In the Netherlands, the model-based approach is used to identify patients with head and neck cancer who may benefit most from proton therapy in terms of prevention of late radiation-induced side ...effects in comparison with photon therapy. To this purpose, a National Indication Protocol Proton therapy for Head and Neck Cancer patients (NIPP-HNC) was developed, which has been approved by the health care authorities. When patients qualify according to the guidelines of the NIPP-HNC, proton therapy is fully reimbursed. This article describes the procedures that were followed to develop this NIPP-HNC and provides all necessary information to introduce model-based selection for patients with head and neck cancer into routine clinical practice.
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling ...training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10
). In comparison of the full workflow (feature selection and classification), no significant difference in ROC was found between centralized and distributed models for both studied endpoints (DeLong p > 0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models.
Diagnostic imaging continues to evolve, and now has unprecedented accuracy for detecting small nodal metastasis. This influences the tumor load in elective target volumes and subsequently has ...consequences for the radiotherapy dose required to control disease in these volumes.
Small metastases that used to remain subclinical and were included in elective volumes, will nowadays be detected and included in high-dose volumes. Consequentially, high-dose volumes will more often contain low-volume disease. These target volume transformations lead to changes in the tumor burden in elective and “gross” tumor volumes with implications for the radiotherapy dose prescribed to these volumes.
For head and neck tumors, nodal staging has evolved from mere palpation to combinations of high-resolution imaging modalities. A traditional nodal gross tumor volume in the neck typically had a minimum diameter of 10–15 mm, while nowadays much smaller tumor deposits are detected in lymph nodes. However, the current dose levels for elective nodal irradiation were empirically determined in the 1950s, and have not changed since.
In this report the radiobiological consequences of target volume transformation caused by modern imaging of the neck are evaluated, and theoretically derived reductions of dose in radiotherapy for head and neck cancer are proposed. The concept of target volume transformation and subsequent strategies for dose adaptation applies to many other tumor types as well. Awareness of this concept may result in new strategies for target definition and selection of dose levels with the aim to provide optimal tumor control with less toxicity.
•GTV delineations based on anatomical MRI overestimated the tumor volume by 95%•Geometrically accurate DW-MRI increases tumor delineation accuracy.•DW-MRI reduces the CTV volumes, while maintaining ...high tumor coverage.
This study aims to determine the added value of a geometrically accurate diffusion-weighted (DW-) MRI sequence on the accuracy of gross tumor volume (GTV) delineations, using pathological tumor delineations as a ground truth.
Sixteen patients with laryngeal or hypopharyngeal carcinoma were included. After total laryngectomy, the specimen was cut into slices. Photographs of these slices were stacked to create a 3D digital specimen reconstruction, which was registered to the in vivo imaging. The pathological tumor (tumorHE) was delineated on the specimen reconstruction.
Six observers delineated all tumors twice: once with only anatomical MR imaging, and once (a few weeks later) when DW sequences were also provided. The majority voting delineation of session one (GTVMRI) and session two (GTVDW-MRI), as well as the clinical target volumes (CTVs), were compared to the tumorHE.
The mean tumorHE volume was 11.1 cm3, compared to a mean GTVMRI volume of 18.5 cm3 and a mean GTVDW-MRI volume of 15.7 cm3. The median sensitivity (tumor coverage) was comparable between sessions: 0.93 (range: 0.61–0.99) for the GTVMRI and 0.91 (range: 0.53–1.00) for the GTVDW-MRI.
The CTV volume also decreased when DWI was available, with a mean CTVMR of 47.1 cm3 and a mean CTVDW-MRI of 41.4 cm3. Complete tumor coverage was achieved in 15 and 14 tumors, respectively.
GTV delineations based on anatomical MR imaging tend to overestimate the tumor volume. The availability of the geometrically accurate DW sequence reduces the GTV overestimation and thereby CTV volumes, while maintaining acceptable tumor coverage.
Background
This study assessed the course of fear of cancer recurrence (FCR) in patients newly diagnosed with head and neck cancer (HNC), identified FCR trajectories and factors associated with FCR ...trajectories.
Methods
Six hundred and seventeen HNC patients from the NET‐QUBIC cohort study completed the Cancer Worry Scale‐6 at diagnosis, 3 and 6 months post‐treatment. FCR trajectories were identified using Latent Class Growth Analysis. Associations were explored between FCR trajectories and baseline demographic and medical variables, coping and self‐efficacy.
Results
Overall, FCR decreased slightly between baseline and 3 months post‐treatment and remained stable up to 6 months. Two FCR trajectories were identified: “high stable” (n = 125) and “low declining” (n = 492). Patients with high stable FCR were younger, reported more negative adjustment, passive coping, and reassuring thoughts, and less avoidance.
Conclusions
The majority of HNC patients have low declining FCR after diagnosis, but one in five patients experience persistent high FCR up to 6 months post‐treatment.
•In OPSCC, high tumor proliferation rate correlates to lower ADCs in DW-MRI.•In OPSCC high CD3-positive lymphocyte influx correlates to lower ADCs.•A correlation between tumor hypoxia and whole tumor ...ADC could not be identified.•Better knowledge of the biological and microanatomical background of ADC should lead to better understanding of the role of DWI in diagnostics and personalized medicine.
Diffusion weighted imaging (DWI) is a frequently performed MRI sequence in cancer patients. While previous studies have shown the clinical value of the apparent diffusion coefficient (ADC) for response prediction and response monitoring, less is known about the biological background of ADC. In the tumor microenvironment, hypoxia and increased proliferation of tumor cells contribute to resistance to (radio-)therapy, while high T-cell influx is related to better prognosis. We investigated the correlation between these three tissue characteristics and ADC in 20 oropharyngeal squamous cell carcinoma patients.
20 patients with oropharyngeal squamous cell carcinoma (OPSCC) who underwent 1.5 T MRI, including DWI were included in this pilot study. Corresponding formalin-fixed paraffin-embedded tumor tissues were immunohistochemically analyzed for protein expression of hypoxia-inducible factor 1a (HIF-1a), Ki-67 and CD3. Expression of these markers was correlated with ADC.
ADC negatively correlated with Ki-67 expression (p = .024) in tumor cells. There was a significant negative correlation between ADC and CD3-positive cell count (p = .009). No correlation was observed between HIF-1a expression and ADC.
This study suggests that ADC reflects characteristics of tumor cells as well as the surrounding microenvironment. Interestingly, high tumor proliferation (a negative prognostic factor) and high T-cell influx (a beneficial prognostic factor) are both associated with a lower ADC. Further studies should be performed to correlate ADC to these histological characteristics in relation to previously known factors that affect ADC, to gain further knowledge on the role of DW-MRI in diagnostics and personalized medicine.
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