Modelling the lymphatic metastatic progression pathways of OPSCC from multi-institutional datasets.

The elective clinical target volume (CTV-N) in oropharyngeal squamous cell carcinoma (OPSCC) is currently based mostly on the prevalence of lymph node metastases in different lymph node levels (LNLs) for a given primary tumor location. We present a probabilistic model for ipsilateral lymphatic spread that can quantify the microscopic nodal involvement risk based on an individual patient's T-category and clinical involvement of LNLs at diagnosis. We extend a previously published hidden Markov model (HMM), which models the LNLs (I, II, III, IV, V, and VII) as hidden binary random variables (RVs). Each represents a patient's true state of lymphatic involvement. Clinical involvement at diagnosis represents the observed binary RVs linked to the true state via sensitivity and specificity. The primary tumor and the hidden RVs are connected in a graph. Each edge represents the conditional probability of metastatic spread per abstract time-step, given disease at the edge's starting node. To learn these probabilities, we draw Markov chain Monte Carlo samples from the likelihood of a dataset (686 OPSCC patients) from three institutions. We compute the model evidence using thermodynamic integration for different graphs to determine which describes the data best.The graph maximizing the model evidence connects the tumor to each LNL and the LNLs I through V in order. It predicts the risk of occult disease in level IV is below 5% if level III is clinically negative, and that the risk of occult disease in level V is below 5% except for advanced T-category (T3 and T4) patients with clinical involvement of levels II, III, and IV. The provided statistical model of nodal involvement in OPSCC patients trained on multi-institutional data may guide the design of clinical trials on volume-deescalated treatment of OPSCC and contribute to more personal guidelines on elective nodal treatment.

A multi-centric dataset on patient-individual pathological lymph node involvement in head and neck squamous cell carcinoma.

Dataset: We provide a dataset on lymph node metastases in 968 patients with newly diagnosed head and neck squamous cell carcinoma (HNSCC). All patients received neck dissection and we report the number of metastatic versus investigated lymph nodes per lymph node level (LNL) for every individual patient. Additionally, clinicopathological factors including T-category, primary tumor subsite (ICD-O-3 code), age, and sex are reported for all patients. The data is provided as three datasets: Dataset 1 contains 373 HNSCC patients treated at Centre Léon Bérard (CLB), France, with primary tumor location in the oral cavity, oropharynx, hypopharynx, and larynx. Dataset 2 contains 332 HNSCC patients treated at the Inselspital, Bern University Hospital (ISB), Switzerland with primary tumor location in the oral cavity, oropharynx, hypopharynx, and larynx. For these patients, additional information is provided including lateralization of the primary tumor, size and location of the largest metastases, and clinical involvement based on computed tomography (CT), magnetic resonance imaging (MRI), and/or 18FDG-positron emission tomography (PET/CT) imaging. Dataset 3 consists of 263 oropharyngeal SCC patients underlying a previous publication by Bauwens et al. [1], which were treated at CLB. For these patients, additional information including HPV status, lateralization of the primary tumor and clinically diagnosed lymph node involvement is provided. Reuse Potential: The data may be used to quantify the probability of occult lymph node metastases in each LNL, depending on an individual patient's characteristics of the primary tumor and the location of clinically diagnosed lymph node metastases. As such, the data may contribute to further personalize the elective treatment of the neck for HNSCC patients, i.e. definition of the elective clinical target volume (CTV-N) in radiotherapy (RT) and the extent of neck dissection (ND) in surgery. There exists only one similar publicly available dataset that reports clinical involvement per LNL in 287 oropharyngeal SCC patients [2]. The data presented in this article substantially extends the available data, it additionally includes pathologically assessed involvement per LNL, and it provides data for multiple subsites in the head and neck region.

A dataset on patient-individual lymph node involvement in oropharyngeal squamous cell carcinoma.

Dataset: We provide a dataset on lymph node level (LNL) involvement in 287 patients with newly diagnosed oropharyngeal squamous cell carcinoma (OPSCC). For each patient, ipsilateral and contralateral LNL involvement for levels I to VII is reported together with clinicopathological factors including TNM-stage, primary tumor subsite, tumor lateralization, HPV status, sex, age, smoking status, and primary treatment. LNL involvement was assessed individually based on available diagnostic modalities (PET, MRI, CT, fine needle aspiration) by reviewing pathology and radiology reports together with the radiological images. The data is shared as a CSV-table with rows of patients and columns of patient/tumor-specific information and the involvement of individual LNL based on the respective diagnostic modalities. Reuse potential: Patterns of lymphatic progression have never been reported on a patient-individual basis in as much detail as provided in this dataset. The data can be used to build quantitative models for lymphatic tumor progression to estimate the probability of occult metastases in LNLs. This may in turn allow for further personalization of the elective clinical target volume definition in radiotherapy and the extent of neck dissection for surgically treated patients. The data can be pooled with other data to build large multi-institutional datasets on lymphatic metastatic progression in the future. Co-submission: This paper supports the original scientific article by Roman Ludwig, Jean-Marc Hoffmann, Bertrand Pouymayou, Grégoire Morand, Martina Broglie Däppen, Matthias Guckenberger, Vincent Grégoire, Panagiotis Balermpas, Jan Unkelbach, "Detailed patient-individual reporting of lymph node involvement in oropharyngeal squamous cell carcinoma with an online interface", Radiotherapy & Oncology [1].

