Integrating DOI in T classification improves the predictive performance of laryngeal cancer staging

Refining prognostic subcategories in intermediate-advanced glottic cancer: A multicentric study on 637 patients treated by transoral laser microsurgery

BACKGROUND

The current TNM staging system does not fully address the variations in glottic squamous cell carcinoma (SCC) extension and subsites involvement, especially if treated with transoral laser microsurgery (TOLMS). This study aims to evaluate the oncologic outcomes after TOLMS in intermediate-advanced glottic SCC, stratified by prognostic subcategories based on anatomical tumor extension.

METHODS

This retrospective multicentric study analyzed 637 previously untreated patients with pT2-T3 glottic SCC treated by TOLMS following the same policies at four tertiary European centers. Patients were stratified into 5 subcategories (III, IV, Va, Vb, VI) based on three-dimensional local tumor extension, a refined definition of the previous classification proposed by Piazza et al. RESULTS: Out of 637 patients, 453 (71 %) were pT2, and 184 (29 %) pT3. The 5-year disease-specific survival for the entire cohort was 91 %, LCL 81 %, and LP 87 %. Subcategories Va (anterior paraglottic space [PGS] involvement) and Vb (posterior PGS involvement) showed significantly poorer disease-specific survival (Va: 91 %, Vb: 80 %) and local control with laser alone (Va: 76 %, Vb: 68 %) compared to subcategories III (tumors extending superficially to the supra- and/or subglottis) and IV (tumors infiltrating the vocal muscle). Tumors with posterior PGS involvement demonstrated the highest risk of local recurrence and total laryngectomy (HR: 3.70).

CONCLUSION

TOLMS is a viable treatment option for T2-T3 glottic SCC, offering high rates of laryngeal preservation and favorable oncologic outcomes in well-selected patients. Stratification based on tumor subsites involvement provides critical prognostic insights, with posterior PGS invasion serving as a key risk factor for poorer outcomes.

The impact of histopathology on prognosis of squamous cell carcinoma of the larynx: can we do better?

Despite decades of progress, laryngeal squamous cell carcinoma (SCC) is still associated with significant morbidity and mortality worldwide. Additional biomarkers are needed to apply precision medicine and predict the clinical course. We reviewed and summarised routinely reported histopathologic features (e.g. subtypes of laryngeal SCC) along with promising potential biomarkers not yet routinely assessed using international guidelines. These include extra- vs intratumoural vascular and perineural invasion, tumour budding, depth of invasion, and tumour-infiltrating lymphocytes. We also address the problem of specimen quality and type (open approach vs endoscopic surgery) and the related limitations. High-risk human papillomavirus infection is another controversial issue to be discussed, being rare in laryngeal SCC, with an indeterminate prognostic significance and less reliable p16 overexpression as a surrogate marker of HPV infection. Further studies are warranted to address the applicability and to see which of the described parameters may help to better stratify patients with laryngeal SCC and should therefore be included in the pathology report.

The relationship between depth of invasion and cervical lymph node metastasis in patients with laryngeal squamous cell carcinoma

INTRODUCTION

This study aimed to investigate the relationship between DOI (Depth of Invasion) and metastasis to the neck lymph nodes in patients with laryngeal squamous cell carcinoma (LSCC).

METHODS

This cross-sectional observational study was conducted on all patients diagnosed with LSCC who underwent total laryngectomy and neck dissection between 2014 and 2021. DOI was measured in millimeters by a pathologist who examined the patients' specimens. Demographic information and tumor characteristics were collected and analyzed.

RESULTS

The study included 62 patients with LSCC, of whom 23 (37%) had cervical lymph node metastasis. There were 54 (87%) male. The mean DOI was (9.91 ± 1.60) mm in the metastatic group and (7.56 ± 1.42) mm in the non-metastatic group. The results showed that tumors with a higher DOI had a higher probability of cervical lymph node metastasis ( p < 0.001), but there was no significant correlation between DOI and the number of involved lymph nodes (p = 0.318). The analysis also revealed that poorly differentiated tumors (p. value < 0.001), male patients (p. value < 0.001), and older patients (p. value < 0.001) had a higher DOI.

CONCLUSION

According to our results, DOI can be an important predictive factor in predicting cervical lymph node metastasis in LSCC. Therefore, measuring DOI can assist surgeons in decision-making for neck dissection. It is recommended that DOI be determined during surgery using frozen sections or pre-operatively with deep biopsy samples. Considering that laryngeal cancer with cervical lymph node metastasis has a worse prognosis, it could be concluded that greater DOI predicts worse prognosis.

Development and validation of a nomogram for predicting advanced liver fibrosis in patients with chronic hepatitis B

Background

The progression of chronic hepatitis B (CHB) to liver fibrosis and even cirrhosis is often unknown to patients, but noninvasive markers capable of effectively identifying advanced liver fibrosis remains absent.

Objective

Based on the results of liver biopsy, we aimed to construct a new nomogram to validate the stage of liver fibrosis in CHB patients by the basic information of CHB patients and routine laboratory tests.

Methods

Patients with CHB diagnosed for the first time in the First Affiliated Hospital of Anhui Medical University from 2010 to 2018 were selected, and their basic information, laboratory tests and liver biopsy information were collected. Eventually, 974 patients were enrolled in the study, while all patients were randomized into a training cohort (n = 732) and an internal validation cohort (n = 242) according to a 3:1 ratio. In the training cohort, least absolute shrinkage and selection operator (Lasso) regression were used for predictor variable screening, and binary logistic regression analysis was used to build the diagnostic model, which was ultimately presented as a nomogram. The predictive accuracy of the nomograms was analyzed by running operating characteristic curve (ROC) to calculate area under curve (AUC), and the calibration was evaluated. Decision curve analysis (DCA) was used to determine patient benefit. In addition, we validated the built models with internal as well as external cohort (n = 771), respectively.

