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Floricel C, Wentzel A, Mohamed A, Fuller CD, Canahuate G, Marai GE. Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:1227-1237. [PMID: 38015695 PMCID: PMC10842255 DOI: 10.1109/tvcg.2023.3326939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.
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Canahuate G, Wentzel A, Mohamed ASR, van Dijk LV, Vock DM, Elgohari B, Elhalawani H, Fuller CD, Marai GE. Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas. Oral Oncol 2023; 144:106460. [PMID: 37390759 PMCID: PMC10561377 DOI: 10.1016/j.oraloncology.2023.106460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 05/26/2023] [Accepted: 06/05/2023] [Indexed: 07/02/2023]
Abstract
OBJECTIVE Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC). MATERIALS & METHODS 675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified. These clusterings were combined into a 3-level patient stratification and included along with other known clinical features in a Cox model for predicting survival outcomes, and logistic regression for toxicity, using independent subsets for training and validation. RESULTS Four groups were identified and combined into a 3-level stratification. The inclusion of patient stratifications in predictive models for 5-yr Overall survival (OS), 5-year recurrence free survival, (RFS) and Radiation-associated dysphagia (RAD) consistently improved model performance measured using the area under the curve (AUC). Test set AUC improvements over models with clinical covariates, was 9 % for predicting OS, and 18 % for predicting RFS, and 7 % for predicting RAD. For models with both clinical and AJCC covariates, AUC improvement was 7 %, 9 %, and 2 % for OS, RFS, and RAD, respectively. CONCLUSION Including data-driven patient stratifications considerably improve prognosis for survival and toxicity outcomes over the performance achieved by clinical staging and clinical covariates alone. These stratifications generalize well to across cohorts, and sufficient information for reproducing these clusters is included.
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Affiliation(s)
- Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA.
| | - Andrew Wentzel
- Department of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David M Vock
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - G Elisabeta Marai
- Department of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA
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Wang Y, Van Dijk L, Mohamed ASR, Naser M, Fuller CD, Zhang X, Marai GE, Canahuate G. Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS 2023; 2023:292-300. [PMID: 38343586 PMCID: PMC10853990 DOI: 10.1109/ichi57859.2023.00047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Patient-Reported Outcomes (PRO) are collected directly from the patients using symptom questionnaires. In the case of head and neck cancer patients, PRO surveys are recorded every week during treatment with each patient's visit to the clinic and at different follow-up times after the treatment has concluded. PRO surveys can be very informative regarding the patient's status and the effect of treatment on the patient's quality of life (QoL). Processing PRO data is challenging for several reasons. First, missing data is frequent as patients might skip a question or a questionnaire altogether. Second, PROs are patient-dependent, a rating of 5 for one patient might be a rating of 10 for another patient. Finally, most patients experience severe symptoms during treatment which usually subside over time. However, for some patients, late toxicities persist negatively affecting the patient's QoL. These long-term severe symptoms are hard to predict and are the focus of this study. In this work, we model PRO data collected from head and neck cancer patients treated at the MD Anderson Cancer Center using the MD Anderson Symptom Inventory (MDASI) questionnaire as time series. We impute missing values with a combination of K nearest neighbor (KNN) and Long Short-Term Memory (LSTM) neural networks, and finally, apply LSTM to predict late symptom severity 12 months after treatment. We compare performance against clinical and ARIMA models. We show that the LSTM model combined with KNN imputation is effective in predicting late-stage symptom ratings for occurrence and severity under the AUC and F1 score metrics.
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Affiliation(s)
- Yaohua Wang
- Electrical and Computer Engineering, The University of Iowa, Iowa City, United States
| | - Lisanne Van Dijk
- Radiation Oncology, UT M.D. Anderson Cancer Center, Houston, United States
| | | | - Mohamed Naser
- Radiation Oncology, UT M.D. Anderson Cancer Center, Houston, United States
| | | | - Xinhua Zhang
- Computer Science, University of Illinois at Chicago, Chicago, United States
| | - G Elisabeta Marai
- Computer Science, University of Illinois at Chicago, Chicago, United States
| | - Guadalupe Canahuate
- Electrical and Computer Engineering, The University of Iowa, Iowa City, United States
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Gronberg MP, Beadle BM, Garden AS, Skinner H, Gay S, Netherton T, Cao W, Cardenas CE, Chung C, Fuentes DT, Fuller CD, Howell RM, Jhingran A, Lim TY, Marquez B, Mumme R, Olanrewaju AM, Peterson CB, Vazquez I, Whitaker TJ, Wooten Z, Yang M, Court LE. Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans. Pract Radiat Oncol 2023; 13:e282-e291. [PMID: 36697347 PMCID: PMC11232032 DOI: 10.1016/j.prro.2022.12.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%,D95%, and D99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. CONCLUSIONS Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.