Detailed patient-individual reporting of lymph node involvement in oropharyngeal squamous cell carcinoma with an online interface.

PURPOSE/OBJECTIVE: Whereas the prevalence of lymph node level (LNL) involvement in head & neck squamous cell carcinomas (HNSCC) has been reported, the details of lymphatic progression patterns are insufficiently quantified. In this study, we investigate how the risk of metastases in each LNL depends on the involvement of upstream LNLs, T-category, HPV status and other risk factors. MATERIALS/METHODS: We retrospectively analyzed patients with newly diagnosed oropharyngeal squamous cell carcinoma (OPSCC) treated at a single institution, resulting in a dataset of 287 patients. For all patients, involvement of LNLs I-VII was recorded individually based on available diagnostic modalities (PET, MRI, CT, FNA) together with clinicopathological factors. To analyze the dataset, a web-based graphical user interface (GUI) was developed, which allows querying the number of patients with a certain combination of co-involved LNLs and tumor characteristics. RESULTS: The full dataset and GUI is part of the publication. Selected findings are: Ipsilateral level IV was involved in 27% of patients with level II and III involvement, but only in 2% of patients with level II but not III involvement. Prevalence of involvement of ipsilateral levels II, III, IV, V was 79%, 34%, 7%, 3% for early T-category patients (T1/T2) and 85%, 50%, 17%, 9% for late T-category (T3/T4), quantifying increasing involvement with T-category. Contralateral levels II, III, IV were involved in 41%, 19%, 4% and 12%, 3%, 2% for tumors with and without midline extension, respectively. T-stage dependence of LNL involvement was more pronounced in HPV negative than positive tumors, but overall involvement was similar. Ipsilateral level VII was involved in 14% and 6% of patients with primary tumors in the tonsil and the base of tongue, respectively. CONCLUSIONS: Detailed quantification of LNL involvement in HNSCC depending on involvement of upstream LNLs and clinicopathological factors may allow for further personalization of CTV-N definition in the future.

A hidden Markov model for lymphatic tumor progression in the head and neck.

Currently, elective clinical target volume (CTV-N) definition for head and neck squamous cell carcinoma (HNSCC) is mostly based on the prevalence of nodal involvement for a given tumor location. In this work, we propose a probabilistic model for lymphatic metastatic spread that can quantify the risk of microscopic involvement in lymph node levels (LNL) given the location of macroscopic metastases and T-category. This may allow for further personalized CTV-N definition based on an individual patient's state of disease. We model the patient's state of metastatic lymphatic progression as a collection of hidden binary random variables that indicate the involvement of LNLs. In addition, each LNL is associated with observed binary random variables that indicate whether macroscopic metastases are detected. A hidden Markov model (HMM) is used to compute the probabilities of transitions between states over time. The underlying graph of the HMM represents the anatomy of the lymphatic drainage system. Learning of the transition probabilities is done via Markov chain Monte Carlo sampling and is based on a dataset of HNSCC patients in whom involvement of individual LNLs was reported. The model is demonstrated for ipsilateral metastatic spread in oropharyngeal HNSCC patients. We demonstrate the model's capability to quantify the risk of microscopic involvement in levels III and IV, depending on whether macroscopic metastases are observed in the upstream levels II and III, and depending on T-category. In conclusion, the statistical model of lymphatic progression may inform future, more personalized, guidelines on which LNL to include in the elective CTV. However, larger multi-institutional datasets for model parameter learning are required for that.

Benefit of replanning in MR-guided online adaptive radiation therapy in the treatment of liver metastasis.

