Implementation of a rapid learning platform: predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Background and Purpose To improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset. Materials and Methods Data extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre\u27s (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort). Results Data mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so. Conclusions The technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort

PTCOG Head and Neck Subcommittee Consensus Guidelines on Particle Therapy for the Management of Head and Neck Tumors

Purpose: Radiation therapy is a standard modality in the treatment for cancers of the head and neck, but is associated with significant short- and long-term side effects. Proton therapy, with its unique physical characteristics, can deliver less dose to normal tissues, resulting in fewer side effects. Proton therapy is currently being used for the treatment of head and neck cancer, with increasing clinical evidence supporting its use. However, barriers to wider adoption include access, cost, and the need for higher-level evidence.Methods: The clinical evidence for the use of proton therapy in the treatment of head and neck cancer are reviewed here, including indications, advantages, and challenges.Results: The Particle Therapy Cooperative Group Head and Neck Subcommittee task group provides consensus guidelines for the use of proton therapy for head and neck cancer.Conclusion: This report can be used as a guide for clinical use, to understand clinical trials, and to inform future research efforts.</p

Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered collaborations

Research in modern healthcare requires vast volumes of data from various healthcare centers across the globe. It is not always feasible to centralize clinical data without compromising privacy. A tool addressing these issues and facilitating reuse of clinical data is the need of the hour. The Federated Learning approach, governed in a set of agreements such as the Personal Health Train (PHT) manages to tackle these concerns by distributing models to the data centers instead of the traditional approach of centralizing datasets. One of the prerequisites of PHT is using semantically interoperable datasets for the models to be able to find them. FAIR (Findable, Accessible, Interoperable, Reusable) principles help in building interoperable and reusable data by adding knowledge representation and providing descriptive metadata. However, the process of making data FAIR is not always easy and straight-forward. Our main objective is to disentangle this process by using domain and technical expertise and get data prepared for federated learning. This paper introduces applications that are easily deployable as Docker containers, which will automate parts of the aforementioned process and significantly simplify the task of creating FAIR clinical data. Our method bypasses the need for clinical researchers to have a high degree of technical skills. We demonstrate the FAIR-ification process by applying it to five Head and Neck cancer datasets (four public and one private). The PHT paradigm is explored by building a distributed visualization dashboard from the aggregated summaries of the FAIR-ified datasets. Using the PHT infrastructure for exchanging only statistical summaries or model coefficients allows researchers to explore data from multiple centers without breaching privacy

99mTc Hynic-rh-Annexin V scintigraphy for in vivo imaging of apoptosis in patients with head and neck cancer treated with chemoradiotherapy

PURPOSE: The purpose of this study was to determine the value of (99m)Tc Hynic-rh-Annexin-V-Scintigraphy (TAVS), a non-invasive in vivo technique to demonstrate apoptosis in patients with head and neck squamous cell carcinoma. METHODS: TAVS were performed before and within 48 h after the first course of cisplatin-based chemoradiation. Radiation dose given to the tumour at the time of post-treatment TAVS was 6-8 Gy. Single-photon emission tomography data were co-registered to planning CT scan. Complete sets of these data were available for 13 patients. The radiation dose at post-treatment TAVS was calculated for several regions of interest (ROI): primary tumour, involved lymph nodes and salivary glands. Annexin uptake was determined in each ROI, and the difference between post-treatment and baseline TAVS represented the absolute Annexin uptake: Delta uptake (DeltaU). RESULTS: In 24 of 26 parotid glands, treatment-induced Annexin uptake was observed. Mean DeltaU was significantly correlated with the mean radiation dose given to the parotid glands (r = 0.59, p = 0.002): Glands that received higher doses showed more Annexin uptake. DeltaU in primary tumour and pathological lymph nodes showed large inter-patient differences. A high correlation was observed on an inter-patient level (r = 0.71, p = 0.006) between the maximum DeltaU in primary tumour and in the lymph nodes. CONCLUSIONS: Within the dose range of 0-8 Gy, Annexin-V-scintigraphy showed a radiation-dose-dependent uptake in parotid glands, indicative of early apoptosis during treatment. The inter-individual spread in Annexin uptake in primary tumours could not be related to differences in dose or tumour volume, but the Annexin uptake in tumour and lymph nodes were closely correlated. This effect might represent a tumour-specific apoptotic respons

Key challenges in normal tissue complication probability model development and validation:towards a comprehensive strategy

Normal Tissue Complication Probability (NTCP) models can be used for treatment plan optimisation and patient selection for emerging treatment techniques. We discuss and suggest methodological approaches to address key challenges in NTCP model development and validation, including: missing data, non-linear response relationships, multicollinearity between predictors, overfitting, generalisability and the prediction of multiple complication grades at multiple time points. The methodological approaches chosen are aimed to improve the accuracy, transparency and robustness of future NTCP-models. We demonstrate our methodological approaches using clinical data