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Affiliation(s)
- Mary P Gronberg
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas.
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Adam S Garden
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Heath Skinner
- Department of Radiation Oncology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Skylar Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Tucker Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Wenhua Cao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Christine Chung
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David T Fuentes
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Clifton D Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tze Yee Lim
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Barbara Marquez
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Adenike M Olanrewaju
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine B Peterson
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ivan Vazquez
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Thomas J Whitaker
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Zachary Wooten
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Statistics, Rice University, Houston, Texas
| | - Ming Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
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Surgical Treatment for Advanced Oropharyngeal Cancer: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59020304. [PMID: 36837506 PMCID: PMC9961866 DOI: 10.3390/medicina59020304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/30/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023]
Abstract
Background and Objectives: to describe current scientific knowledge regarding the treatment options in advanced oropharyngeal cancer. The standard care for advanced oropharyngeal cancer (OPSCC) has been chemoradiotherapy, although surgical approaches followed by adjuvant treatment have been proposed. The best therapy for each patient should be decided by an interdisciplinary tumour-board. Different strategies should be considered for the specific patient's treatment: surgery, chemotherapy and radiation therapy or combinations of them. The treatment choice is influenced by tumour variability and prognostic factors, but it also depends on cancer extension, extranodal extension, nervous invasion, human papilloma virus (HPV) presence, making the decisional algorithm not always clear. HPV-related OPSCC is strongly associated with a favourable overall survival (OS) and disease-free survival rate (DSS); by contrast, HPV-negative OPSCC often flags a worse prognosis. Consequently, the American Joint Committee on Cancer (AJCC) differentiates OPSCC treatment and prognosis based on HPV status. Methods: we carried out a review of current scientific literature to analyze the different indications and limitations of surgical treatment options in OPSCC stage III and IV. Conclusion: robotic surgery or open approaches with reconstructive flaps can be considered in advanced stages, resulting in the de-intensification of subsequent systemic therapy and fewer related side effects. Furthermore, in the event of the primary failure of systemic therapy or disease recurrence, the surgical approach constitutes an additional therapeutic option which lengthens patient survival functions.
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Floricel C, Nipu N, Biggs M, Wentzel A, Canahuate G, Van Dijk L, Mohamed A, Fuller CD, Marai GE. THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:151-161. [PMID: 34591766 PMCID: PMC8785360 DOI: 10.1109/tvcg.2021.3114810] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.
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Volpe S, Pepa M, Zaffaroni M, Bellerba F, Santamaria R, Marvaso G, Isaksson LJ, Gandini S, Starzyńska A, Leonardi MC, Orecchia R, Alterio D, Jereczek-Fossa BA. Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist. Front Oncol 2021; 11:772663. [PMID: 34869010 PMCID: PMC8637856 DOI: 10.3389/fonc.2021.772663] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND AND PURPOSE Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT). MATERIALS AND METHODS Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1. RESULTS Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation). DISCUSSION AND CONCLUSION The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Federica Bellerba
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Riccardo Santamaria
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Sara Gandini
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria Cristina Leonardi
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Identifying Symptom Clusters Through Association Rule Mining. ARTIFICIAL INTELLIGENCE IN MEDICINE. CONFERENCE ON ARTIFICIAL INTELLIGENCE IN MEDICINE (2005- ) 2021. [PMID: 34541584 DOI: 10.1007/978-3-030-77211-6_58] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Cancer patients experience many symptoms throughout their cancer treatment and sometimes suffer from lasting effects post-treatment. Patient-Reported Outcome (PRO) surveys provide a means for monitoring the patient's symptoms during and after treatment. Symptom cluster (SC) research seeks to understand these symptoms and their relationships to define new treatment and disease management methods to improve patient's quality of life. This paper introduces association rule mining (ARM) as a novel alternative for identifying symptom clusters. We compare the results to prior research and find that while some of the SCs are similar, ARM uncovers more nuanced relationships between symptoms such as anchor symptoms that serve as connections between interference and cancer-specific symptoms.
