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Purpose To determine whether machine learning assisted-texture analysis of multi-energy virtual monochromatic image (VMI) datasets from dual-energy CT (DECT) can be used to differentiate metastatic head and neck squamous cell carcinoma (HNSCC) lymph nodes from lymphoma, inflammatory, or normal lymph nodes. Materials and methods A retrospective evaluation of 412 cervical nodes from 5 different patient groups (50 patients in total) having undergone DECT of the neck between 2013 and 2015 was performed: (1) HNSCC with pathology proven metastatic adenopathy, (2) HNSCC with pathology proven benign nodes (controls for (1)), (3) lymphoma, (4) inflammatory, and (5) normal nodes (controls for (3) and (4)). Texture analysis was performed with TexRAD® software using two independent sets of contours to assess the impact of inter-rater variation. Two machine learning algorithms (Random Forests (RF) and Gradient Boosting Machine (GBM)) were used with independent training and testing sets and determination of accuracy, sensitivity, specificity, PPV, NPV, and AUC. Results In the independent testing (prediction) sets, the accuracy for distinguishing different groups of pathologic nodes or normal nodes ranged between 80 and 95%. The models generated using texture data extracted from the independent contour sets had substantial to almost perfect agreement. The accuracy, sensitivity, specificity, PPV, and NPV for correctly classifying a lymph node as malignant (i.e. metastatic HNSCC or lymphoma) versus benign were 92%, 91%, 93%, 95%, 87%, respectively. Conclusion Machine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy.

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Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

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Background: Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. Hypothesis: It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. Results: Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (–1.7° ± 4.8° vs –4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. Conclusion: A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.

Previous work has shown that the morphology of the knee joint is associated with the risk of primary anterior cruciate ligament (ACL) injury. The objective of this study is to analyze the effect of the meniscal height, anteroposterior distance of the lateral tibial plateau, and other morphological features of the knee joint on risk of ACL reconstruction failure. A nested case–control study was conducted on patients who underwent an ACL reconstruction surgery during the period between 2008 and 2015. Cases were individuals who failed surgery during the study period. Controls were patients who underwent primary ACL reconstruction surgery successfully during the study period. They were matched by age (±2 years), gender, surgeon, and follow-up time (±1 year). A morphological analysis of the knees was then performed using the preoperative magnetic resonance imaging scans. The anteroposterior distance of the medial and lateral tibial plateaus was measured on the T2 axial cuts. The nonweightbearing maximum height of the posterior horn of both menisci was measured on the T1 sagittal scans. Measurements of the medial and lateral tibial slope and meniscal slope were then taken from the sagittal T1 scans passing through the center of the medial and lateral tibial plateau. A binary logistic regression analysis was done to calculate crude and adjusted odds ratios (ORs) estimates. Thirty-four cases who underwent ACL revision surgery were selected and were matched with 68 controls. Cases had a lower lateral meniscal height (6.39 ± 1.2 vs. 7.02 ± 0.9, p = 0.008, power = 84.4%). No differences were found between the two groups regarding the bone slope of the lateral compartment (6.19 ± 4.8 vs. 6.92 ± 5.8, p = 0.552), the lateral meniscal slope (–0.28 ± 5.8 vs. –1.03 ± 4.7, p = 0.509), and the anteroposterior distance of the lateral tibial plateau (37.1 ± 5.4 vs. 35.6 ± 4, p = 0.165). In addition, no differences were found in the medial meniscus height between cases and controls (5.58 ± 1.2 vs. 5.81 ± 1.2, respectively, p = 0.394). There were also no differences between cases and controls involving the medial bone slope, medial meniscal slope, or anterior posterior distance of the medial tibial plateau. Female patients had a higher medial (4.8 degrees ± 3.2 vs. 3.3 ± 4.1, p = 0.047) and lateral (8.1 degrees ± 5.1 vs. 5.6 degrees  ± 5.6, p = 0.031) tibial bone slope, and a lower medial (5.3 mm ± 1.0 vs. 6.1 mm ± 1.2, p = 0.001) and lateral (6.6 ± 1.0 vs. 7.0 ± 1.2, p = 0.035) meniscus height, and medial (4.3 ± 0.4 vs. 4.8 ± 0.4, p =0.000) and lateral (3.3 ± 0.3 vs. 3.9 ± 0.4, p = 0.000) anteroposterior distance than males, respectively.The adjusted OR of suffering an ACL reconstruction failure compared to controls was 5.1 (95% confidence interval [CI]: 1.7–14.9, p = 0.003) for patients who had a lateral meniscus height under 6.0 mm. The adjusted OR of suffering an ACL reconstruction failure was 2.4 (95% CI: 1.0–7.7, p = 0.01) for patients who had an anteroposterior distance above 35.0 mm. Patients with a lateral meniscal height under 6.0 mm have a 5.1-fold risk of suffering an ACL reconstruction failure compared to individuals who have a lateral meniscal height above 6.0 mm. Patients with a higher anteroposterior distance of the lateral tibial plateau also have a higher risk of ACL reconstruction failure.