Accuracy of standard magnetic resonance imaging sequences for meniscal and chondral lesions versus knee arthroscopy. A prospective case-controlled study of 719 cases

Improved Outcome With Knee Arthroscopy Relative to Physiotherapy for Symptomatic Unstable Meniscal Tears: 2-Year Prospective Cohort Study

BACKGROUND

Although preservation of meniscal tissue is paramount, there are occasions when repair of a torn meniscus is not possible. The surgical option may be a partial meniscectomy, the objective being to alleviate the patient's symptoms with resection of only that portion of the meniscus that is no longer functioning but is the cause of symptoms. Previous studies have questioned the need to perform such surgery and have recommended nonoperative treatment instead. Our goal was to compare the outcome of partial meniscectomy with that of physiotherapy alone for irreparable meniscal tears.

HYPOTHESIS

Clinical outcome following arthroscopic partial meniscectomy may differ from that following physiotherapy alone, for patients with symptomatic irreparable meniscal tears.

STUDY DESIGN

Nonrandomized prospective cohort study.

LEVEL OF EVIDENCE

Level 2.

METHODS

Patients satisfying the inclusion criteria chose to undergo knee arthroscopy (group A) or physiotherapy (group B). The diagnosis of a meniscal tear was made on physical assessment and magnetic resonance imaging examination. The meniscal tear was preventing them from continuing with their regular weightbearing exercise. Outcomes of interest were the following patient-reported outcomes (PROs): the Knee Osteoarthritis Outcome Score (KOOS) and Tegner Activity Score (TAS), with minimal clinically important differences being 10 and 1, respectively. PROs were completed at baseline, and at 1-year and 2-year follow-up. Changes in scores within and between groups were compared using analysis of variance and Wilcoxon tests (P <0.05). A power analysis demanded 65 patients in each group to produce a power of 80% and a P value of 5%.

RESULTS

Of 528 patients enrolled in the study, 10 were lost to follow-up and 8 were excluded. Data were complete for 269 in group A, and 228 in Group B. Group A and B were similar in terms of age (41.1 years, SD 7.8 vs 40 years, SD 13.3), body mass index (22.5 kg/m2, SD 3.1 vs 23.1 kg/m2, SD 2.3), radiographic grade of osteoarthritis (median grade 2, range 0-3 in both groups), gender (male:female = 134:135 vs 112:116), and duration of symptoms (44.4 days, SD 5.6 vs 46.6 days, SD 8.8), with P >0.05. At both 1-year and 2-year follow-up, Group A had higher scores in the mean total KOOS (88.8, SD 8.0 vs 72.4, SD 3.8), as well as in all subscales within the KOOS, and the TAS (median 7, range 5-9 vs median 5, range 3-6), with P <0.001 for each variable.

CONCLUSION

Performing a knee arthroscopy with partial meniscectomy is associated with improved KOOS and TAS, relative to treatment with physiotherapy alone, at 2-year follow-up.

CLINICAL RELEVANCE

Physically active patients with symptomatic irreparable meniscal tears may experience a better clinical outcome following knee arthroscopy, relative to to physiotherapy alone.

Quantitative Magnetic Resonance Imaging Had Greater Sensitivity in Diagnosing Chondral Lesions of the Knee: A Systematic Review and Meta-Analysis

PURPOSE

To investigate the accuracy and reliability of magnetic resonance imaging (MRI) in identifying and grading chondral lesions and explore the optimal imaging technique to image cartilage.

METHOD

A comprehensive search was conducted on Medline, Embase, and Cochrane Library. Eligible cohort studies published before August 2022 were included. The study reports used MRI to diagnose and grade cartilage lesions, with intraoperative findings as the reference standard. Summary estimates of diagnostic performance were obtained. The reliability of MRI interpretation was summarized. Subgroup analyses were performed based on assessed imaging techniques, field strength, and joint surface.

RESULTS

Forty-three trials and 3,706 patients were included in the systematic review. The overall area under curve for hierarchical summarized receiver operating characteristics was 0.91 (95% confidence interval [CI] 0.88-0.93). The pooled sensitivity for quantitative MRI, 3-dimensional MRI, and 2-dimensional MRI was 0.82 (95% CI 0.64-0.92), 0.79 (95% CI 0.74-0.83), and 0.63 (95% CI 0.51-0.73), respectively. The pooled sensitivity of 3 Tesla (3T), 1.5 Tesla (1.5T), and <1.5 Tesla MRI was 0.79 (95% CI 0.72-0.85), 0.67 (95% CI 0.60-0.74), and 0.55 (95% CI 0.39-0.71), respectively. There were differences in interobserver consistency across different studies.

CONCLUSIONS

In general, MRI had high specificity in discriminating normal cartilage, but its sensitivity for identifying chondral lesions is less optimal. Further analysis showed that quantitative MRI, 3D MRI, and 3T MRI demonstrate greater sensitivity compared with 2D MRI, 1.5T MRI, and <1.5 Tesla MRI.

