Semaphorin-3F/Neuropilin-2 Transcriptional Expression as a Predictive Biomarker of Occult Lymph Node Metastases in HNSCC

Semaphorin 3F (SEMA3F) influences patient survival in esophageal adenocarcinoma

In esophageal adenocarcinoma, the presence of lymph node metastases predicts patients' survival even after curative resection. Currently, there is no highly accurate marker for detecting the presence of lymph node metastasis. The SEMA3F/NRP2 axis was initially characterized in axon guidance and recent evidence has revealed its significant involvement in lymphangiogenesis, angiogenesis, and carcinogenesis. Hence, the objective of this study was to elucidate the roles of SEMA3F and its receptor NRP2 in esophageal adenocarcinoma. We conducted an immunohistochemical evaluation of SEMA3F and NRP2 protein expression in 776 patients with esophageal adenocarcinoma who underwent Ivor-Lewis esophagectomy at the University Hospital of Cologne. Total and positive cancer cell counts were digitally analyzed using QuPath and verified by experienced pathologists to ensure accuracy. Positive expression was determined as a cell percentage exceeding the 50th percentile threshold. In our cohort, patients exhibiting SEMA3F positive expression experience significantly lower pT- and pN-stages. In contrast, positive NRP2 expression is associated with the presence of lymph node metastases. Survival analyses showed that the expression status of NRP2 had no impact on patient survival. However, SEMA3F positivity was associated with a favorable patient survival outcome (median OS: 38.9 vs. 26.5 months). Furthermore, SEMA3F could be confirmed as an independent factor for better patient survival in patients with early tumor stage (pT1N0-3: HR = 0.505, p = 0.014, pT1-4N0: HR = 0.664, p = 0.024, pT1N0: HR = 0.483, p = 0.040). In summary, SEMA3F emerges as an independent predictor for a favorable prognosis in patients with early-stage esophageal adenocarcinoma. Additionally, NRP2 expression is linked to a higher risk of lymph node metastases occurrence. We hypothesize that low SEMA3F expression could identify patients with early-stage tumors who might benefit from more aggressive treatment options or intensified follow-up. Furthermore, SEMA3F and its associated pathways should be explored as potential tumor-suppressing agents.

Transcriptional expression of SLC16A7 as a biomarker of occult lymph node metastases in patients with head and neck squamous cell carcinoma

PURPOSE

Glucose is the main energy substrate of tumor cells. This study aims to assess whether the transcriptional expression of glucose metabolism-related genes is associated with occult lymph node metastases in head and neck squamous cell carcinoma (HNSCC) patients.

METHODS

We examined the transcriptional expression of a panel of glucose metabolism-related genes in a cohort of 53 patients with HNSCC without cervical lymph node involvement at the time of diagnosis (cN0) and subsequently treated with elective neck dissection.

RESULTS

Occult lymph node metastases were found in 37.7% (n = 20) of the patients. Among the analyzed genes, SLC16A7 exhibited the strongest association with the presence of occult lymph node metastases. Patients with occult lymph node metastases (cN0/pN +) had significantly lower SLC16A7 expression values (p = 0.001). Patients with low SLC16A7 expression (n = 17, 32.1%) had a frequency of occult lymph node metastases of 76.5%, while for patients with high SLCA16A7 expression (n = 36, 67.9%) it was 19.4% (P = 0.0001). A multivariable analysis showed that patients with low expression of SLC16A7 had a 12.6 times higher risk of developing occult lymph node metastases.

CONCLUSION

cN0 HNSCC patients with low SLC16A7 expression had a higher risk of occult lymph node metastases.

Comparing three-dimensional and two-dimensional deep-learning, radiomics, and fusion models for predicting occult lymph node metastasis in laryngeal squamous cell carcinoma based on CT imaging: a multicentre, retrospective, diagnostic study

Background

The occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis of patients. This study aimed to comprehensively compare the performance of the three-dimensional and two-dimensional deep learning models, radiomics model, and the fusion models for predicting occult LNM in LSCC.

