Objectives
This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine ...learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction.
Methods
Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck.
Results
Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy.
Conclusions
Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone.
Key Points
• Texture features of HNSCC tumor are predictive of nodal status.
• Multi-energy texture analysis is superior to analysis of datasets at a single energy.
• Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.
Spectral computed tomography (CT) or dual-energy CT (DECT) is an advanced form of CT with increasing applications in head and neck radiology. This article provides an overview of the DECT technique ...and reviews current applications for the evaluation of neck pathology, focusing on oncologic applications. Included are an overview of the basic underlying principles and approaches for DECT scan acquisition and material characterization; a discussion of various DECT reconstructions and a brief overview of practical issues pertaining to DECT implementation, including those related to workflow impact of DECT; and a discussion of various applications of DECT for the evaluation of the neck, especially in oncology.
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.
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.
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.
Machine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy.
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There is increasing use of dual-energy computed tomography (DECT) for the evaluation of head and neck pathologic entities. Optimal DECT utilization requires familiarity with the appearance of normal ...tissues variants, and pathologic entities on different DECT reconstructions that may be used in clinical practice. The purpose of this article is to provide a practical, pictorial review of the appearance of normal anatomic structures and different neoplastic and nonneoplastic head and neck pathologic entities on commonly used DECT reconstructions.
Accidental breach of the vertebral artery (VA) during the performance of cervical pain blocks can result in significant morbidity. Whereas anatomical variations have been described for the foraminal ...(V2) segment of the VA, those involving its V3 portion (between the C2 transverse process and dura) have not been investigated and may be of importance for procedures targeting the third occipital nerve or the lateral atlantoaxial joint.
Five hundred computed tomography angiograms of the neck performed in patients older than 50 years for the management of cerebrovascular accident or cervical trauma (between January 2010 and May 2016) were retrospectively and independently reviewed by 2 neuroradiologists. Courses of the VA in relation to the lateral aspect of the C2/C3 joint and the posterior surface of the C1/C2 joint were examined. For the latter, any medial encroachment of the VA (or one of its branches) was noted. The presence of a VA loop between C1 and C2 and its distance from the upper border of the superior articular process (SAP) of C3 were also recorded. If the VA loop coursed posteriorly, its position in relation to 6 fields found on the lateral aspects of the articular pillars of C2 and C3 was tabulated.
At the C1/C2 level, the VA coursed medially over the lateral quarter of the dorsal joint surface in 1% of subjects (0.6% and 0.4% on the left and right sides, respectively; P = 0.998). A VA loop originating between C1 and C2 was found to travel posteroinferiorly over the anterolateral aspect of the inferior articular pillar of C2 in 55.5% of patients on the left and 41.9% on the right side (P < 0.001), as well as over the SAP of C3 in 0.4% of subjects. When present in the quadrant immediately cephalad to the C3 SAP, VA loops coursed within 2.0 ± 1.5 and 3.3 ± 2.5 mm on the left and right sides, respectively, of its superior aspect (P < 0.001).
The VA commonly travels adjacent to areas targeted by third occipital nerve procedures and more rarely over the access point for lateral atlantoaxial joint injections. Modifications to existing techniques may reduce the risk of accidental VA breach.
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 (
-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.
There is increasing use and popularity of dual-energy computed tomography (DECT) in many subspecialties in radiology. This article reviews the practical workflow implications of routine DECT scanning ...based on the experience at a single institution where a large percentage of elective neck CTs are acquired in DECT mode. The article reviews factors both on the production (technologist) and on the interpretation (radiologist) side, focusing on challenges posed and potential solutions for seamless workflow implementation.
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.
Background
Proton pump inhibitors (PPIs) are one of the most frequently used drugs worldwide. Previous research has shown that they could increase the risk of fracture and interfere with the fracture ...healing process. In this study, we analyzed the effect of PPIs on the risk of fracture non-union in patients with femoral and tibial shaft fractures.
Methods
A case–control study was conducted at our institution, including a total of 254 patients who underwent fixation surgery for a femoral or tibial shaft fracture between January 2012 and December 2017. We defined cases as patients who experienced a delayed union (case group A;
n
= 44), or non-union (cases group B;
n
= 12). Cases were matched by age, sex, and fractured bone, to 144 controls who did not experience delayed fracture union and did not require further procedures. A conditional logistic regression analysis was performed adjusted to potential confounders, and to the proportion of days covered (PDC) with PPIs.
Results
Adjusted ORs (95% CI) for undergoing a nail dynamization following a tibial or femoral shaft fracture were 1.38 (0.70–2.65) for any use PPIs. Patients with a longer PPI treatment courses (PDC ≥ 0.5) had an adjusted OR of 1.86 (0.70–4.76) for undergoing nail dynamization when compared with controls. Contrastingly, patients with a PDC < 0.5 had an adjusted OR of 1.03 (0.43–2.48). The adjusted OR (95% CI) for undergoing additional surgical procedures due to non-union was 4.5 (0.62–32.8) for any use of PPIs, and 12.3 (1.9–81.0) in patients with a PDC ≥ 0.5.
Conclusions
A prolonged use of PPIs use was associated with a higher risk of fracture non-union in tibial and femoral shaft fractures.
Background Elastofibroma dorsi (ED) is a benign soft tissue tumor that classically presents as an ill-defined mass at the inferior pole of the scapula. Several studies have indicated the benefits of ...using magnetic resonance imaging (MRI) to identify ED. In this study, we calculate the sensitivity and positive predictive value (PPV) of MRI in the diagnosis of ED using histopathology as the gold standard diagnostic method. Clinical characteristics of ED and radiologic features of MRI as well as treatment options are discussed. Materials and methods A systematic retrospective review was performed of all ED patients treated in our center between 1999 and 2009. MRI and histopathology samples were performed in all cases. The MRI sensitivity and PPV in the diagnosis of ED were calculated. Results A total of 15 patients who were treated within the study period were reviewed; of these, were 14 (3 men, 11 women) true ED cases. MRI scan results matched the histopathology in 14 of 15 patients; 1 false-positive patient was observed, and no false-negative patients (negative MRI and positive histopathology) were noted. The PPV and sensitivity of MRI scan in the diagnosis of ED were 93.3% (95% confidence interval, 68.0%-100.0%) and 100% (95% confidence interval, 75.2%-100.0%) respectively. Conclusions MRI is a useful tool for assessment of ED and can potentially help avoid the need for unnecessary biopsy and surgery, especially in the asymptomatic patient.
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