A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma

Exploring genes associated with metabolic dysfunction as therapeutic targets for head and neck cancers: a novel strategy

Evidence suggests a potential link between metabolic dysfunction and head and neck cancer (HNC). This study investigates the potential causal relationships between metabolic dysfunction and HNC using genetic data. While no significant causal associations were identified between metabolic indicators and HNC risk, the research revealed that inhibition of certain genes could reduce cancer risk. Specifically, inhibiting sodium/glucose cotransporter 2 (SLC5A2) was associated with a decreased risk of HNC and oropharyngeal cancer (OPC), while ATP-sensitive inward rectifier potassium channel 11 inhibition was linked to a reduced risk of oral cavity cancer. Additionally, inhibiting SLC5A1 and voltage-dependent L-type calcium channel subunit beta-2 showed a connection to lower OPC risk. These findings suggest that targeting these genes could offer promising therapeutic strategies for preventing and treating HNC, as well as improving both preoperative and postoperative management in affected patients.

Clinical application of CT-based radiomics model in differentiation between laryngeal squamous cell carcinoma and squamous cell hyperplasia

Objective

To evaluate the clinical application of the CT-based radiomics prediction model for discriminating SCC and SCH.

Methods

A total of 254 clinical samples were selected from 291 patients with larynx-occupying lesions who underwent primary surgery. All lesions were validated via histopathological examination at The Second Hospital of Jilin University between June 2004 and December 2019. All patients were randomly allocated to the training (n = 177) and validation (n = 77) cohorts. After the acquisition of CT images, manual 3D tumor segmentation was performed using the CT images of the arterial, venous, and non-contrast phases via ITK-SNAP software. Subsequently, radiomics features were extracted using A.K. software. Based on the above features, three different diagnostic models (CTN, CTA+CTV, and CTN+CTA+CTV) were constructed to classify squamous cell carcinoma (SCC) and squamous cell hyperplasia (SCH). Additionally, receiver operating characteristic (ROC) and decision curve analysis (DCA) curves were measured to evaluate the diagnostic characteristics and clinical safety of the proposed three prognostic models.

Results

In the radiomic prediction Model 1 (CTN), the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the training cohorts in differentiating SCC and SCH were 0.883, 0.785, 0.645, 1.000, 1.000, and 0.648, while in the testing cohorts, these values were 0.852, 0.792, 0.66, 1.000, 1.000, and 0.652, respectively. In the radiomic prediction Model 2 (CTA+CTV), the AUC, accuracy, sensitivity, specificity, PPV, and NPV values of the training cohorts were 0.965, 0.91, 0.916, 0.9, 0.933, and 0.875, respectively, while in the testing cohorts, the corresponding values were 0.902, 0.805, 0.851, 0.733, 0.833, and 0.759, respectively. In the radiomic prediction Model 3(CTN+CTA+CTV), the AUC, accuracy, sensitivity, specificity, PPV, and NPV values of the training cohorts were 0.985, 0.944, 0.953, 0.929, 0.953, and 0.929, while in the testing cohorts, the corresponding values were 0.965, 0.857, 0.894, 0.8, 0.875, and 0.828, respectively.

Conclusion

The radiomic prediction Model 3, based on the arterial-venous-plain combined scan phase of CT, achieved promising diagnostic performance, expected to be regarded as a preoperative imaging tool in classifying SCC and SCH to guide clinicians to develop individualized treatment programs.