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Wentzel A, Luciani T, van Dijk LV, Taku N, Elgohari B, Mohamed ASR, Canahuate G, Fuller CD, Vock DM, Elisabeta Marai G. Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas. Radiother Oncol 2021; 161:152-158. [PMID: 34126138 PMCID: PMC8479634 DOI: 10.1016/j.radonc.2021.06.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 05/18/2021] [Accepted: 06/05/2021] [Indexed: 11/18/2022]
Abstract
PURPOSE To determine whether patient similarity in terms of head and neck cancer spread through lymph nodes correlates significantly with radiation-associated toxicity. MATERIALS AND METHODS 582 head and neck cancer patients received radiotherapy for oropharyngeal cancer (OPC) and had non-metastatic affected lymph nodes in the head and neck. Affected lymph nodes were segmented from pretreatment contrast-enhanced tomography scans and categorized according to consensus guidelines. Similar patients were clustered into 4 groups according to a graph-based representation of disease spread through affected lymph nodes. Correlation between dysphagia-associated symptoms and patient groups was calculated. RESULTS Out of 582 patients, 26% (152) experienced toxicity during a follow up evaluation 6 months after completion of radiotherapy treatment. Patient groups identified by our approach were significantly correlated with dysphagia, feeding tube, and aspiration toxicity (p < .0005). DISCUSSION Our results suggest that structural geometry-aware characterization of affected lymph nodes can be used to better predict radiation-associated dysphagia at time of diagnosis, and better inform treatment guidelines. CONCLUSION Our work successfully stratified a patient cohort into similar groups using a structural geometry, graph-encoding of affected lymph nodes in oropharyngeal cancer patients, that were predictive of late radiation-associated dysphagia and toxicity.
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Affiliation(s)
- Andrew Wentzel
- Department of Computer Science, The University of Illinois at Chicago, Chicago, USA.
| | - Timothy Luciani
- Department of Computer Science, The University of Illinois at Chicago, Chicago, USA
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicolette Taku
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Clinical Oncology and Nuclear Medicine, Mansoura University, Mansoura, Egypt
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - David M Vock
- Division of Biostatistics, University of Minnesota, Minneapolis, USA
| | - G Elisabeta Marai
- Department of Computer Science, The University of Illinois at Chicago, Chicago, USA.
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10
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Wang Y, Van Dijk L, Mohamed ASR, Fuller CD, Zhang X, Marai GE, Canahuate G. Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes. PROCEEDINGS. INTERNATIONAL DATABASE ENGINEERING AND APPLICATIONS SYMPOSIUM 2021; 2021:273-279. [PMID: 35392138 PMCID: PMC8982996 DOI: 10.1145/3472163.3472177] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Patient-Reported Outcome (PRO) surveys are used to monitor patients' symptoms during and after cancer treatment. Acute symptoms refer to those experienced during treatment and late symptoms refer to those experienced after treatment. While most patients experience severe symptoms during treatment, these usually subside in the late stage. However, for some patients, late toxicities persist negatively affecting the patient's quality of life (QoL). In the case of head and neck cancer patients, PRO surveys are recorded every week during the patient's visit to the clinic and at different follow-up times after the treatment has concluded. In this paper, we model the PRO data as a time-series and apply Long-Short Term Memory (LSTM) neural networks for predicting symptom severity in the late stage. The PRO data used in this project corresponds to MD Anderson Symptom Inventory (MDASI) questionnaires collected from head and neck cancer patients treated at the MD Anderson Cancer Center. We show that the LSTM model is effective in predicting symptom ratings under the RMSE and NRMSE metrics. Our experiments show that the LSTM model also outperforms other machine learning models and time-series prediction models for these data.
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Affiliation(s)
- Yaohua Wang
- Electrical and Computer Engineering University of Iowa
| | | | | | | | - Xinhua Zhang
- Computer Science University of Illinois at Chicago
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11
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Pu D, Lee VHF, Chan KMK, Yuen MTY, Quon H, Tsang RKY. The Relationships Between Radiation Dosage and Long-term Swallowing Kinematics and Timing in Nasopharyngeal Carcinoma Survivors. Dysphagia 2021; 37:612-621. [PMID: 33909131 PMCID: PMC9072442 DOI: 10.1007/s00455-021-10311-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 04/20/2021] [Indexed: 11/16/2022]
Abstract
This study aimed to investigate the relationship between intensity-modulated radiation therapy (IMRT) dosimetry and swallowing kinematic and timing measures. Thirteen kinematic and timing measures of swallowing from videofluoroscopic analysis were used as outcome measures to reflect swallowing function. IMRT dosimetry was accessed for thirteen swallowing-related structures. A cohort of 44 nasopharyngeal carcinoma (NPC) survivors at least 3 years post-IMRT were recruited. The cohort had a mean age of 53.2 ± 11.9 years, 77.3% of whom were male. There was an average of 68.24 ± 14.15 months since end of IMRT; 41 (93.2%) had undergone concurrent chemotherapy. For displacement measures, female sex and higher doses to the cricopharyngeus, glottic larynx, and base of tongue were associated with reduced hyolaryngeal excursion and pharyngeal constriction, and more residue. For timing measures, higher dose to the genioglossus was associated with reduced processing time at all stages of the swallow. The inferior pharyngeal constrictor emerged with a distinctly different pattern of association with mean radiation dosage compared to other structures. Greater changes to swallowing kinematics and timing were observed for pudding thick consistency than thin liquid. Increasing radiation dosage to swallowing-related structures is associated with reduced swallowing kinematics. However, not all structures are affected the same way, therefore organ sparing during treatment planning for IMRT needs to consider function rather than focusing on select muscles. Dose-response relationships should be investigated with a comprehensive set of swallowing structures to capture the holistic process of swallowing.