LEVEL OF EVIDENCE

Level III, systematic review of Level II-III studies.

Preoperative diagnosis of knee cartilage, meniscal, and ligament injuries by magnetic resonance imaging

PURPOSE

The purpose of the study was to report on the current accuracy measures specific to 1.5-Tesla MRI of the knee in the patient population prone to injuries of the anterior cruciate ligament (ACL), the menisci, and the articular cartilage.

METHODS

We accrued patients between January 2018 through August 2021 who underwent a preoperative MRI and were diagnosed with an articular cartilage injury either due to unevenness of articular cartilage in T2-weighted sequences or due to the irregularity of subchondral bone in T1-weighted sequences. All patients were treated arthroscopically. Sensitivity, specificity, and accuracy were calculated for the detection of ACL, meniscus, and cartilage injuries. A P-value of < 0.05 represented statistical significance.

RESULTS

One-hundred and forty-seven cases which included 150 knee joints were enrolled in this study. The mean age at the time of surgery was 42.9 years-old. The sensitivity in the diagnosis of ACL injuries was significantly greater than that in the diagnosis of cartilage injuries (P = 0.0083). The ratios of the equality of operative indication in 6 recipient sites were found to be between 90.0% and 96.0%. The diagnostic critical point was within a 1 cm in diameter.

CONCLUSION

The diagnostic sensitivity in cartilage injuries was significantly lower than ones of ACL and meniscal injuries. The ratios of the equality of operative indication was determined to be between 90.0% and 96.0%, if we consider the unevenness of articular cartilage or the irregularity of subchondral bone.

LEVEL OF EVIDENCE

Level III, Prospective diagnostic cohort study.

Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN

BACKGROUND

The diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis.

PURPOSE

This study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears.

MATERIALS AND METHODS

Our institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the arthroscopic surgical diagnosis.

RESULTS

Our model produced strong Dice coefficients for sagittal PDW of 0.924, 0.95 sensitivity with 0.823 FPs/knee. 3D-Unet produced a Dice coefficient for sagittal PDW of 0.891, 0.95 sensitivity with 1.355 FPs/knee. The difference in the areas under 3D-Mask-RCNN FROC and 3D-Unet FROC was statistically significant (p = 0.0011) by bootstrap test. Our model detection performance achieved an area under the curve (AUC) value, accuracy, and sensitivity of 0.907, 0.924, 0.941, and 0.785, respectively. Based on the radiological evaluations, the AUC value, accuracy, sensitivity, and specificity were 0.834, 0.835, 0.889, and 0.754, respectively. The difference in the areas between 3D-Mask-RCNN ROC and radiological evaluation ROC was statistically significant (p = 0.0009) by bootstrap test. 3D Mask RCNN significantly outperformed the 3D-Unet and radiological evaluation demonstrated by these results.

CONCLUSIONS

3D-Mask RCNN has demonstrated efficacy and precision for meniscus segmentation and tear detection in knee MRI, which can assist radiologists in improving the accuracy and efficiency of diagnosis. It can also provide effective diagnostic indicators for orthopedic surgeons before arthroscopic surgery and further promote precise treatment.

Near-Infrared Spectroscopy Enables Arthroscopic Histologic Grading of Human Knee Articular Cartilage

Purpose

To develop the means to estimate cartilage histologic grades and proteoglycan content in ex vivo arthroscopy using near-infrared spectroscopy (NIRS).

Methods

In this experimental study, arthroscopic NIR spectral measurements were performed on both knees of 9 human cadavers, followed by osteochondral block extraction and in vitro measurements: reacquisition of spectra and reference measurements (proteoglycan content, and three histologic scores). A hybrid model, combining principal component analysis and linear mixed-effects model (PCA-LME), was trained for each reference to investigate its relationship with in vitro NIR spectra. The performance of the PCA-LME model was validated with ex vivo spectra before and after the exclusion of outlying spectra. Model performance was evaluated based on Spearman rank correlation (ρ) and root-mean-square error (RMSE).

Results

The PCA-LME models performed well (independent test: average ρ = 0.668, RMSE = 0.892, P < .001) in the prediction of the reference measurements based on in vitro data. The performance on ex vivo arthroscopic data was poorer but improved substantially after outlier exclusion (independent test: average ρ = 0.462 to 0.614, RMSE = 1.078 to 0.950, P = .019 to .008).

Conclusions

NIRS is capable of nondestructive evaluation of cartilage integrity (i.e., histologic scores and proteoglycan content) under similar conditions as in clinical arthroscopy.

Clinical Relevance

There are clear clinical benefits to the accurate assessment of cartilage lesions in arthroscopy. Visual grading is the current standard of care. However, optical techniques, such as NIRS, may provide a more objective assessment of cartilage damage.