Adaptive Radiation Therapy for Head and Neck Cancer—Can an Old Goal Evolve into a New Standard?

Current head and neck intensity-modulated radiotherapy (IMRT) techniques cause significant toxicity. This may be explained in part by the fact that IMRT cannot compensate for changes in the location of disease and normal anatomy during treatment, leading to exposure of at-risk bystander tissues to higher-than-anticipated doses. Adaptive radiotherapy (ART) is a novel approach to correct for daily tumor and normal tissue variations through online or offline modification of original IMRT target volumes and plans. ART has been discussed on a conceptual level for many years, but technical limitations have hampered its integration into routine care. In this paper, we review the key anatomic, dosimetric, and treatment delivery issues at play in current investigational development of head and neck ART. We also describe pilot findings from initial clinical deployment of head and neck ART, as well as emerging pathways of future research

INTEGRATION OF BIOMEDICAL IMAGING AND TRANSLATIONAL APPROACHES FOR MANAGEMENT OF HEAD AND NECK CANCER

The aim of the clinical component of this work was to determine whether the currently available clinical imaging tools can be integrated with radiotherapy (RT) platforms for monitoring and adaptation of radiation dose, prediction of tumor response and disease outcomes, and characterization of patterns of failure and normal tissue toxicity in head and neck cancer (HNC) patients with potentially curable tumors. In Aim 1, we showed that the currently available clinical imaging modalities can be successfully used to adapt RT dose based-on dynamic tumor response, predict oncologic disease outcomes, characterize RT-induced toxicity, and identify the patterns of disease failure. We used anatomical MRIs for the RT dose adaptation purpose. Our findings showed that after proper standardization of the immobilization and image acquisition techniques, we can achieve high geometric accuracy. These images can then be used to monitor the shrinkage of tumors during RT and optimize the clinical target volumes accordingly. Our results also showed that this MR-guided dose adaptation technique has a dosimetric advantage over the standard of care and was associated with a reduction in normal tissue doses that translated into a reduction of the odds of long-term RT-induced toxicity. In the second aim, we used quantitative MRIs to determine its benefit for prediction of oncologic outcomes and characterization of RT-induced normal tissue toxicity. Our findings showed that delta changes of apparent diffusion coefficient parameters derived from diffusion-weighted images at mid-RT can be used to predict local recurrence and recurrence free-survival. We also showed that Ktrans and Ve vascular parameters derived from dynamic contrast-enhanced MRIs can characterize the mandibular areas of osteoradionecrosis. In the final clinical aim, we used CT images of recurrence and baseline CT planning images to develop a methodology and workflow that involves the application of deformable image registration software as a tool to standardize image co-registration in addition to granular combined geometric- and dosimetric-based failure characterization to correctly attribute sites and causes of locoregional failure. We then successfully applied this methodology to identify the patterns of failure following postoperative and definitive IMRT in HNC patients. Using this methodology, we showed that most recurrences occurred in the central high dose regions for patients treated with definitive IMRT compared with mainly non-central high dose recurrences after postoperative IMRT. We also correlated recurrences with pretreatment FDG-PET and identified that most of the central high dose recurrences originated in an area that would be covered by a 10-mm margin on the volume of 50% of the maximum FDG uptake. In the translational component of this work, we integrated radiomic features derived from pre-RT CT images with whole-genome measurements using TCGA and TCIA data. Our results demonstrated a statistically significant associations between radiomic features characterizing different tumor phenotypes and different genomic features. These findings represent a promising potential towards non-invasively tract genomic changes in the tumor during treatment and use this information to adapt treatment accordingly. In the final project of this dissertation, we developed a high-throughput approach to identify effective systemic agents against aggressive head and neck tumors with poor prognosis like anaplastic thyroid cancer. We successfully identified three candidate drugs and performed extensive in vitro and in vivo validation using orthotopic and PDX models. Among these drugs, HDAC inhibitor and LBH-589 showed the most effective tumor growth inhibition that can be used in future clinical trials

Validating Dose Uncertainty Estimates Produced by AUTODIRECT: An Automated Program to Evaluate Deformable Image Registration Accuracy.

