Evaluating Observer Reliability and Diagnostic Accuracy of CT-LEFAT Criteria for Post-Treatment Head and Neck Lymphedema: A Prospective Blinded Comparative Analysis of Oncologist Human Inter-Rater Performance
MD Anderson Head and Neck Cancer Symptom Working Group, ; West, N. A.; Kamel, S.; Kaffey, Z.; Dede, C.; Mulder, S. L.; El-Habashy, D. M.; Neuberger, R.; Naser, M. A.; Frank, S. J.; Mao, S.; McMillan, H.; Smith, B.; Rosenthal, D.; Lai, S. Y.; Hutcheson, K. A.; Moreno, A. C.; Fuller, C. D.
Show abstract
BackgroundRadiation-associated lymphedema and fibrosis (LEF) is a significant toxicity following radiation therapy (RT) for head and neck cancer (HNC) patients. Recently, the CT Lymphedema and Fibrosis Assessment Tool (CT-LEFAT) was developed to standardize LEF diagnosis through fat stranding visualized on CT. This study aims to evaluate the inter-observer reliability and diagnostic accuracy of the CT-LEFAT criteria. Materials and MethodsThis study retrospectively evaluated 26 HNC patients treated with RT that received a minimum of two contrast-enhanced CT scans. Qualitative review was conducted by five physician raters to assess the fat stranding observed on CT according to the CT-LEFAT criteria. Fleiss kappa analysis was used to assess the inter- and intra-rater reliability, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) analysis was used to evaluate diagnostic accuracy. ResultsThe inter-rater reliability across the six CT-LEFAT regions generally indicated a slight to fair agreement across all raters (0.04 [≤] kappa [≤] 0.36). Intra-observer agreement was generally fair to moderate (overall kappa=0.44). The ROC AUC analysis varied based on aggregation method used (0.60 [≤] average AUC [≤] 0.70). ConclusionThis specific use-case evaluating CT-LEFAT criteria displays limited performance. This suggests that additional materials, such as further training, refinement of imaging methods, or other processes may be required before achieving clinically-ready diagnostic performance of LEF diagnosis.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Comprehensive Quantitative Evaluation of Inter-observer Delineation Performance of MR-guided Delineation of Oropharyngeal Gross Tumor Volumes and High-risk Clinical Target Therapy: An R-IDEAL Stage 0 Prospective Study 96%
- Contralateral Neck Recurrence Rates After Ipsilateral Neck Adjuvant Radiation in Head and Neck Carcinomas with a Pathologically Negative Contralateral Neck 94%
- Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study 94%
Similar papers in this journal
- Dosimetric Analysis of Fast Forward Breast Radiotherapy Using 3D-CRT with Deep Inspiration Breath Hold(DIBH) 91%
- Benchmarking Deep Learning-based Image Retrieval of Oral Tumor Histology 89%
- SARS-CoV-2 antibody seroprevalence in cancer patients on systemic antineoplastic treatment in the first wave of the COVID-19 pandemic in Portugal 89%
Similar papers in this journal
- Auto-Detection and Segmentation of Involved Lymph Nodes in HPV-Associated Oropharyngeal Cancer Using a Convolutional Deep Learning Neural Network 95%
- Detection of Alteration in Carotid Artery Volumetry Using Standard-of-care Computed Tomography Surveillance Scans Following Unilateral Radiation Therapy for Early-stage Tonsillar Squamous Cell Carcinoma Survivors: A Cross-Sectional Internally-Matched Carotid Isodose Analysis 94%
- Personalized volume-deescalated elective nodal irradiation in oropharyngeal squamous cell carcinoma (DeEscO): a study protocol 93%
Similar papers in this journal
- Establishment and validation of pre-therapy cervical vertebrae muscle quantification as a prognostic marker of sarcopenia in head and neck patients receiving definitive cancer surgery 92%
- Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning 92%
- Segmentation stability of human head and neck medical images for radiotherapy applications under de-identification conditions: benchmarking for data sharing and artificial intelligence use-cases 92%
Similar papers in this journal
- Large language models to help appeal denied radiotherapy services 93%
- Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA 92%
- Use of natural language understanding to facilitate surgical de-escalation of axillary staging in patients with breast cancer 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.