Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

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.

Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas

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.

Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients

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.

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

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.

Surgical Treatment for Advanced Oropharyngeal Cancer: A Narrative Review

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.

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

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.

Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist

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.

Identifying Symptom Clusters Through Association Rule Mining

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.

Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas

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.

Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes

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.

The Relationships Between Radiation Dosage and Long-term Swallowing Kinematics and Timing in Nasopharyngeal Carcinoma Survivors

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.

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

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.

Early MRI Blood Volume Changes in Constrictor Muscles Correlate With Postradiation Dysphagia

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.

Defining the needs of patients with recurrent and/or metastatic head and neck cancer: An expert opinion

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.

Conditional survival among patients with oropharyngeal cancer treated with radiation therapy and alive without recurrence 5 years after diagnosis

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.

Quality of Life Implications After Transoral Robotic Surgery for Oropharyngeal Cancers

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.

Effects of a Multidisciplinary Residential Nutritional Rehabilitation Program in Head and Neck Cancer Survivors-Results from the NUTRI-HAB Randomized Controlled Trial

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.

Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy

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.

Proton Beam Therapy Combined with Intra-Arterial Infusion Chemotherapy for Stage IV Adenoid Cystic Carcinoma of the Base of the Tongue

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.