Non-tuberculous mycobacterial (NTM) infection is an emerging infectious entity that often presents as lymphadenitis in the pediatric age group. Current practice involves invasive testing and ...excisional biopsy to diagnose NTM lymphadenitis. In this study, we performed a retrospective analysis of 249 lymph nodes selected from 143 CT scans of pediatric patients presenting with lymphadenopathy at the Montreal Children's Hospital between 2005 and 2018. A Random Forest classifier was trained on the ten most discriminative features from a set of 1231 radiomic features. The model classifying nodes as pyogenic, NTM, reactive, or proliferative lymphadenopathy achieved an accuracy of 72%, a precision of 68%, and a recall of 70%. Between NTM and all other causes of lymphadenopathy, the model achieved an area under the curve (AUC) of 89%. Between NTM and pyogenic lymphadenitis, the model achieved an AUC of 90%. Between NTM and the reactive and proliferative lymphadenopathy groups, the model achieved an AUC of 93%. These results indicate that radiomics can achieve a high accuracy for classification of NTM lymphadenitis. Such a non-invasive highly accurate diagnostic approach has the potential to reduce the need for invasive procedures in the pediatric population.
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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•CT-based radiomics with machine learning classifier is able to accurately predict primary refractory Diffuse Large B Cell Lymphomas (DLBCL).•The radiomics model exhibits a better discrimination for ...refractory DLBCL identification compared to available standard clinical criteria.
Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.
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.
Machine Learning Algorithm Validation Maleki, Farhad; Muthukrishnan, Nikesh; Ovens, Katie ...
Neuroimaging clinics of North America,
November 2020, Volume:
30, Issue:
4
Journal Article
Peer reviewed
Open access
The deployment of machine learning (ML) models in the health care domain can increase the speed and accuracy of diagnosis and improve treatment planning and patient care. Translating academic ...research to applications that are deployable in clinical settings requires the ability to generalize and high reproducibility, which are contingent on a rigorous and sound methodology for the development and evaluation of ML models. This article describes the fundamental concepts and processes for ML model evaluation and highlights common workflows. It concludes with a discussion of the requirements for the deployment of ML models in clinical settings.
Multiple applications of dual energy computed tomography (DECT) have been described for the evaluation of disorders in the head and neck, especially in oncology. We review the body of evidence ...suggesting advantages of DECT for the evaluation of the neck compared with conventional single energy computed tomography scans, but the full potential of DECT is still to be realized. There is early evidence suggesting significant advantages of DECT for the extraction of quantitative biomarkers using radiomics and machine learning, representing a new horizon that may enable this technology to reach its full potential.
Introduction
Approximately 15% of diffuse large B-cell lymphomas (DLBCL) do not respond to R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) or equivalent regimen. These ...primary refractory cases (prDLBCL) have a particularly poor survival. There are currently no reliable biomarkers to a priori identify prDLBCL patients and include them in clinical trials, while avoiding needless toxicity from predictably ineffective therapy. In this study, we evaluated the potential for radiomic analysis with machine learning for predicting prDLBCL.
Method
This study included adult patients with prDLBCL from a single institution from 2009 to 2018, who had first-line treatment with an R-CHOP like regimen, had never received systemic treatment for indolent lymphoma, and who had a CT scan at the time of diagnosis. Refractory (R) patients were defined by progression of disease (PD) after completion of at least one cycle, or failure to achieve a complete response (CR) after at least 4 cycles, as per Lugano criteria (Cheson, JCO 2014). Non-refractory (NR) patients were matched 1:1 on sex and R-IPI for the comparison group. Enlarged lymph nodes (≥1.5 cm in greatest diameter) were eligible for evaluation. The 6 largest nodes were selected at each node site (abdomen, chest, axilla and neck) and for each node category (refractory node (RN), partial response (PR) and CR, as per Lugano criteria).
3D Slicer software was used for the delineation of the region of interest (ROI) either for subsequent 2D analysis (largest axial section) or 3D analysis (total node volume). Each node was manually contoured by two independent readers and also was reviewed by an experienced senior oncologic radiologist. A total of 788 and 1218 features were extracted from 2D and 3D regions of interest, respectively, using Pyradiomics open source software.
Two independent machine learning approaches, Random Forests (RF) and Support Vector Machine (SVM), were tested for constructing the prediction models. 70% of cases were randomly assigned to the training set and 30% to the independent testing set. In the node model (NM) each independent node's response to treatment was predicted. In the patient model (PM), groups of nodes per site (abdomen, chest, axilla and neck) were used to predict the overall patient response.
