetic resonance imaging (MRI) of such defects. Thus, the aim of this study was to introduce the MOCART 2.0 knee score - an incremental update on the original MOCART score - that incorporates this progression.Materials and methodsThe volume of cartilage defect filling is now assessed in 25% increments, with hypertrophic filling of up to 150% receiving the same scoring as complete repair. Integration now assesses only the integration to neighboring native cartilage, and the severity of surface irregularities is assessed in reference to cartilage repair length rather than depth. The signal intensity of the repair tissue differentiates normal signal, minor abnormal, or severely abnormal signal alterations. The assessment of the variables "subchondral lamina," "adhesions," and "synovitis" was removed and the points were reallocated to the new variable "bony defect or bony overgrowth." The variable "subchondral bone" was renamed to "subchondral changes" and assesses minor and severe edema-like marrow signal, as well as subchondral cysts or osteonecrosis-like signal. Overall, a MOCART 2.0 knee score ranging from 0 to 100 points may be reached. Four independent readers (two expert readers and two radiology residents with limited experience) assessed the 3 T MRI examinations of 24 patients, who had undergone cartilage repair of a femoral cartilage defect using the new MOCART 2.0 knee score. One of the expert readers and both inexperienced readers performed two readings, separated by a four-week interval. For the inexperienced readers, the first reading was based on the evaluation sheet only. For the second reading, a newly introduced atlas was used as an additional reference. Intrarater and interrater reliability was assessed using intraclass correlation coefficients (ICCs) and weighted kappa statistics. ICCs were interpreted according to Koo and Li; weighted kappa statistics were interpreted according to the criteria of Landis and Koch.ResultsThe overall intrarater (ICC = 0.88, P < 0.001) as well as the interrater (ICC = 0.84, P < 0.001) reliability of the expert readers was almost perfect. Based on the evaluation sheet of the MOCART 2.0 knee score, the overall interrater reliability of the inexperienced readers was poor (ICC = 0.34, P < 0.019) and improved to moderate (ICC = 0.59, P = 0.001) with the use of the atlas.ConclusionsThe MOCART 2.0 knee score was updated to account for changes in the past decade and demonstrates almost perfect interrater and intrarater reliability in expert readers. In inexperienced readers, use of the atlas may improve interrater reliability and, thus, increase the comparability of results across studies.

Project description:PurposeTo establish normative values for articular cartilage thickness in pediatric and adolescent knees using magnetic resonance imaging (MRI) and investigate for any associations with age and skeletal maturity.MethodsMRI scans were analyzed in patients 7 to 18 years old without osteochondral lesions, chondral wear/pathology, intra-articular fractures, or history of knee surgery. Measurements of articular cartilage thickness at the patella (medial facet, lateral facet, median ridge), femur (medial condyle, lateral condyle, lateral trochlea), and tibia (medial plateau, lateral plateau) were made on axial, coronal, and sagittal MRI. Descriptive statistics were used to calculate mean cartilage thickness by age and sex. Analysis of variance with repeated measures, analysis of covariance, independent samples t test, and linear regression were performed to determine differences in mean cartilage thickness by anatomic location, sex, physeal status, and age, respectively.ResultsA total of 240 knee MRI scans were included. Articular cartilage was thickest at the patella and did not vary with age or skeletal maturity. On the femur, articular cartilage was thickest at the lateral trochlea with mean cartilage thickness of 4.4 ± 1.4 mm in male patients and 3.6 ± 1.3 mm in female patients (P < .001). Patients with open distal femoral physes had significantly thicker cartilage at the medial femoral condyle, lateral femoral condyle, and lateral trochlea compared to patients with closing/closed physes (P < .001). Linear regression analysis revealed a significant inverse association between cartilage thickness at the femur and age.ConclusionsIn pediatric and adolescent knees, articular cartilage is thickest at the patella, where it does not strongly correlate with age. In contrast, there is a strong inverse association between increasing age and articular cartilage thickness of the distal femoral condyles.Clinical relevanceThe longitudinal reference data presented in this study can aid in pre-operative interpretation of knee cartilage under pathologic conditions in pediatric and adolescent patients.

Project description:ObjectiveTo determine whether an anabolic drug (sprifermin) is capable of reducing cartilage loss wherever it occurs in a given knee, using a subject-specific, location-independent analysis of cartilage change in patients with knee osteoarthritis (OA).MethodsStudy participants (n = 168; ages ≥40 years, 69% women) had symptomatic femorotibial OA not confined to the medial compartment. Sprifermin (10, 30, or 100 μg) or placebo was injected intraarticularly 3 times over 3 weeks, both after randomization (baseline) and 3 months later. Coronal magnetic resonance images were acquired at baseline and 3, 6, and 12 months after treatment. The femorotibial cartilage of each subject was segmented, and changes in cartilage thickness were computed across 16 subregions. Location-independent post hoc analysis was used to compute summary scores of negative and positive changes in the subregions, summarized as the total cartilage thinning sum score (ThCTnS) and the total cartilage thickening sum score (ThCTkS), capturing change in either direction in each knee. Ordered values of the magnitude of subject-specific subregional changes in thickness were determined. The ThCTnS and ThCTkS in each sprifermin dose group at 12 months of followup were compared with the values in the matched placebo groups, using the Wilcoxon-Mann-Whitney test.ResultsThe mean ± SD ThCTnS was -591 ± 617 μm (median -360 μm, Q1/Q3 = -820/-200 μm) in patients treated with 100 μg sprifermin (n = 57), and -921 ± 777 μm (median -745 μm, Q1/Q3 = -1,190/-380 μm) in patients given placebo (n = 18). The mean difference in the ThCTnS between the 100-μg sprifermin group and the placebo group was 331 μm (95% confidence interval [95% CI] 24, 685), a difference that was statistically significant (P = 0.03). The mean difference in the ThCTkS in the 100-μg sprifermin group compared with the placebo group was 237 μm (95% CI 34, 440), also a statistically significant difference (P = 0.028).ConclusionSprifermin not only increases cartilage thickness, but also reduces cartilage loss. Subject-specific, location-independent analysis of both cartilage thinning and thickening represents a sensitive and informative approach for studying the effects of disease-modifying OA drugs.

Dataset Information

Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.

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Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.

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