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Affiliation(s)
- Dai Pu
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Swallowing Research Laboratory, Faculty of Education, The University of Hong Kong, Hong Kong, China.,School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Frankston, Australia
| | - Victor H F Lee
- Department of Clinical Oncology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Karen M K Chan
- Swallowing Research Laboratory, Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Margaret T Y Yuen
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Oncology and Otolaryngology and Head and Neck Surgery, Johns Hopkins University, Baltimore, USA
| | - Raymond K Y Tsang
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China. .,Department of Surgery and Department of ENT, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong, China.
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12
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Ilmarinen T, Hammarstedt-Nordenvall L, Bäck L, Mäkitie A. Enteral tube feeding of head and neck cancer patients undergoing definitive chemoradiotherapy in the Nordic Countries: Survey of the Scandinavian Society for Head and Neck Oncology. Eur Arch Otorhinolaryngol 2021; 278:3489-3496. [PMID: 33389006 DOI: 10.1007/s00405-020-06545-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/01/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE We assessed current policies and perceptions of enteral tube feeding in head and neck cancer (HNC) patients undergoing chemoradiotherapy (CRT). A web-based survey was used to compare the timing and method of tube feeding within and between the five Nordic countries, covering a population of 27 million. METHODS From each of the 21 Nordic university hospitals, one oncologist and one otorhinolaryngology-head and neck (ORL-HN) surgeon responded to a survey. The respondents were asked whether tube insertion before the onset of CRT, or during CRT as required (prophylactic vs. reactive) was preferred. The use of a pretreatment nutritional screening tool and the choice of feeding route (nasogastric vs. gastrostomy tube) were assessed. In total, we analyzed responses from 21 oncologists and 21 ORL-HN surgeons. RESULTS A tendency was observed towards decreasing the use of a PEG tube. Of the 21 university hospitals, only 2 (10%) reported using a prophylactic PEG tube in over half of HNC patients undergoing definitive CRT. The preferred method for reactive tube feeding was by a NG tube in 14 of 21 (67%), and by a PEG in 7 of 21 (33%). In general, both oncologists and ORL-HN surgeons were content with their current policy. CONCLUSIONS The practices for enteral tube feeding in HNC management vary within and between the Nordic countries. We suggest that unified protocols for tube feeding should be developed for this patient population.