The relation between prediction model performance measures and patient selection outcomes for proton therapy in head and neck cancer

Background: Normal-tissue complication probability (NTCP) models predict complication risk in patients receiving radiotherapy, considering radiation dose to healthy tissues, and are used to select patients for proton therapy, based on their expected reduction in risk after proton therapy versus photon radiotherapy (ΔNTCP). Recommended model evaluation measures include area under the receiver operating characteristic curve (AUC), overall calibration (CITL), and calibration slope (CS), whose precise relation to patient selection is still unclear. We investigated how each measure relates to patient selection outcomes. Methods: The model validation and consequent patient selection process was simulated within empirical head and neck cancer patient data. By manipulating performance measures independently via model perturbations, the relation between model performance and patient selection was studied. Results: Small reductions in AUC (-0.02) yielded mean changes in ΔNTCP between 0.9–3.2 %, and single-model patient selection differences between 2–19 %. Deviations (-0.2 or +0.2) in CITL or CS yielded mean changes in ΔNTCP between 0.3–1.4 %, and single-model patient selection differences between 1–10 %. Conclusions: Each measure independently impacts ΔNTCP and patient selection and should thus be assessed in a representative sufficiently large external sample. Our suggested practical model selection approach is considering the model with the highest AUC, and recalibrating it if needed

The impact of responding to patient messages with large language model assistance

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.Comment: 4 figures and tables in main, submitted for revie

Assessing the prognostic value of tumor-infiltrating CD57+ cells in advanced stage head and neck cancer using QuPath digital image analysis

This study aimed to assess the prognostic value of intratumoral CD57+ cells in head and neck squamous cell carcinoma (HNSCC) and to examine the reproducibility of these analyses using QuPath. Pretreatment biopsies of 159 patients with HPV-negative, stage III/IV HNSCC treated with chemoradiotherapy were immunohistochemically stained for CD57. The number of CD57+ cells per mm2 tumor epithelium was quantified by two independent observers and by QuPath, software for digital pathology image analysis. Concordance between the observers and QuPath was assessed by intraclass correlation coefficients (ICC). The correlation between CD57 and clinicopathological characteristics was assessed; associations with clinical outcome were estimated using Cox proportional hazard analysis and visualized using Kaplan-Meier curves. The patient cohort had a 3-year OS of 65.8% with a median follow-up of 54 months. The number of CD57+ cells/mm2 tumor tissue did not correlate to OS, DFS, or LRC. N stage predicted prognosis (OS: HR 0.43, p = 0.008; DFS: HR 0.41, p = 0.003; LRC: HR 0.24, p = 0.007), as did WHO performance state (OS: HR 0.48, p = 0.028; LRC: 0.33, p = 0.039). Quantification by QuPath showed moderate to good concordance with two human observers (ICCs 0.836, CI 0.805–0.863, and 0.741, CI 0.692–0.783, respectively). In conclusion, the presence of CD57+ TILs did not correlate to prognosis in advanced stage, HPV-negative HNSCC patients treated with chemoradiotherapy. Substantial concordance between human observers and QuPath was found, confirming a promising future role for digital, algorithm driven image analysis

Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer:A new concept for individually optimised treatment

Background and purpose: A comprehensive individual toxicity risk profile is needed to improve radiation treatment optimisation, minimising toxicity burden, in head and neck cancer (HNC) patients. We aimed to develop and externally validate NTCP models for various toxicities at multiple time points. Materials and methods: Using logistic regression, we determined the relationship between normal tissue irradiation and the risk of 22 toxicities at ten time points during and after treatment in 750 HNC patients. The toxicities involved swallowing, salivary, mucosal, speech, pain and general complaints. Studied pre-dictors included patient, tumour and treatment characteristics and dose parameters of 28 organs. The resulting NTCP models were externally validated in 395 HNC patients. Results: The NTCP models involved 14 organs that were associated with at least one toxicity. The oral cavity was the predominant organ, associated with 12 toxicities. Other important organs included the parotid and submandibular glands, buccal mucosa and swallowing muscles. In addition, baseline toxicity, treatment modality, and tumour site were common predictors of toxicity. The median discrimination performance (AUC) of the models was 0.71 (interquartile range: 0.68-0.75) at internal validation and 0.67 (interquartile range: 0.62-0.71) at external validation. Conclusion: We established a comprehensive individual toxicity risk profile that provides essential insight into how radiation exposure of various organs translates into multiple acute and late toxicities. This comprehensive understanding of radiation-induced toxicities enables a new radiation treatment optimisation concept that balances multiple toxicity risks simultaneously and minimises the overall tox-icity burden for an individual HNC patient who needs to undergo radiation treatment. (C) 2021 The Author(s). Published by Elsevier B.V