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.
Objective: Patients that do not show up for scheduled clinic appointments affect the quality of healthcare provided. This study aimed to recognize the reasons behind missing scheduled appointments and understand possible solutions from the patient's perspective. Method: We included 100 patients that attended the outpatient Medicine clinic in January 2020. Selection criteria were based on missing one or more of the scheduled clinic appointments in the last year. The participants answered a questionnaire to clarify the reasons for missing a scheduled clinic appointment and offer suggestions for a solution. The recruiter, in turn, answered several demographical questions Results: The study showed a statistically significant difference between the no-show rate in females at 60% compared to males at 40% (P = 0.0023). The no show rate was not significantly affected by the day of the week, time of appointment, or the weather. Forgetting about the appointment was the most common cause (36 subjects). Work-related issues were reported in 17 participants, making it the 2 nd most common cause. Not notified about the appointment, Lack of transportation, childcare-related issues, along with other reasons, were less likely reported (Table 2). 11 out of 36 (30%) subjects suggested a reminder text message in their preferred language; meanwhile, 4 others suggested a weekend clinic. Conclusion: The patients should be aware of different appointment reminders options and have the freedom to choose a suitable reminder. Patients should be educated about the importance of calling to cancel the appointment since some of the reasons for no show are unpreventable.
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