Deformable image registration is a powerful tool for mapping information, such as radiation therapy dose calculations, from one computed tomography image to another. However, deformable image registration is susceptible to mapping errors. Recently, an automated deformable image registration evaluation of confidence tool was proposed to predict voxel-specific deformable image registration dose mapping errors on a patient-by-patient basis. The purpose of this work is to conduct an extensive analysis of automated deformable image registration evaluation of confidence tool to show its effectiveness in estimating dose mapping errors. The proposed format of automated deformable image registration evaluation of confidence tool utilizes 4 simulated patient deformations (3 B-spline-based deformations and 1 rigid transformation) to predict the uncertainty in a deformable image registration algorithm's performance. This workflow is validated for 2 DIR algorithms (B-spline multipass from Velocity and Plastimatch) with 1 physical and 11 virtual phantoms, which have known ground-truth deformations, and with 3 pairs of real patient lung images, which have several hundred identified landmarks. The true dose mapping error distributions closely followed the Student t distributions predicted by automated deformable image registration evaluation of confidence tool for the validation tests: on average, the automated deformable image registration evaluation of confidence tool-produced confidence levels of 50%, 68%, and 95% contained 48.8%, 66.3%, and 93.8% and 50.1%, 67.6%, and 93.8% of the actual errors from Velocity and Plastimatch, respectively. Despite the sparsity of landmark points, the observed error distribution from the 3 lung patient data sets also followed the expected error distribution. The dose error distributions from automated deformable image registration evaluation of confidence tool also demonstrate good resemblance to the true dose error distributions. Automated deformable image registration evaluation of confidence tool was also found to produce accurate confidence intervals for the dose-volume histograms of the deformed dose

SURGICAL NAVIGATION AND AUGMENTED REALITY FOR MARGINS CONTROL IN HEAD AND NECK CANCER

I tumori maligni del distretto testa-collo rappresentano un insieme di lesioni dalle diverse caratteristiche patologiche, epidemiologiche e prognostiche. Per una porzione considerevole di tali patologie, l’intervento chirurgico finalizzato all’asportazione completa del tumore rappresenta l’elemento chiave del trattamento, quand’anche esso includa altre modalità quali la radioterapia e la terapia sistemica. La qualità dell’atto chirurgico ablativo è pertanto essenziale al fine di garantire le massime chance di cura al paziente. Nell’ambito della chirurgia oncologica, la qualità delle ablazioni viene misurata attraverso l’analisi dello stato dei margini di resezione. Oltre a rappresentare un surrogato della qualità della resezione chirurgica, lo stato dei margini di resezione ha notevoli implicazioni da un punto di vista clinico e prognostico. Infatti, il coinvolgimento dei margini di resezione da parte della neoplasia rappresenta invariabilmente un fattore prognostico sfavorevole, oltre che implicare la necessità di intensificare i trattamenti postchirurgici (e.g., ponendo indicazione alla chemioradioterapia adiuvante), comportando una maggiore tossicità per il paziente. La proporzione di resezioni con margini positivi (i.e., coinvolti dalla neoplasia) nel distretto testa-collo è tra le più elevate in ambito di chirurgia oncologica. In tale contesto si pone l’obiettivo del dottorato di cui questa tesi riporta i risultati. Le due tecnologie di cui si è analizzata l’utilità in termini di ottimizzazione dello stato dei margini di resezione sono la navigazione chirurgica con rendering tridimensionale e la realtà aumentata basata sulla videoproiezione di immagini. Le sperimentazioni sono state svolte parzialmente presso l’Università degli Studi di Brescia, parzialmente presso l’Azienda Ospedale Università di Padova e parzialmente presso l’University Health Network (Toronto, Ontario, Canada). I risultati delle sperimentazioni incluse in questo elaborato dimostrano che l'impiego della navigazione chirurgica con rendering tridimensionale nel contesto di procedure oncologiche ablative cervico-cefaliche risulta associata ad un vantaggio significativo in termini di riduzione della frequenza di margini positivi. Al contrario, le tecniche di realtà aumentata basata sulla videoproiezione, nell'ambito della sperimentazione preclinica effettuata, non sono risultate associate a vantaggi sufficienti per poter considerare tale tecnologia per la traslazione clinica.Head and neck malignancies are an heterogeneous group of tumors. Surgery represents the mainstay of treatment for the large majority of head and neck cancers, with ablation being aimed at removing completely the tumor. Radiotherapy and systemic therapy have also a substantial role in the multidisciplinary management of head and neck cancers. The quality of surgical ablation is intimately related to margin status evaluated at a microscopic level. Indeed, margin involvement has a remarkably negative effect on prognosis of patients and mandates the escalation of postoperative treatment by adding concomitant chemotherapy to radiotherapy and accordingly increasing the toxicity of overall treatment. The rate of margin involvement in the head and neck is among the highest in the entire field of surgical oncology. In this context, the present PhD project was aimed at testing the utility of 2 technologies, namely surgical navigation with 3-dimensional rendering and pico projector-based augmented reality, in decreasing the rate of involved margins during oncologic surgical ablations in the craniofacial area. Experiments were performed in the University of Brescia, University of Padua, and University Health Network (Toronto, Ontario, Canada). The research activities completed in the context of this PhD course demonstrated that surgical navigation with 3-dimensional rendering confers a higher quality to oncologic ablations in the head and neck, irrespective of the open or endoscopic surgical technique. The benefits deriving from this implementation come with no relevant drawbacks from a logistical and practical standpoint, nor were major adverse events observed. Thus, implementation of this technology into the standard care is the logical proposed step forward. However, the genuine presence of a prognostic advantage needs longer and larger study to be formally addressed. On the other hand, pico projector-based augmented reality showed no sufficient advantages to encourage translation into the clinical setting. Although observing a clear practical advantage deriving from the projection of osteotomy lines onto the surgical field, no substantial benefits were measured when comparing this technology with surgical navigation with 3-dimensional rendering. Yet recognizing a potential value of this technology from an educational standpoint, the performance displayed in the preclinical setting in terms of surgical margins optimization is not in favor of a clinical translation with this specific aim