Results

Ultimately, the training cohort, the internal validation cohort, and the external validation cohort contained sample sizes of 188, 53, and 149, respectively, for advanced liver fibrosis. Gender, albumin (Alb), globulin (Glb), platelets (PLT), alkaline phosphatase (AKP), glutamyl transpeptidase (GGT), and prothrombin time (PT) were screened as independent predictors. Compared with the aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and King's score, the model in the training cohort (AUC = 0.834, 95% CI 0.800-0.868, p < 0.05) and internal validation cohort (AUC = 0.804, 95% CI 0.742-0.866, p < 0.05) showed the best discrimination and the best predictive performance. In addition, DCA showed that the clinical benefit of the nomogram was superior to the APRI, FIB-4 and King's scores in all cohorts.

Conclusion

This study constructed a validated nomogram model with predictors screened from clinical variables which could be easily used for the diagnosis of advanced liver fibrosis in CHB patients.

Depth of Invasion Assessment in Laryngeal Glottic Carcinoma: A Preoperative Imaging Approach for Prognostication

OBJECTIVE

The prognostic value of depth of invasion (DOI) in oral squamous cell cancer carcinoma and cutaneous melanoma is well established, while there is a lack of reports investigating the role of DOI in laryngeal cancer. This study aims to explore the association of glottic cancer DOI with other established pathological risk factors and nodal metastasis and evaluate the feasibility of measuring DOI preoperatively using tomographic imaging.

METHODS

The medical records of glottic cancer patients treated between 2015 and 2020 in a single tertiary referral center were screened retrospectively. Pathologically measured DOI (pDOI) value was also reviewed and registered. Preoperative computer tomography (CT) was used to obtain the radiological DOI (rDOI) measured by two dedicated radiologists. Their inter-rated agreement was assessed and the correlation between pDOI and rDOI was calculated. pDOI association with the main pathology report features was assessed with univariable analysis. Cox univariable and multivariable models were used to explore the role of pDOI on survival.

RESULTS

Ninety-one patients had pDOI data available, of which 59 also had rDOI data. A strong concordance between the two radiologists was found (concordance correlation coefficient = 0.96); rDOI and pDOI were highly and significantly correlated (R = 0.85; p < 0.001). pDOI was significantly higher in patients with perineural invasion (PNI; p < 0.001), lymphovascular invasion (LVI; p < 0.001), and nodal metastasis (p < 0.001). pDOI was associated with disease-free survival at univariable analysis (p = 0.04) while it did not show a significant impact (p = 0.10) at multivariable analysis.

CONCLUSION

Glottic carcinoma DOI correlates with PNI, LVI, and nodal metastasis and it can be reliably assessed in a preoperative setting using CT imaging.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3230-3237, 2024.

Histopathologic Features of Mucosal Head and Neck Cancer Cachexia

Objective

Determine the histopathologic features that correlate with head and neck cancer (HNC) cachexia.

Methods

A single-institution, retrospective study was performed on adults with HPV-negative, mucosal squamous cell carcinoma of the aerodigestive tract undergoing resection and free flap reconstruction from 2014 to 2019. Patients with distant metastases were excluded. Demographics, comorbidities, preoperative nutrition, and surgical pathology reports were collected. Comparisons of histopathologic features and cachexia severity were made.

Results

The study included 222 predominantly male (64.9%) patients aged 61.3 ± 11.8 years. Cachexia was identified in 57.2% patients, and 18.5% were severe (≥15% weight loss). No differences in demographics were identified between the groups. Compared to control, patients with severe cachexia had lower serum hemoglobin (p=0.048) and albumin (p < 0.001), larger tumor diameter (p < 0.001), greater depth of invasion (p < 0.001), and elevated proportions of pT4 disease (p < 0.001), pN2-N3 disease (p=0.001), lymphovascular invasion (p=0.009), and extranodal extension (p=0.014). Multivariate logistic regression identified tumor size (OR [95% CI] = 1.36 [1.08-1.73]), oral cavity tumor (OR [95% CI] = 0.30 [0.11-0.84]), and nodal burden (OR [95% CI] = 1.16 [0.98-1.38]) as significant histopathologic contributors of cancer cachexia.

Conclusions

Larger, more invasive tumors with nodal metastases and aggressive histologic features are associated with greater cachexia severity in mucosal HNC.

Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction

Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors is complex and regulated by many factors, and the treatment of a single drug is easy to cause the human body to produce a drug-resistant phenotype to specific drugs and eventually leads to treatment failure. In the process of clinical tumor treatment, the combination of multiple drugs can produce stronger anti-tumor effects by regulating multiple mechanisms and can reduce the problem of tumor drug resistance while reducing the toxic side effects of drugs. Therefore, it is still a great challenge to construct an efficient and accurate screening method that can systematically consider the synergistic anti- tumor effects of multiple drugs. However, anti-tumor drug synergy prediction is of importance in improving cancer treatment outcomes. However, identifying effective drug combinations remains a complex and challenging task. This review provides a comprehensive overview of cancer drug synergy therapy and the application of artificial intelligence (AI) techniques in cancer drug synergy prediction. In addition, we discuss the challenges and perspectives associated with deep learning approaches. In conclusion, the review of the AI techniques' application in cancer drug synergy prediction can further advance our understanding of cancer drug synergy and provide more effective treatment plans and reasonable drug use strategies for clinical guidance.