Synergies Radiotherapy-Immunotherapy in Head and Neck Cancers. A New Concept for Radiotherapy Target Volumes-"Immunological Dose Painting".

The combination of immune checkpoint inhibitors and definitive radiotherapy is investigated for the multimodal treatment of cisplatin non-eligible locally advanced head and neck cancers (HNC). In the case of recurrent and metastatic HNC, immunotherapy has shown benefit over the EXTREME protocol, being already considered the standard treatment. One of the biggest challenges of multimodal treatment is to establish the optimal therapy sequence so that the synergistic effect is maximal. Thus, superior results were obtained for the administration of anti-CTLA4 immunotherapy followed by hypofractionated radiotherapy, but the anti-PD-L1 therapy demonstrates the maximum potential of radio-sensitization of the tumor in case of concurrent administration. The synergistic effect of radiotherapy-immunotherapy (RT-IT) has been demonstrated in clinical practice, with an overall response rate of about 18% for HNC. Given the demonstrated potential of radiotherapy to activate the immune system through already known mechanisms, it is necessary to identify biomarkers that direct the "nonresponders" of immunotherapy towards a synergistic RT-IT stimulation strategy. Stimulation of the immune system by irradiation can convert "nonresponder" to "responder". With the development of modern techniques, re-irradiation is becoming an increasingly common option for patients who have previously been treated with higher doses of radiation. In this context, radiotherapy in combination with immunotherapy, both in the advanced local stage and in recurrent/metastatic of HNC radiotherapy, could evolve from the "first level" of knowledge (i.e., ballistic precision, dose conformity and homogeneity) to "level two" of "biological dose painting" (in which the concept of tumor heterogeneity and radio-resistance supports the need for doses escalation based on biological criteria), and finally to the "third level" ofthe new concept of "immunological dose painting". The peculiarity of this concept is that the radiotherapy target volumes and tumoricidal dose can be completely reevaluated, taking into account the immune-modulatory effect of irradiation. In this case, the tumor target volume can include even the tumor microenvironment or a partial volume of the primary tumor or metastasis, not all the gross and microscopic disease. Tumoricidal biologically equivalent dose (BED) may be completely different from the currently estimated values, radiotherapy treating the tumor in this case indirectly by boosting the immune response. Thus, the clinical target volume (CTV) can be replaced with a new immunological-clinical target volume (ICTV) for patients who benefit from the RT-IT association (Image 1).

New mechanisms of vesicles migration.

In multicellular organisms, both health and disease are defined by means of communication patterns involving the component cells. Despite the intricate networks of soluble mediators, cells are also programed to exchange complex messages pre-assembled as multimolecular cargo of membranous structures known as extracellular vesicles (EVs). Several biogenetic pathways produce EVs with different properties able to orchestrate neighboring cell reactions or to establish an environment ripe for spreading tumor cells. Such an effect is in fact an extension of similar physiological roles played by exosomes in guiding cell migration under nontumoral tissue remodeling and organogenesis. We start with a biological thought experiment equivalent to Bénard's experiment, involving a fluid layer of EVs adherent to an extracellular matrix, in a haptotactic gradient, then, we build and present the first Lorenz model for EVs migration. Using Galerkin's method of reducing a system of partial differential equations to a system of ordinary differential equations, a biological Lorenz system is developed. Such a physical frame distributing individual molecular or exosomal type cell-guiding cues in the extracellular matrix space could serve as a guide for tissue neoformation of the budding pattern in nontumoral or tumoral instances.

Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis.

This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC, and other metrics derived from confusion matrices. The CNN model showcased notable performance with an accuracy of 98% and an AUC of 0.97, effectively complementing the broader model analysis. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like "control at one year", "age of the patient", and "beam-on time for volume V1 treated" were consistently influential across various models, albeit their impacts were interpreted differently depending on the model's underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial. Our research explores the effects of staged Gamma Knife radiosurgery (GKRS) on larger tumors, revealing no significant outcome differences across protocols. It uniquely considers the impact of beam-on time and fraction intervals on treatment efficacy. However, the investigation is limited by a small patient cohort and data from a single institution, suggesting the need for future multicenter research.

AI Evaluation of Imaging Factors in the Evolution of Stage-Treated Metastases Using Gamma Knife.

