Support Tools in the Differential Diagnosis of Salivary Gland Tumors through Inflammatory Biomarkers and Radiomics Metrics: A Preliminary Study

Committeri, Umberto; Barone, Simona; Salzano, Giovanni; Arena, Antonio; Borriello, Gerardo; Giovacchini, Francesco; Fusco, Roberta; Vaira, Luigi Angelo; Scarpa, Aldo; Abbate, Vincenzo; Ugga, Lorenzo; Piombino, Pasquale; Ionna, Franco; Califano, Luigi; Orabona, Giovanni Dell’Aversana

doi:10.3390/cancers15061876

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Urbanowicz et al reported that SIRI levels higher than 1.22 had a higher probability of single and complex coronary disease [ 19 ]. Committeri et al used inflammatory markers to differentiate Warthin tumors from pleomorphic adenoma and malignant neoplasms [ 20 ]. They reported SIRI with an accuracy of 0.83 and significant differences in the univariate Kruskal–Wallis test.…”

Section: Discussionmentioning

confidence: 99%

Should we consider Systemic Inflammatory Response Index (SIRI) as a new diagnostic marker for rectal cancer?

Yazici,

Eren Kayaci,

Sevindi

et al. 2024

Discov Onc

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Purpose The Systemic Inflammatory Response Index (SIRI), which depends on peripheral neutrophil, monocyte, and lymphocyte count, was found to be an effective prognostic indicator for various malignancies. In this study, we aimed to investigate the diagnostic value and the prognostic impact of SIRI on rectal cancer patients. Method The medical records of patients underwent sphincter-sparing rectal cancer surgery at general surgery between 2017 and 2022 were examined retrospectively. Patient demographics, operation types, neoadjuvant chemo/radiotherapies, pathological results, and complications were recorded. A total number of 99 patients who operated with diagnoses other than cancer were conducted as a control group. SIRI was calculated from preoperative peripheral blood samples’ neutrophil, lymphocyte, and monocyte count. The optimal cut-off value for SIRI was found to be 1.38. The clinicopathological outcomes and Overall Survival (OS) were analyzed under two groups according to the SIRI values lower or higher than 1.38. Results The number of eligible patients was 104. The median age of the entire cohort was 62 (31–89). The median follow-up time was 33 (1–62) months. The median SIRI value in the study group was significantly higher compared with the control group. The study group was examined under two groups: SIRI 1.38 and SIRI > 1.38. The male gender was significantly more frequent in the high SIRI group. The remaining patient demographics and operation types were similar between the groups. The pathological outcomes were similar between the two groups. Overall Survival rate was better in the low SIRI group than those higher. The higher group had significantly higher complication rates than the lower SIRI group (p: 0.004). Conclusion SIRI may be a valuable diagnostic marker in rectal cancer patients. Higher SIRI levels were also associated with poorer prognosis and increased complication rates. Still, further prospective studies with a larger number of patients are needed.

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Section: Discussionmentioning

confidence: 99%

Should we consider Systemic Inflammatory Response Index (SIRI) as a new diagnostic marker for rectal cancer?

Yazici,

Eren Kayaci,

Sevindi

et al. 2024

Discov Onc

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“…In other fields of medicine, especially in oncology, radiomic analyses using statistical learning methods by analysing ultrasound [53,54], computed tomography [55][56][57], magnetic resonance [58,59], or positron emission tomography [60] images are very useful for differentiating between cancer and inflammation [61].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma

et al. 2023

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Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.

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“…Several diagnostic methods are currently studied for establishing diagnosis and guiding the treatment strategy for salivary gland tumors. Morphological parameters like inflammatory biomarkers and radiomics extracted from imaging techniques or from histopathological slides are analyzed as possible targets in establishing diagnosis [ 61 , 65 ].…”

Section: Artificial Intelligence Algorithms In Salivary Gland Tumor D...mentioning

confidence: 99%

Current Developments in Diagnosis of Salivary Gland Tumors: From Structure to Artificial Intelligence

Faur,

Buzaș,

Lăzărescu

et al. 2024

Life

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Salivary glands tumors are uncommon neoplasms with variable incidence, heterogenous histologies and unpredictable biological behaviour. Most tumors are located in the parotid gland. Benign salivary tumors represent 54–79% of cases and pleomorphic adenoma is frequently diagnosed in this group. Salivary glands malignant tumors that are more commonly diagnosed are adenoid cystic carcinomas and mucoepidermoid carcinomas. Because of their diversity and overlapping features, these tumors require complex methods of evaluation. Diagnostic procedures include imaging techniques combined with clinical examination, fine needle aspiration and histopathological investigation of the excised specimens. This narrative review describes the advances in the diagnosis methods of these unusual tumors—from histomorphology to artificial intelligence algorithms.

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Support Tools in the Differential Diagnosis of Salivary Gland Tumors through Inflammatory Biomarkers and Radiomics Metrics: A Preliminary Study

Cited by 7 publications

References 30 publications

Should we consider Systemic Inflammatory Response Index (SIRI) as a new diagnostic marker for rectal cancer?

Should we consider Systemic Inflammatory Response Index (SIRI) as a new diagnostic marker for rectal cancer?

Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma

Current Developments in Diagnosis of Salivary Gland Tumors: From Structure to Artificial Intelligence

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