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Affiliation(s)
- Taru Ilmarinen
- Department of Otorhinolaryngology, Head and Neck Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
| | - Lalle Hammarstedt-Nordenvall
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Leif Bäck
- Department of Otorhinolaryngology, Head and Neck Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Antti Mäkitie
- Department of Otorhinolaryngology, Head and Neck Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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13
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Mierzwa ML, Gharzai LA, Li P, Wilkie JR, Hawkins PG, Aryal MP, Lee C, Rosen B, Lyden T, Blakely A, Chapman CH, Thamarus J, Schonewolf C, Shah J, Eisbruch A, Schipper MJ, Cao Y. Early MRI Blood Volume Changes in Constrictor Muscles Correlate With Postradiation Dysphagia. Int J Radiat Oncol Biol Phys 2020; 110:566-573. [PMID: 33346093 DOI: 10.1016/j.ijrobp.2020.12.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 12/01/2020] [Accepted: 12/13/2020] [Indexed: 12/01/2022]
Abstract
PURPOSE Predicting individual patient sensitivity to radiation therapy (RT) for tumor control or normal tissue toxicity is necessary to individualize treatment planning. In head and neck cancer, radiation doses are limited by many nearby critical structures, including structures involved in swallowing. Previous efforts showed that imaging parameters correlate with RT dose; here, we investigate the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) blood volume (BV) changes in predicting dysphagia. METHODS AND MATERIALS This study included 32 patients with locally advanced oropharyngeal squamous cell carcinoma treated with definitive chemoradiation on an institutional protocol incorporating baseline and early midtreatment DCE-MRI. BV maps of the pharyngeal constrictor muscles (PCM) were created, and BV increases midtreatment were correlated with the following parameters at 3 and 12 months post-RT: RT dose, Dynamic Imaging Grade of Swallowing Toxicity swallow score, aspiration frequency, European Organisation for Research and Treatment of Cancer HN35 patient-reported outcomes, physician-reported dysphagia, and feeding tube (FT) dependence. RESULTS The mean BV to the PCMs increased from baseline to fraction 10, which was significant for the superior PCM (P = .006) and middle PCM (P < .001), with a trend in the inferior PCM where lower mean doses were seen (P = .077). The factors associated with FT dependence at 3 months included BV increases in the total PCM (correlation, 0.48; P = .006) and middle PCM (correlation, 0.50; P = .004). A post-RT increase in aspiration was associated with a BV increase in the superior PCM (correlation, 0.44; P = .013),and the increase in the total PCMs was marginally significant (correlation, 0.34; P = .06). The best-performing models of FT dependence (area under the receiver operating curve [AUC] = 0.84) and aspiration increases (AUC = 0.78) included BV increases as well as a mean RT dose to middle PCM. CONCLUSIONS Our results suggest that midtreatment BV increases derived from DCE-MRI are an early predictor of dysphagia. Further investigation of these promising imaging markers to assess individual patient sensitivity to treatment and the patient's subsequent risk of toxicities is warranted to improve personalization of RT planning.
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Affiliation(s)
- Michelle L Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
| | - Laila A Gharzai
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Pin Li
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Joel R Wilkie
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - Madhava P Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Teresa Lyden
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Anna Blakely
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
| | - Christina H Chapman
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Jennifer Thamarus
- Department of Speech and Language Pathology, Veterans Affairs Hospital, Ann Arbor, Michigan
| | - Caitlin Schonewolf
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Jennifer Shah
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Avraham Eisbruch
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Matthew J Schipper
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
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14
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Oliveira TB, Mesía R, Falco A, Hsieh JCH, Yokota T, Saada-Bouzid E, Schmitz S, Elicin O, Giacomelli L, Bossi P. Defining the needs of patients with recurrent and/or metastatic head and neck cancer: An expert opinion. Crit Rev Oncol Hematol 2020; 157:103200. [PMID: 33321152 DOI: 10.1016/j.critrevonc.2020.103200] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 12/03/2020] [Accepted: 12/04/2020] [Indexed: 02/08/2023] Open
Abstract
The clinical and biological heterogeneity of head and neck cancer (HNC) is paralleled by a plethora of different symptoms that affect the patient's quality of life. These symptoms include, for instance, pain, fatigue, nutritional issues, airways obstruction, voice alterations and psychological distress. In addition, patients with HNC are prone to a high risk of infection, and may also suffer from acute complications, such as hypercalcemia, spine compression by bone metastasis or bleeding. Prolonging survival is also an inherent expectation for all patients. Addressing the above needs is crucial in all patients with HNC, and especially in those with recurrent and/or metastatic (RM) disease. However, research on how to address patients' needs in RM-HNC remains scarce. This paper defines patients' needs for RM HNC and presents an Expert Opinion on how to address them, proposing also some lines of research.
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Affiliation(s)
| | - Ricard Mesía
- Medical Oncology Department, Catalan Institute of Oncology - Badalona, B-ARGO Group, IGTP, Barcelona, Spain
| | - Agustin Falco
- Medical Oncology Department, Instituto Alexander Fleming, Buenos Aires, Argentina
| | - Jason Chia-Hsun Hsieh
- Division of Hematology-Oncology, Department of Internal Medicine, New Taipei Municipal TuCheng Hospital, Chang Gung Memorial Hospital at Linkou and Chang Gung University, Taoyuan, 333, Taiwan
| | - Tomoya Yokota
- Division of Gastrointestinal Oncology, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi, Sunto-gun, Shizuoka, 411-8777, Japan
| | - Esma Saada-Bouzid
- Medical Oncology Department, Centre Antoine Lacassagne, Nice, France
| | - Sandra Schmitz
- Departments of Medical Oncology and Head and Neck Surgery, Institut Roi Albert II, Institut de Recherche Clinique et Expérimentale (Pole MIRO), Cliniques Universitaires Saint-Luc, Catholic University of Louvain, Woluwe-Saint-Lambert, Belgium
| | - Olgun Elicin
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
| | - Luca Giacomelli
- Polistudium srl, Milan, Italy; Department of Surgical Sciences and Integrated Diangostics, University of Genoa, Genoa, Italy
| | - Paolo Bossi
- Medical Oncology, Department of Medical & Surgical Specialties, Radiological Sciences & Public Health, University of Brescia, ASST Spedali Civili, 25123, Brescia, Italy.
