Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network

The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step the segmentation of the glottal area within each video frame from which the vibrating edges of the vocal folds are usually derived. Consequently, the outcome of any further vibration analysis depends on the quality of this initial segmentation process. In this work we propose for the first time a procedure to fully automatically segment not only the time-varying glottal area but also the vocal fold tissue directly from laryngeal high-speed video (HSV) using a deep Convolutional Neural Network (CNN) approach. Eighteen different Convolutional Neural Network (CNN) network configurations were trained and evaluated on totally 13,000 high-speed video (HSV) frames obtained from 56 healthy and 74 pathologic subjects. The segmentation quality of the best performing Convolutional Neural Network (CNN) model, which uses Long Short-Term Memory (LSTM) cells to take also the temporal context into account, was intensely investigated on 15 test video sequences comprising 100 consecutive images each. As performance measures the Dice Coefficient (DC) as well as the precisions of four anatomical landmark positions were used. Over all test data a mean Dice Coefficient (DC) of 0.85 was obtained for the glottis and 0.91 and 0.90 for the right and left vocal fold (VF) respectively. The grand average precision of the identified landmarks amounts 2.2 pixels and is in the same range as comparable manual expert segmentations which can be regarded as Gold Standard. The method proposed here requires no user interaction and overcomes the limitations of current semiautomatic or computational expensive approaches. Thus, it allows also for the analysis of long high-speed video (HSV)-sequences and holds the promise to facilitate the objective analysis of vocal fold vibrations in clinical routine. The here used dataset including the ground truth will be provided freely for all scientific groups to allow a quantitative benchmarking of segmentation approaches in future

Immune contexture monitoring in solid tumors focusing on Head and Neck Cancer

Forti evidenze dimostrano una stretta interazione tra il sistema immunitario e lo sviluppo biologico e la progressione clinica dei tumori solidi. L'effetto che il microambiente immunitario del tumore può avere sul comportamento clinico della malattia è indicato come "immunecontexture". Nonostante ciò, l'attuale gestione clinica dei pazienti affetti da cancro non tiene conto di alcuna caratteristica immunologica né per la stadiazione né per le scelte terapeutiche. Il tumore della testa e del collo (HNSCC) rappresenta il 7° tumore più comune al mondo ed è caratterizzato da una prognosi relativamente sfavorevole e dall'effetto negativo dei trattamenti sulla qualità della vita dei pazienti. Oltre alla chirurgia e alla radioterapia, sono disponibili pochi trattamenti sistemici, rappresentati principalmente dalla chemioterapia a base di platino-derivati o dal cetuximab. L'immunoterapia è una nuova strategia terapeutica ancora limitata al setting palliativo (malattia ricorrente non resecabile o metastatica). La ricerca di nuovi biomarcatori o possibili nuovi meccanismi target è molto rilevante quindi nel contesto clinico dell'HNSCC. In questa tesi ci si concentrerà sullo studio di tre possibili popolazioni immunitarie pro-tumorali studiate nell'HNSCC: i neutrofili tumore-associati (TAN), le cellule B intratumorali con fenotipo immunosoppressivo e i T-reg CD8+. Particolare attenzione è data all'applicazione di moderne tecniche biostatistiche e bioinformatiche per riassumere informazioni complesse derivate da variabili cliniche e immunologiche multiparametriche e per validare risultati derivati ​​in situ, attraverso dati di espressione genica derivati da dataset pubblici. Infine, la seconda parte della tesi prenderà in considerazione progetti di ricerca clinica rilevanti, volti a migliorare l'oncologia di precisione nell'HNSCC, sviluppando modelli predittivi di sopravvivenza, confrontando procedure oncologiche alternative, validando nuovi classificatori o testando l'uso di nuovi protocolli clinici come l'uso dell'immunonutrizione.Strong evidences demonstrate a close interplay between the immune system and the biological development and clinical progression of solid tumors. The effect that the tumor immune microenvironment can have on the clinical behavior of the disease is referred as the immuno contexture. Nevertheless, the current clinical management of patients affected by cancer does not take into account any immunological features either for the staging or for the treatment choices. Head and Neck Cancer (HNSCC) represents the 7th most common cancer worldwide and it is characterized by a relatively poor prognosis and detrimental effect of treatments on the quality of life of patients. Beyond surgery and radiotherapy, few systemic treatments are available, mainly represented by platinum-based chemotherapy or cetuximab. Immunotherapy is a new therapeutical strategy still limited to the palliative setting (recurrent not resectable or metastatic disease). The search for new biomarkers or possible new targetable mechanisms is meaningful especially in the clinical setting of HNSCC. In this thesis a focus will be given on the study of three possible pro-tumoral immune populations studied in HNSCC: the tumor associated neutrophils (TAN), intratumoral B-cells with a immunosuppressive phenotype and the CD8+ T-regs. Biostatistical and bioinformatical techniques are applied to summarize complex information derived from multiparametric clinical and immunological variables and to validate in-situ derived findings through gene expression data of public available datasets. Lastly, the second part of the thesis will take into account relevant clinical research projects, aimed at improving the precision oncology in HNSCC developing survival prediction models, comparing alternative oncological procedures, validating new classifiers or testing the use of novel clinical protocols as the use of immunnutrition