PURPOSE: To assess the effects of daily adaptive MR-guided replanning in stereotactic body radiation therapy (SBRT) of liver metastases based on a patient individual longitudinal dosimetric analysis. METHODS: Fifteen patients assigned to SBRT for oligometastatic liver metastases underwent daily MR-guided target localization and on-table treatment plan re-optimization. Gross tumor volume (GTV) and organs at risk (OARs) were adapted to the anatomy-of-the-day. A reoptimized plan (RP) and a rigidly shifted baseline plan (sBP) without re-optimization were generated for each fraction. After extraction of DVH parameters for GTV, planning target volume (PTV), and OARs (stomach, duodenum, bowel, liver, heart) plans were compared on a per-patient basis. RESULTS: Median pre-treatment GTV and PTV were 14.9 cc (interquartile range (IQR): 7.7-32.9) and 62.7 cc (IQR: 42.4-105.5) respectively. SBRT with RP improved PTV coverage (V100%) for 47/75 of the fractions and reduced doses to the most proximal OARs (D1cc, Dmean) in 33/75 fractions compared to sBP. RP significantly improved PTV coverage (V100%) for metastases within close proximity to an OAR by 4.0% (≤ 0.2 cm distance from the edge of the PTV to the edge of the OAR; n = 7; p = 0.01), but only by 0.2% for metastases farther away from OAR (> 2 cm distance; n = 7; p = 0.37). No acute grade 3 treatment-related toxicities were observed. CONCLUSIONS: MR-guided online replanning SBRT improved target coverage and OAR sparing for liver metastases with a distance from the edge of the PTV to the nearest luminal OAR < 2 cm. Only marginal improvements in target coverage were observed for target distant to critical OARs, indicating that these patients do not benefit from daily adaptive replanning.

The role of computational methods for automating and improving clinical target volume definition.

Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.

Invasion and metastasis in pancreatic cancer.

Pancreatic cancer remains a challenging disease with an overall cumulative 5-year survival rate below 1%. The process of cancer initiation, progression and metastasis is still not understood well. Invasion and tumor metastasis are closely related and both occur within a tumour-host microecology, where stroma and tumour cells exchange enzymes and cytokines that modify the local extracellular matrix, stimulate cell migration, and promote cell proliferation and tumor cell survival. During the last decade considerable progress has been made in understanding genetic alterations of genes involved in local and systemic tumor growth. The most important changes occur in genes which regulate cell cycle progression, extracellular matrix homeostasis and cell migration. Furthermore, there is growing evidence that epigenetic factors including angiogenesis and lymphangiogenesis may participate in the formation of tumor metastasis. In this review we highlight the most important genetic alterations involved in tumor invasion and metastasis and further outline the role of tumor angiogenesis and lymphangiogenesis in systemic tumor dissemination.

Distinguishing of primary cerebral lymphoma from high-grade glioma with perfusion-weighted magnetic resonance imaging.

To assess the usefulness of perfusion-weighted echo-planar magnetic resonance imaging in the differential diagnosis of primary supratentorial lymphoma (PCNSL) and glioblastoma (GBM), 12 patients with a PCNSL and 12 with a GBM were examined using a 1.5 T magnetic resonance (MR) imager. With dynamic-susceptibility contrast MR imaging the intensity-time curves of each tumor were analyzed, and we determined the relative regional cerebral blood volume ratios (rrCBV [tumor/contralateral white matter (WM)]) to find out whether these parameters could be used to separate PCNSL from GBM. The maximum rrCBV ratio in the PCNSL was significantly lower than that of the GBM (P<0.0001). Comparing the intensity-time curves for the two tumor groups, the PCNSL showed a characteristic type of curve with a significant increase in signal intensity above the baseline due to massive leakage of contrast media into the interstitial space. PCNSL tend to have low maximum CBV ratios and typical intensity-time curves. These two parameters may be useful in distinguishing PCNSL from GBM.

PKC 412 small-molecule tyrosine kinase inhibitor: single-compound therapy for pancreatic cancer.

BACKGROUND: PKC412 is a kinase inhibitor that blocks protein kinase C (PKC), vascular endothelial growth factor receptors, platelet-derived growth factor receptor FLT3, and other class III receptor tyrosine kinases. The enthusiasm for this compound is based on its inhibitory effect even in the case of FLT3 mutations. The aim of this study was to analyze the role of FLT3 in pancreatic cancer and to study the biological activity of combined inhibition of neovascularization and mitogenesis in this disease. METHODS: FLT3 expression was analyzed in 18 pancreatic cancer specimens by real-time quantitative polymerase chain reaction (RTQ-PCR) and immunohistochemistry. Sixteen pancreatic cancer cell lines were screened for ITD and D835 point mutations of the FLT3 gene. MTT assays and anchorage-independent growth assays were used to study cell growth. Flow cytometry was used for cell cycle analysis and apoptosis quantification. In vivo AsPC-1 and HPAF-II cells were used for orthotopic tumor modeling. Immunohistochemistry was used to quantify tumor angiogenesis. RESULTS: FLT3 expression is down-regulated in pancreatic cancer. Activating FLT3 mutations (ITD, D835) were not detectable in any of the pancreatic cancer cell lines. Cell growth was significantly inhibited as cell-cycle progression was reduced and programmed cell death increased. In vivo PKC412 therapy resulted in a significant inhibition of orthotopic tumor growth with abrogation of tumor angiogenesis. CONCLUSIONS: These data highlight that PKC412 may be a new compound in target therapy of inoperable pancreatic cancer patients and suggest a potential role for the combined use of broad spectrum kinase inhibitors in the management of these patients.