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15
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Dahlstrom KR, Song J, Thall PF, Fuller CD, Hutcheson KA, Johnson FM, Gunn GB, Phan J, Frank SJ, Morrison WH, Ferrarotto R, Rosenthal DI, Sturgis EM, Garden AS. Conditional survival among patients with oropharyngeal cancer treated with radiation therapy and alive without recurrence 5 years after diagnosis. Cancer 2020; 127:1228-1237. [PMID: 33306202 DOI: 10.1002/cncr.33370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 10/27/2020] [Accepted: 11/02/2020] [Indexed: 11/12/2022]
Abstract
BACKGROUND Risk of recurrence among patients with oropharyngeal cancer (OPC) who survive 5 years is low. The goal of this study was to assess long-term survival of patients with OPC alive without recurrence 5 years after diagnosis. METHODS This study included newly diagnosed patients with OPC, who had been treated with radiation and were alive without recurrence 5 years after diagnosis. Overall survival (OS) probabilities beyond 5 years were estimated using the Kaplan-Meier method. Factors associated with OS were determined using Bayesian piecewise exponential survival regression. Standardized mortality ratios for all-cause death were estimated controlling for study year, age, and sex in the US general population. RESULTS Among 1699 patients, the additional 2-year, 5-year, and 10-year OS probabilities were 94%, 83%, and 63%, respectively, and were lower than those in the general population. Patients who were older, were current or former smokers, had other than tonsil or base of tongue tumors, or had T4 tumors had a higher risk of death. Patients who had base of tongue tumors and had received intensity-modulated radiation therapy (IMRT) or lower-radiation doses had a lower risk of death. Standardized mortality ratios were higher among current and heavy smokers and lower among recipients of IMRT and lower radiation doses. CONCLUSIONS In this large cohort, long-term survival among patients with OPC was good but lower than predicted for the general population. Patients treated with IMRT and those with less tobacco exposure had better outcomes.
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Affiliation(s)
- Kristina R Dahlstrom
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Katherine A Hutcheson
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Faye M Johnson
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,University of Texas Graduate School of Biomedical Sciences, Houston, Texas
| | - G Brandon Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jack Phan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Steven J Frank
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - William H Morrison
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Renata Ferrarotto
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David I Rosenthal
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Erich M Sturgis
- Department of Otolaryngology-Head and Neck Surgery, Baylor College of Medicine, Houston, Texas
| | - Adam S Garden
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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16
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Yao CMKL, Hutcheson KA. Quality of Life Implications After Transoral Robotic Surgery for Oropharyngeal Cancers. Otolaryngol Clin North Am 2020; 53:1117-1129. [PMID: 32917421 DOI: 10.1016/j.otc.2020.07.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Oropharyngeal cancers and their treatment can exquisitely affect a patient's quality of life and functional outcome. Transoral robotic surgery offers a minimally invasive surgical approach that mitigates injury from traditional open surgical approaches and offers a treatment more likely to have short-term side effects compared with nonsurgical treatment. Feeding tube dependence, oral intake, and swallowing questionnaires, in addition to swallowing evaluations provide a snapshot of a patient's current swallowing function. Investigation of patient-reported quality-of-life outcomes allows for understanding of their symptomatology and the comparison of different treatment strategies.
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Affiliation(s)
- Christopher M K L Yao
- Advanced Head and Neck Surgical Oncology and Microvascular Reconstruction, Department of Head and Neck Surgery, The University of Texas at MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1445, Houston, TX 77030, USA
| | - Katherine A Hutcheson
- Department of Head and Neck Surgery, The University of Texas at MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1445, Houston, TX 77030, USA; Division of Radiation Oncology, The University of Texas at MD Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 1445, Houston, TX 77030, USA.
