Label-free, real-time detection of perineural invasion and cancer margins in a murine model of head and neck cancer surgery

Perspective on the use of fluorescence molecular imaging for peripheral and deep en face margin assessment

Significance

Current standard practice for margin assessment in solid tumor resection often leads to suboptimal results due to the inability to assess margins completely in a time-efficient manner. On the other hand, for small skin cancers, peripheral and deep en face margin assessment (PDEMA) offers 100% assessment of margins while sparing the utmost amount of normal surrounding tissues. Nonetheless, PDEMA is limited in its use owing to its lengthy tissue processing and imaging time as well as its requirement for high-quality frozen sections and real-time histologic analysis.

Aim

We aim to explore fluorescence molecular imaging (FMI) as a tool for resolving obstacles and integrating PDEMA into the surgeon-to-pathologist workflow for large solid tumors.

Approach

A review of recent pre-clinical and clinical studies using FMI to assess surgical margins was conducted to highlight promising fluorescence imaging technologies utilized in the surgical suite and laboratory.

Results

FMI techniques that provide macroscopic resolution are efficient in time and have a notable ability to identify true negative tissue yet have limited capability in identifying true positive tissues. Moreover, meso- and microscopic FMI methods require additional time to attain a higher resolution but deliver an enhanced sensitivity in detecting true positive tissues. In both cases, experts are still required to learn to interpret the FMI signals, which prohibits a seamless clinical integration.

Conclusions

Our proposed margin assessment platform (MAP) incorporates both macroscopic and, meso- or microscopic imaging with post-processing and machine learning for interpretation, to enable the application of PDEMA into solid tumor surgery. MAP leverages the advantages of each technique and thoroughly tackles the limitations of time and expertise to optimize the efficiency and accuracy of margin assessment and ultimately improve clinical outcomes.

Pathological Risk Factors for Occult Nodal Metastasis in Early-Stage Squamous Cell Carcinoma of the Oral Cavity

Occult neck metastasis is the presence of metastasis in the cervical lymph nodes that cannot be radiologically or clinically identified. Presence of metastasis in any neck node can have a significant impact on overall survival of patients with oral squamous cell carcinoma (OSCC). Our aim was to analyze the correlation of various histopathological parameters with occult nodal metastasis in early-stage OSCC and to obtain an optimal DOI cut-off value for predicting its increased risk. We conducted a retrospective study on patients who reported to our institute with clinical stage I and II OSCC. The patients having well-differentiated and moderately differentiated OSCC were included. Association of various histopathological parameters with occult nodal metastasis was assessed using statistical analysis. A total of 102 patients of early-stage well-differentiated and moderately differentiated OSCC with clinically negative necks who underwent elective neck dissection at our institute from the year 2018 to 2023 were enrolled in the study. Depth of invasion (DOI), perineural invasion (PNI), worst pattern of invasion (WPOI), and grade of tumor differentiation were the histopathological parameters entered into the univariate regression analysis as predictive variables, and they were found to be predictors of occult nodal metastasis. An optimal DOI cut-off value of 5.5 mm was obtained for predicting the increase in the risk of occult nodal metastasis. DOI, PNI, WPOI, and grade of tumor differentiation are predictors of occult nodal metastasis. There is a need for searching methods for preoperative and intraoperative detection of all these histopathological factors so that unnecessary elective neck treatment can be avoided.

Glioblastoma Standard of Care: Effects on Tumor Evolution and Reverse Translation in Preclinical Models

Glioblastoma (GBM) presents a significant public health challenge as the deadliest and most common malignant brain tumor in adults. Despite standard-of-care treatment, which includes surgery, radiation, and chemotherapy, mortality rates are high, underscoring the critical need for advancing GBM therapy. Over the past two decades, numerous clinical trials have been performed, yet only a small fraction demonstrated a benefit, raising concerns about the predictability of current preclinical models. Traditionally, preclinical studies utilize treatment-naïve tumors, failing to model the clinical scenario where patients undergo standard-of-care treatment prior to recurrence. Recurrent GBM generally exhibits distinct molecular alterations influenced by treatment selection pressures. In this review, we discuss the impact of treatment-surgery, radiation, and chemotherapy-on GBM. We also provide a summary of treatments used in preclinical models, advocating for their integration to enhance the translation of novel strategies to improve therapeutic outcomes in GBM.

Surgical margins in head and neck squamous cell carcinoma: A narrative review

Head and neck squamous cell carcinoma (HNSCC), a prevalent and frequently recurring malignancy, often necessitates surgical intervention. The surgical margin (SM) plays a pivotal role in determining the postoperative treatment strategy and prognostic evaluation of HNSCC. Nonetheless, the process of clinical appraisal and assessment of the SMs remains a complex and indeterminate endeavor, thereby leading to potential difficulties for surgeons in defining the extent of resection. In this regard, we undertake a comprehensive review of the suggested surgical distance in varying circumstances, diverse methods of margin evaluation, and the delicate balance that must be maintained between tissue resection and preservation in head and neck surgical procedures. This review is intended to provide surgeons with pragmatic guidance in selecting the most suitable resection techniques, and in improving patients' quality of life by achieving optimal functional and aesthetic restoration.

Stochastic differential equation modelling of cancer cell migration and tissue invasion

Invasion of the surrounding tissue is a key aspect of cancer growth and spread involving a coordinated effort between cell migration and matrix degradation, and has been the subject of mathematical modelling for almost 30 years. In this current paper we address a long-standing question in the field of cancer cell migration modelling. Namely, identify the migratory pattern and spread of individual cancer cells, or small clusters of cancer cells, when the macroscopic evolution of the cancer cell colony is dictated by a specific partial differential equation (PDE). We show that the usual heuristic understanding of the diffusion and advection terms of the PDE being one-to-one responsible for the random and biased motion of the solitary cancer cells, respectively, is not precise. On the contrary, we show that the drift term of the correct stochastic differential equation scheme that dictates the individual cancer cell migration, should account also for the divergence of the diffusion of the PDE. We support our claims with a number of numerical experiments and computational simulations.