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BACKGROUND AND AIMS: Double lumen tube (DLT) insertion for isolation of lung during thoracic surgery is challenging and is associated with considerable airway trauma. The advent of video laryngoscopy has revolutionized the management of difficult airway. Use of video laryngoscopy may reduce the time to intubate for DLTs even in patients with normal airway. MATERIAL AND METHODS: A total of 87 ASA 1-3 adults, scheduled to undergo elective thoracotomy, requiring a DLT were randomly allocated to videolaryngoscope (CMAC) arm or Macintosh laryngoscope arm. It was on open label study, and only the patient was blinded. The primary objective of this study was to compare the mean time taken for DLT intubation with CMAC (Mac 3) and Macintosh laryngoscope blade and the secondary objectives included the hemodynamic response to intubation, the level of difficulty using the intubation difficulty scale (IDS), and complications associated with intubation. Data was analysed using the statistical software SPSS (version 18.0). RESULTS: The time taken for intubation was not significantly different (42.8 ± 14.8 s for CMAC and 42.5 ± 11.5 s for Macintosh laryngoscope P -0.908). The CMAC video laryngoscope was associated with an improved laryngoscopy grade (Grade I in 81.8% with CMAC and in 46.5% with Macintosh), less pressure applied on the tongue, and less external laryngeal pressure required. Hemodynamic responses to intubation were similar in both groups. CONCLUSION: Macintosh blade is as good as CMAC (mac 3) blade to facilitate DLT intubation in adult patients with no anticipated airway difficulty, however CMAC was superior as it offers better laryngoscopic view, needed less force, and fewer external laryngeal manipulations.
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CONTEXT: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). AIMS: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. SETTINGS AND DESIGN: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. SUBJECTS AND METHODS: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). RESULTS: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51-0.54). CONCLUSIONS: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.
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OBJECTIVES: Oral visual screening can avert oral cancer mortality. Oral premalignancies are currently considered as separate, individual disorders. The objective was to develop a simple clinical screening tool to detect oral premalignancies in general health care setting and validate diagnostic accuracy against Oral Medicine specialist examination as gold standard. MATERIALS AND METHODS: All steps in development of new tool, from item generation to item selection and item reduction were carried out. Item generation was done using qualitative methods. After pre-testing and piloting, junior dental house surgeon administered the refined tool, to patients attending dental outpatient department (n=255). Subsequent evaluation by an Oral Medicine specialist using consensus clinical criteria, detected 59 screen positive cases. Each case was recoded as per scores assigned by binary logistic regression coefficients and total score computed. The Receiver Operator Characteristic (ROC) was performed against specialist examination as gold standard. Performance ability and reliability coefficient of final tool was assessed. A simple score was assigned to indicate stage and clinical severity. RESULTS: Screening Tool for Oral Premalignancies (STOP) has sensitivity 96.6%, specificity 99.0% and accuracy 98.4% with reliability coefficient 0.874. Scores detect clinical signs and staging reflect clinical severity alerts. CONCLUSION: Oral Potentially Malignant Disorders need to be evaluated as single entity, under a common umbrella - Mucosal Disorders with Oral Epithelial Dysplasia risk (MD-OEDr). STOP is a simple tool for opportunistic general health care screening of MD-OEDr.