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17
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Kristensen MB, Wessel I, Beck AM, Dieperink KB, Mikkelsen TB, Møller JJK, Zwisler AD. Effects of a Multidisciplinary Residential Nutritional Rehabilitation Program in Head and Neck Cancer Survivors-Results from the NUTRI-HAB Randomized Controlled Trial. Nutrients 2020; 12:nu12072117. [PMID: 32708864 PMCID: PMC7400860 DOI: 10.3390/nu12072117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/26/2020] [Accepted: 07/14/2020] [Indexed: 12/24/2022] Open
Abstract
Head and neck cancer survivors frequently experience nutritional challenges, and proper rehabilitation should be offered. The trial objective was to test the effect of a multidisciplinary residential nutritional rehabilitation programme addressing physical, psychological, and social aspects of eating problems after treatment. In a randomized controlled trial, 71 head and neck cancer survivors recruited through a nationwide survey were randomized to the program or a wait-list control group. Inclusion was based on self-reported interest in participation. The primary outcome was change in body weight. Secondary outcomes included physical function, quality of life, and symptoms of anxiety and depression. Differences between groups at the 3-month follow-up were tested. No significant differences were seen in body weight change, but there were overall trends towards greater improvements in physical function (hand grip strength: p = 0.042; maximal mouth opening: p = 0.072) and quality of life ("Role functioning": p = 0.041; "Speech problems": p = 0.040; "Pain": p = 0.048) in the intervention group. To conclude, a multidisciplinary residential nutritional rehabilitation program had no effect on body weight in head and neck cancer survivors with self-reported interest in participation, but it may have effect on physical function and quality of life. Further research on relevant outcomes, inclusion criteria, and the program's effect in different subgroups is needed.
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Affiliation(s)
- Marianne Boll Kristensen
- Department of Nursing and Nutrition, University College Copenhagen, Sigurdsgade 26, 2200 Copenhagen N, Denmark;
- REHPA, The Danish Knowledge Centre for Rehabilitation and Palliative Care, Odense University Hospital, and Department of Clinical Research, University of Southern Denmark, Vestergade 17, 5800 Nyborg, Denmark; (T.B.M.); (J.-J.K.M.); (A.-D.Z.)
- OPEN, Odense Patient data Explorative Network, Odense University Hospital, J.B. Winsløws Vej 9A, 5000 Odense C, Denmark
- Correspondence: ; Tel.: +45-2429-6329
| | - Irene Wessel
- Department of Otorhinolaryngology, Head and Neck Surgery & Audiology, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark;
| | - Anne Marie Beck
- Department of Nursing and Nutrition, University College Copenhagen, Sigurdsgade 26, 2200 Copenhagen N, Denmark;
- Dietetics and Clinical Nutrition Research Unit, Herlev and Gentofte Hospital, Borgmester Ib Juuls Vej 50, 4. 2730 Herlev, Denmark
| | - Karin B. Dieperink
- Research Unit of Oncology, Department of Oncology, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark;
- Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19.3, DK-5000 Odense C, Denmark
| | - Tina Broby Mikkelsen
- REHPA, The Danish Knowledge Centre for Rehabilitation and Palliative Care, Odense University Hospital, and Department of Clinical Research, University of Southern Denmark, Vestergade 17, 5800 Nyborg, Denmark; (T.B.M.); (J.-J.K.M.); (A.-D.Z.)
| | - Jens-Jakob Kjer Møller
- REHPA, The Danish Knowledge Centre for Rehabilitation and Palliative Care, Odense University Hospital, and Department of Clinical Research, University of Southern Denmark, Vestergade 17, 5800 Nyborg, Denmark; (T.B.M.); (J.-J.K.M.); (A.-D.Z.)
| | - Ann-Dorthe Zwisler
- REHPA, The Danish Knowledge Centre for Rehabilitation and Palliative Care, Odense University Hospital, and Department of Clinical Research, University of Southern Denmark, Vestergade 17, 5800 Nyborg, Denmark; (T.B.M.); (J.-J.K.M.); (A.-D.Z.)