A method for accurate spatial registration of PET images and histopathology slices

Background: Accurate alignment between histopathology slices and positron emission tomography (PET) images is important for radiopharmaceutical validation studies. Limited data is available on the registration accuracy that can be achieved between PET and histopathology slices acquired under routine pathology conditions where slices may be non-parallel, non-contiguously cut and of standard block size. The purpose of this study was to demonstrate a method for aligning PET images and histopathology slices acquired from patients with laryngeal cancer and to assess the registration accuracy obtained under these conditions. Methods: Six subjects with laryngeal cancer underwent a 64Cu-copper-II-diacetyl-bis(N4-methylthiosemicarbazone) (64Cu-ATSM) PET computed tomography (CT) scan prior to total laryngectomy. Sea urchin spines were inserted into the pathology specimen to act as fiducial markers. The specimen was fixed in formalin, as per standard histopathology operating procedures, and was then CT scanned and cut into millimetre-thick tissue slices. A subset of the tissue slices that included both tumour and fiducial markers was taken and embedded in paraffin blocks. Subsequently, microtome sectioning and haematoxylin and eosin staining were performed to produce 5-μm-thick tissue sections for microscopic digitisation. A series of rigid registration procedures was performed between the different imaging modalities (PET; in vivo CT—i.e. the CT component of the PET-CT; ex vivo CT; histology slices) with the ex vivo CT serving as the reference image. In vivo and ex vivo CTs were registered using landmark-based registration. Histopathology and ex vivo CT images were aligned using the sea urchin spines with additional anatomical landmarks where available. Registration errors were estimated using a leave-one-out strategy for in vivo to ex vivo CT and were estimated from the RMS landmark accuracy for histopathology to ex vivo CT. Results: The mean ± SD accuracy for registration of the in vivo to ex vivo CT images was 2.66 ± 0.66 mm, and the accuracy for registration of histopathology to ex vivo CT was 0.86 ± 0.41 mm. Estimating the PET to in vivo CT registration accuracy to equal the PET-CT alignment accuracy of 1 mm resulted in an overall average registration error between PET and histopathology slices of 3.0 ± 0.7 mm. Conclusions: We have developed a registration method to align PET images and histopathology slices with an accuracy comparable to the spatial resolution of the PET images.</p

Investigation of Radiation-Induced Toxicity in Head and Neck Cancer Patients through Radiomics and Machine Learning: A Systematic Review

Background. Radiation-induced toxicity represents a crucial concern in oncological treatments of patients affected by head and neck neoplasms, due to its impact on survivors' quality of life. Published reports suggested the potential of radiomics combined with machine learning methods in the prediction and assessment of radiation-induced toxicities, supporting a tailored radiation treatment management. In this paper, we present an update of the current knowledge concerning these modern approaches. Materials and Methods. A systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported. Results. Eight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases. Conclusions. Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities