Utility of CT and MRI in assessment of mandibular involvement in oral cavity cancer

Nae, Andreea; O’Leary, Gerard; Feeley, Linda; Fives, Cassie; Fitzgerald, Brendan; Chiriac, Elena; Sheahan, Patrick

doi:10.1016/j.wjorl.2019.02.001

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…MRI, capable of detecting alterations in the mandible’s bone marrow signal, stands out as one of the most effective morphological imaging tools for identifying bone invasion in OSCC. Its high sensitivity and negative predictive value have been well-documented [5,22]. MRI images with positive findings for bone invasion can be instrumental in ruling out bone marrow invasion when CT scans are negative [23].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of bone marrow invasion on the machine learning of 18F-FDG PET texture analysis in lower gingival squamous cell carcinoma

Fukushima,

Suzuki,

Kim

et al. 2024

Nuclear Medicine Communications

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Objectives Lower gingival squamous cell carcinoma (LGSCC) has the potential to invade the alveolar bone. Traditionally, the diagnosis of LGSCC relied on morphological imaging, but inconsistencies between these assessments and surgical findings have been observed. This study aimed to assess the correlation between LGSCC bone marrow invasion and PET texture features and to enhance diagnostic accuracy by using machine learning. Methods A retrospective analysis of 159 LGSCC patients with pretreatment 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) examination from 2009 to 2017 was performed. We extracted radiomic features from the PET images, focusing on pathologic bone marrow invasion detection. Extracted features underwent the least absolute shrinkage and selection operator algorithm-based selection and were then used for machine learning via the XGBoost package to distinguish bone marrow invasion presence. Receiver operating characteristic curve analysis was performed. Results From the 159 patients, 88 qualified for further analysis (59 men; average age, 69.2 years), and pathologic bone marrow invasion was identified in 69 (78%) of these patients. Three significant radiological features were identified: Gray level co-occurrence matrix_Correlation, INTENSITY-BASED_IntensityInterquartileRange, and MORPHOLOGICAL_SurfaceToVolumeRatio. An XGBoost machine-learning model, using PET radiomic features to detect bone marrow invasion, yielded an area under the curve value of 0.83. Conclusion Our findings highlighted the potential of 18F-FDG PET radiomic features, combined with machine learning, as a promising avenue for improving LGSCC diagnosis and treatment. Using 18F-FDG PET texture features may provide a robust and accurate method for determining the presence or absence of bone marrow invasion in LGSCC patients.

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Section: Discussionmentioning

confidence: 99%

Evaluation of bone marrow invasion on the machine learning of 18F-FDG PET texture analysis in lower gingival squamous cell carcinoma

Fukushima,

Suzuki,

Kim

et al. 2024

Nuclear Medicine Communications

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“…Pre-operative imaging is important. There are some instrumental exams that should be practiced to understand the size and the stage of the pathology and to define the surgical and/or the medical therapy (Nae et al , 2019). Sometimes of these exams may require long times to be practiced because long waiting lists in the hospitals.…”

Section: Methodsmentioning

confidence: 99%

Improving performance of the hospitalization process by applying the principles of Lean Thinking

Fiorillo

Sorrentino

Scala

et al. 2021

TQM

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PurposeThe goal was to improve the quality of the hospitalization process and the management of patients, allowing the reduction of costs and the minimization of the preoperative Length of Hospital Stay (LOS).Design/methodology/approachThe methodology used to improve the quality of the hospitalization process and patient management was Lean Thinking. Therefore, the Lean tools (Value stream map and Ishikawa diagram) were used to identify waste and inefficiencies, improving the process with the implementation of corrective actions. The data was collected through personal observations, patient interviews, brainstorming and from printed medical records of 151 patients undergoing oral cancer surgery in the period from 2006 to 2018.FindingsThe authors identified, through Value Stream Map, waste and inefficiencies during preoperative activities, consequently influencing preoperative LOS, considered the best performance indicator. The main causes were identified through the Ishikawa diagram, allowing reflection on possible solutions. The main corrective action was the introduction of the pre-hospitalization service. A comparative statistical analysis showed the significance of the solutions implemented. The average preoperative LOS decreased from 4.90 to 3.80 days (−22.40%) with a p-value of 0.001.Originality/valueThe methodology allowed to highlight the improvement of the patient hospitalization process with the introduction of the pre-hospitalization service. Therefore, by adopting the culture of continuous improvement, the flow of hospitalization was redrawn. The benefits of the solutions implemented are addressed to the patient in terms of lower LOS and greater service satisfaction and to the hospital for lower patient management costs and improved process quality. This article will be useful for those who need examples on how to apply Lean tools in healthcare.

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“…For detecting mandibular invasion, Vidiri et al reported 94% sensitivity and 60% specificity [114]. A subsequent study by Nae et al demonstrated that combining MRI with CT improved diagnostic efficacy (100% sensitivity and 72% specificity) [115]. However, false negatives were high and attributed to minor cortical inflammatory changes unrelated to the tumor and improper sampling of cortical bone.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Current and emerging techniques for oral cancer screening and diagnosis: a review

Bisht

Mishra²,

Yadav

et al. 2021

Prog. Biomed. Eng.

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Oral cancer causes over 350 000 deaths annually worldwide. Although most cases are in Asia, the incidence of oral cancer is rising across the world. Despite recent advances in screening methods, oral cancer remains a significant cause of mortality and morbidity. The 5-year survival rate (50%–60%) has not improved over the past several decades. Early detection and accurate diagnosis of the disease can improve the survival rate and patients’ quality of life. This article provides a topical review of current and emerging techniques for screening and diagnosing oral cancer. Currently available technologies have only been moderately useful towards identifying oral cancer early, motivating the development of novel approaches to address this goal. In this article, we provide an overview of adjunctive screening aids, including biofluid (saliva and serum) diagnostics, vital staining, brush biopsy, chemiluminescence, and tissue autofluorescence. Furthermore, we discuss diagnostic imaging modalities, such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound (including traditional B-mode imaging, color Doppler, and elastography), photoacoustics imaging, and optical coherence tomography, and artificial intelligence-based methods, which are either being used clinically or are under development for oral cancer staging. The physical and biological basis underpinning each technique are discussed, along with their advantages and limitations in the technological and clinical context. The review concludes with a discussion of the future perspectives in this rapidly evolving field.

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Utility of CT and MRI in assessment of mandibular involvement in oral cavity cancer

Cited by 12 publications

References 20 publications

Evaluation of bone marrow invasion on the machine learning of 18F-FDG PET texture analysis in lower gingival squamous cell carcinoma

Evaluation of bone marrow invasion on the machine learning of 18F-FDG PET texture analysis in lower gingival squamous cell carcinoma

Improving performance of the hospitalization process by applying the principles of Lean Thinking

Current and emerging techniques for oral cancer screening and diagnosis: a review

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