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18
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Wentzel A, Hanula P, van Dijk LV, Elgohari B, Mohamed ASR, Cardenas CE, Fuller CD, Vock DM, Canahuate G, Marai GE. Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy. Radiother Oncol 2020; 148:245-251. [PMID: 32422303 DOI: 10.1016/j.radonc.2020.05.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 05/12/2020] [Accepted: 05/12/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE Using a 200 Head and Neck cancer (HNC) patient cohort, we employ patient similarity based on tumor location, volume, and proximity to organs at risk to predict radiation-associated dysphagia (RAD) in a new patient receiving intensity modulated radiation therapy (IMRT). MATERIAL AND METHODS All patients were treated using curative-intent IMRT. Anatomical features were extracted from contrast-enhanced tomography scans acquired pre-treatment. Patient similarity was computed using a topological similarity measure, which allowed for the prediction of normal tissues' mean doses. We performed feature selection and clustering, and used the resulting groups of patients to forecast RAD. We used Logistic Regression (LG) cross-validation to assess the potential toxicity risk of these groupings. RESULTS Out of 200 patients, 34 patients were recorded as having RAD. Patient clusters were significantly correlated with RAD (p < .0001). The area under the receiver-operator curve (AUC) using pre-established, baseline features gave a predictive accuracy of 0.79, while the addition of our cluster labels improved accuracy to 0.84. CONCLUSION Our results show that spatial information available pre-treatment can be used to robustly identify groups of RAD high-risk patients. We identify feature sets that considerably improve toxicity risk prediction beyond what is possible using baseline features. Our results also suggest that similarity-based predicted mean doses to organs can be used as valid predictors of risk to organs.
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Affiliation(s)
- Andrew Wentzel
- Department of Computer Science, The University of Illinois at Chicago, Chicago, USA.
| | - Peter Hanula
- Department of Computer Science, The University of Illinois at Chicago, Chicago, USA
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Clinical Oncology and Nuclear Medicine, Mansoura University, Egypt
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - David M Vock
- Division of Biostatistics, University of Minnesota, Minneapolis, USA
| | - Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USA
| | - G E Marai
- Department of Computer Science, The University of Illinois at Chicago, Chicago, USA.
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19
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Tong J, Hei TK. Aging and age-related health effects of ionizing radiation. RADIATION MEDICINE AND PROTECTION 2020. [DOI: 10.1016/j.radmp.2020.01.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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20
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Takayama K, Kato T, Nakamura T, Azami Y, Ono T, Suzuki M, Takada A, Yamaguchi H, Seto I, Nakasato T, Wada H, Kikuchi Y, Mitsudo K, Fuwa N, Murakami M. Proton Beam Therapy Combined with Intra-Arterial Infusion Chemotherapy for Stage IV Adenoid Cystic Carcinoma of the Base of the Tongue. Cancers (Basel) 2019; 11:cancers11101413. [PMID: 31546728 PMCID: PMC6826952 DOI: 10.3390/cancers11101413] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 09/12/2019] [Accepted: 09/20/2019] [Indexed: 01/06/2023] Open
Abstract
Adenoid cystic carcinoma (ACC) is a very rare epithelial tumor of the salivary glands. Surgical resection is considered to be a standard therapy. However, the optimal treatment strategy for managing advanced cases has not yet been established. This study evaluated the efficacy and toxicity of proton beam therapy (PBT) combined with selective intra-arterial infusion chemotherapy (IAIC) using weekly cisplatin for locally advanced ACC of the base of the tongue. Between March 2009 and February 2018, 15 patients were treated. The median follow-up duration was 56 (range: 15–116) months. The 5-year local control and overall survival rates were 89% and 76%, respectively. With regard to late toxicities, grade 2 osteoradionecrosis was found in one patient and grade 5 pharyngeal necrosis was observed in one patient. Considering most cases were significantly advanced and inoperable, this therapy was effective in controlling the primary tumor, preserving function and maintaining the quality of life. Although improvements are needed to reduce adverse events, PBT in combination with IAIC can be a treatment option for locally advanced ACC of the base of the tongue.
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Affiliation(s)
- Kanako Takayama
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
- Department of Oral and Maxillofacial Surgery, Yokohama City University Graduate School of Medicine, 3-9, Fukuura, Kanazawa, Yokohama, Kanagawa 236-0004, Japan.
| | - Takahiro Kato
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Tatsuya Nakamura
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Yusuke Azami
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Takashi Ono
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Motohisa Suzuki
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Akinori Takada
- Department of Radiation Oncology, Mie University Hospital, 2-174, Edobashi Tsu, Mie 514-8507, Japan.
| | - Hisashi Yamaguchi
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Ichiro Seto
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Tatsuhiko Nakasato
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Hitoshi Wada
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Yasuhiro Kikuchi
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
| | - Kenji Mitsudo
- Department of Oral and Maxillofacial Surgery, Yokohama City University Graduate School of Medicine, 3-9, Fukuura, Kanazawa, Yokohama, Kanagawa 236-0004, Japan.
| | - Nobukazu Fuwa
- Department of Radiation Oncology, Ise red cross hospital, 1-471-2, Funae, Ise, Mie 516-8512, Japan.
| | - Masao Murakami
- Department of Radiation Oncology, Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Koriyama, Fukushima 963-8052, Japan.
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