Methods

In this retrospective diagnostic study, a total of 553 patients with clinical N0 stage LSCC, who underwent surgical treatment without distant metastasis and multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 and December 30, 2020. The participant data were manually retrieved from medical records, imaging databases, and pathology reports. The study cohort was divided into a training set (n = 300), an internal test set (n = 89), and two external test sets (n = 120 and 44, respectively). The three-dimensional deep learning (3D DL), two-dimensional deep learning (2D DL), and radiomics model were developed using CT images of the primary tumor. The clinical model was constructed based on clinical and radiological features. Two fusion strategies were utilized to develop the fusion model: the feature-based DLRad_FB model and the decision-based DLRad_DB model. The discriminative ability and correlation of 3D DL, 2D DL and radiomics features were analysed comprehensively. The performances of the predictive models were evaluated based on the pathological diagnosis.

Findings

The 3D DL features had superior discriminative ability and lower internal redundancy compared to 2D DL and radiomics features. The DLRad_DB model achieved the highest AUC (0.89-0.90) among all the study sets, significantly outperforming the clinical model (AUC = 0.73-0.78, P = 0.0001-0.042, Delong test). Compared to the DLRad_DB model, the AUC values for the DLRad_FB, 3D DL, 2D DL, and radiomics models were 0.82-0.84 (P = 0.025-0.46), 0.86-0.89 (P = 0.75-0.97), 0.83-0.86 (P = 0.029-0.66), and 0.79-0.82 (P = 0.0072-0.10), respectively in the study sets. Additionally, the DLRad_DB model exhibited the best sensitivity (82-88%) and specificity (79-85%) in the test sets.

Interpretation

The decision-based fusion model DLRad_DB, which combines 3D DL, 2D DL, radiomics, and clinical data, can be utilized to predict occult LNM in LSCC. This has the potential to minimize unnecessary lymph node dissection and prophylactic radiotherapy in patients with cN0 disease.

Funding

National Natural Science Foundation of China, Natural Science Foundation of Shandong Province.

Clinical value of dual-energy CT for predicting occult metastasis in central neck lymph nodes of papillary thyroid carcinoma

OBJECTIVES

To predict the probability of occult lymph node metastasis (OLNM) in the central cervical by analyzing the dual-energy computed tomography (DECT) parameters derived from papillary thyroid carcinoma (PTC).

METHODS

Data were retrospectively collected from patients with pathologically confirmed PTC who underwent arterial and venous phases of enhanced DECT with concurrent central neck lymph node dissection (CLND). Three clinical features, three shape-related features, and twenty-six DECT-derived parameters were measured. The univariate and multivariate analyses were applied to select the relevant parameters and develop the nomogram.

RESULTS

A total 140 cases with negative diagnosis of cervical central lymph node metastases by preoperative evaluation were included, among which 88 patients with metastasis (OLNM +) and 52 patients without metastasis (OLNM -) were finally confirmed by pathology. (1) Anteroposterior/transverse diameter ratio (A/T) derived from the PTC focus had significant difference between the OLNM + and OLNM - groups (p < 0.05). (2) In the arterial phase, iodine concentration (ICarterial), normalized iodine concentration (NICarterial), effective atomic number (Zeff-arterial), electron density (EDarterial), and slope of energy curve (karterial) from PTC focus showed significant difference (all p < 0.05) between the two groups. In the venous phase, only the CT value under the 40 keV (HU40keVvenous) had differences (p < 0.05). (3) The nomogram was produced to predict the probability of OLNM, and the AUC, sensitivity, and specificity in the training and test cohort were 0.830, 75.0%, 76.9%, and 0.829, 65.9%, 84.6%, respectively.

CONCLUSIONS

DECT parameters combined with shape-related feature derived from PTC might be used as predictors of OLNM in the central neck.

CLINICAL RELEVANCE STATEMENT

Preoperative imaging evaluation combining shape-related features and dual-energy CT parameters could serve as a reference to discern occult lymph node metastasis in central neck during the surgically planning of papillary thyroid carcinoma.

KEY POINTS

• Papillary thyroid carcinoma (PTC) patients may have occult lymph node metastasis (OLNM) in the central neck, which is extremely difficult to find by preoperative imaging examination. • Dual-energy CT quantitative evaluation has higher accuracy than conventional CT and can predicting OLNM in the central neck of PTC. • Dual-energy CT quantitative parameters and morphology of PTC can serve as a useful tool in predicting OLNM in the central neck, and as a guide for personalized treatment.