BACKGROUND: The study investigated whether three deep-learning models, namely, the CNN_model (trained from scratch), the TL_model (transfer learning), and the FT_model (fine-tuning), could predict the early response of brain metastases (BM) to radiosurgery using a minimal pre-processing of the MRI images. The dataset consisted of 19 BM patients who underwent stereotactic-radiosurgery (SRS) within 3 months. The images used included axial fluid-attenuated inversion recovery (FLAIR) sequences and high-resolution contrast-enhanced T1-weighted (CE T1w) sequences from the tumor center. The patients were classified as responders (complete or partial response) or non-responders (stable or progressive disease). METHODS: A total of 2320 images from the regression class and 874 from the progression class were randomly assigned to training, testing, and validation groups. The DL models were trained using the training-group images and labels, and the validation dataset was used to select the best model for classifying the evaluation images as showing regression or progression. RESULTS: Among the 19 patients, 15 were classified as "responders" and 4 as "non-responders". The CNN_model achieved good performance for both classes, showing high precision, recall, and F1-scores. The overall accuracy was 0.98, with an AUC of 0.989. The TL_model performed well in identifying the "progression" class, but could benefit from improved precision, while the "regression" class exhibited high precision, but lower recall. The overall accuracy of the TL_model was 0.92, and the AUC was 0.936. The FT_model showed high recall for "progression", but low precision, and for the "regression" class, it exhibited a high precision, but lower recall. The overall accuracy for the FT_model was 0.83, with an AUC of 0.885. CONCLUSIONS: Among the three models analyzed, the CNN_model, trained from scratch, provided the most accurate predictions of SRS responses for unlearned BM images. This suggests that CNN models could potentially predict SRS prognoses from small datasets. However, further analysis is needed, especially in cases where class imbalances exist.

New Challenges of Treatment for Locally Advanced Head and Neck Cancers in the Covid-19 Pandemic Era.

Locally advanced head and neck cancer is a unique challenge for cancer management in the Covid-19 situation. The negative consequences of delaying radio-chemotherapy treatment make it necessary to prioritize these patients, the continuation of radiotherapy being indicated even if SARS-CoV-2 infection is confirmed in the case of patients with moderate and mild symptoms. For an early scenario, the standard chemo-radiotherapy using simultaneous integrated boost (SIB) technique is the preferred option, because it reduces the overall treatment time. For a late scenario with limited resources, hypo-fractionated treatment, with possible omission of chemotherapy for elderly patients and for those who have comorbidities, is recommended. Concurrent chemotherapy is controversial for dose values >2.4 Gy per fraction. The implementation of hypo-fractionated regimens should be based on a careful assessment of dose-volume constraints for organs at risks (OARs), using recommendations from clinical trials or dose conversion based on the linear-quadratic (LQ) model. Induction chemotherapy is not considered the optimal solution in this situation because of the risk of immunosuppression even though in selected groups of patients TPF regimen may bring benefits. Although the MACH-NC meta-analysis of chemotherapy in head and neck cancers did not demonstrate the superiority of induction chemotherapy over concurrent chemoradiotherapy, an induction regimen could be considered for cases with an increased risk of metastasis even in the case of a possible Covid-19 pandemic scenario.

Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology.

Radiomics is a relatively new concept that consists of extracting data from images and applies advanced characterization algorithms to generate imaging features. These features are biomarkers with prognostic and predictive value, which provide a characterization of tumor phenotypes in a non-invasive manner. The clinical application of radiomics is hampered by challenges such as lack of image acquisition and analysis standardization. Textural features extracted from computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT) images of patients diagnosed with head and neck cancers can be used in the pre-therapeutic evaluation of the response to multimodal chemo-radiotherapy. For patients with positive HPV-oropharyngeal cancers, the correlation of the radiomic textural features from the tumor with p16 values from the pathological sample can identify tumor specific signatures in CT imaging, an entity with favorable prognosis and a better response to chemo-radiotherapy. Pretreatment contrast CT-scans were extracted and radiomics analysis of gross tumor volume were performed using MaZda package apart from MaZda software containing B11 program for texture analysis and visualization. Data set was randomly divided into a training dataset and a test dataset and machine learning algorithms were applied to identify a textural radiomic signature. Radiomic texture analysis and machine learning algorithms demonstrate a predictive potential related to the capability of stratification for subclasses of platinum-chemotherapy resistance and radioresistant head and neck cancers requiring an intensification of multimodal treatment.