Results
A total of 26 refractory patients were identified with a total 149 nodes (RN=55, PR=20, CR=74) and matched to 26 NR patients for comparison, with a total of 105 CR nodes. Seventeen nodes with significant artifact were excluded from the analysis (7 from NR patients and 10 from R patients).
RF had consistently superior performance compared to SVM and was used for constructing the final prediction models. Furthermore, 2D radiomic analysis had superior performance compared to 3D radiomic analysis. In the independent testing (prediction) set, the mean accuracy between the 2 readers for this model for distinguishing a R from NR patient was 80% (mean sensitivity and specificity, 73% and 88%, respectively). This model was able to predict a R patient (positive predictive value (PPV)) in 100% and 71% of the case, respectively for readers 1 and 2. The area under the ROC curve (AUC) was 0.96 and 0.81 for reader 1 and 2, respectively (Figure 1A).
For performance of the radiomic model for distinguishing individual refractory from responsive nodes, the independent testing set had a mean accuracy of 75% (mean sensitivity, specificity, PPV, and NPV of 80%, 69%, 78%, and 71% respectively). The AUC per reader were 0.82 and 0.85 (Figure 1B).
Conclusion
We demonstrate that the use of CT radiomic analysis with machine learning for identifying a priori primary refractory DLBCL patients is feasible. These models provide a relatively high prediction accuracy, which currently cannot be done in the clinical setting based on standard, largely qualitative, imaging characteristics.
The main limitations of our study include small patient numbers in this pilot study and exclusion of extranodal sites. The next step for this project would be to evaluate this approach in a larger cohort that includes a second independent institution. CT-based radiomics is promising and should be further explored to achieve this unmet need for predicting prDLBCL prior to therapy initiation.
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Forghani:GE Healthcare: Consultancy, Honoraria, Research Funding; 4Intel Inc: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: Founder. Reinhold:FRQS: Other: FRQS Grant. Assouline:Pfizer: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Abbvie: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria.
Objective:
Major postoperative adverse events (MPAEs) following head and neck surgery are not infrequent and lead to significant morbidity. The objective of this study was to ascertain which factors ...are most predictive of MPAEs in patients undergoing head and neck surgery.
Methods:
A cohort study was carried out based on data from patients registered in the National Surgical Quality Improvement Program (NSQIP) from 2006 to 2018. All patients undergoing non-ambulatory head and neck surgery based on Current Procedural Terminology codes were included. Perioperative factors were evaluated to predict MPAEs within 30-days of surgery. Age was classified as both a continuous and categorical variable. Retained factors were classified by attributable fraction and C-statistic. Multivariate regression and supervised machine learning models were used to quantify the contribution of age as a predictor of MPAEs.
Results:
A total of 43 701 operations were analyzed with 5106 (11.7%) MPAEs. The results of supervised machine learning indicated that prolonged surgeries, anemia, free tissue transfer, weight loss, wound classification, hypoalbuminemia, wound infection, tracheotomy (concurrent with index head and neck surgery), American Society of Anesthesia (ASA) class, and sex as most predictive of MPAEs. On multivariate regression, ASA class (21.3%), hypertension on medication (15.8%), prolonged operative time (15.3%), sex (13.1%), preoperative anemia (12.8%), and free tissue transfer (9%) had the largest attributable fractions associated with MPAEs. Age was independently associated with MPAEs with an attributable fraction ranging from 0.6% to 4.3% with poor predictive ability (C-statistic 0.60).
Conclusion:
Surgical, comorbid, and frailty-related factors were most predictive of short-term MPAEs following head and neck surgery. Age alone contributed a small attributable fraction and poor prediction of MPAEs.
Level of evidence:
3
In the domain of medical image processing, medical device manufacturers protect their intellectual property in many cases by shipping only compiled software, i.e. binary code which can be executed ...but is difficult to be understood by a potential attacker. In this paper, we investigate how well this procedure is able to protect image processing algorithms. In particular, we investigate whether the computation of mono-energetic images and iodine maps from dual energy CT data can be reverse-engineered by machine learning methods. Our results indicate that both can be approximated using only one single slice image as training data at a very high accuracy with structural similarity greater than 0.98 in all investigated cases.
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