Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies.
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ABSTRACT:
SUBMITTER: Tran WT
PROVIDER: S-EPMC7668124 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
Publications
Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies.
Tran William T WT Suraweera Harini H Quiaoit Karina K DiCenzo Daniel D Fatima Kashuf K Jang Deok D Bhardwaj Divya D Kolios Christopher C Karam Irene I Poon Ian I Sannachi Lakshmanan L Gangeh Mehrdad M Sadeghi-Naini Ali A Dasgupta Archya A Czarnota Gregory J GJ
Future science OA 20200904 9
<h4>Aim</h4>We investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT).<h4>Materials & methods</h4>QUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were c ...[more]
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Project description:PurposeThis study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC).MethodsFifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation.ResultsThe median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment.ConclusionQUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
| S-EPMC7985224 | biostudies-literature
Project description:This prospective study was conducted to investigate the role of quantitative ultrasound (QUS) radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). The most prominent cervical lymph node (LN) was scanned with a clinical ultrasound device having central frequency of 6.5 MHz. Ultrasound radiofrequency data were processed to obtain 7 QUS parameters. Color-coded parametric maps were generated based on individual QUS spectral features corresponding to each of the smaller units. A total of 31 (7 primary QUS and 24 texture) features were obtained before treatment. All patients were treated with radical RT and followed according to standard institutional practice. Recurrence (local, regional, or distant) served as an endpoint. Three different machine learning classifiers with a set of maximally three features were used for model development and tested with leave-one-out cross-validation for nonrecurrence and recurrence groups. Fifty-one patients were included, with a median follow up of 38 months (range 7-64 months). Recurrence was observed in 17 patients. The best results were obtained using a k-nearest neighbor (KNN) classifier with a sensitivity, specificity, accuracy, and an area under curve of 76%, 71%, 75%, and 0.74, respectively. All the three features selected for the KNN model were texture features. The KNN-model-predicted 3-year recurrence-free survival was 81% and 40% in the predicted no-recurrence and predicted-recurrence groups, respectively. (p = 0.001). The pilot study demonstrates pretreatment QUS-radiomics can predict the recurrence group with an accuracy of 75% in patients with node-positive HNSCC. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
| S-EPMC8026932 | biostudies-literature
Project description:Tumor hypoxia is associated with radiation resistance and can be longitudinally monitored by 18F-fluoromisonidazole (18F-FMISO)-PET/CT. Our study aimed at evaluating radiomics dynamics of 18F-FMISO-hypoxia imaging during chemo-radiotherapy (CRT) as predictors for treatment outcome in head-and-neck squamous cell carcinoma (HNSCC) patients. We prospectively recruited 35 HNSCC patients undergoing definitive CRT and longitudinal 18F-FMISO-PET/CT scans at weeks 0, 2 and 5 (W0/W2/W5). Patients were classified based on peritherapeutic variations of the hypoxic sub-volume (HSV) size (increasing/stable/decreasing) and location (geographically-static/geographically-dynamic) by a new objective classification parameter (CP) accounting for spatial overlap. Additionally, 130 radiomic features (RF) were extracted from HSV at W0, and their variations during CRT were quantified by relative deviations (∆RF). Prediction of treatment outcome was considered statistically relevant after being corrected for multiple testing and confirmed for the two 18F-FMISO-PET/CT time-points and for a validation cohort. HSV decreased in 64% of patients at W2 and in 80% at W5. CP distinguished earlier disease progression (geographically-dynamic) from later disease progression (geographically-static) in both time-points and cohorts. The texture feature low grey-level zone emphasis predicted local recurrence with AUCW2 = 0.82 and AUCW5 = 0.81 in initial cohort (N = 25) and AUCW2 = 0.79 and AUCW5 = 0.80 in validation cohort. Radiomics analysis of 18F-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT.
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Project description:Second primary malignancy (SPM) may occur after index head and neck cancer (HNC) treatment. This study evaluated the prevalence and outcome of SPM in patients with HNC treated with definitive radiotherapy. Eligible patients include those with index mucosal HNC treated with definitive radiotherapy between 2000 and 2010. SPM was defined as an invasive cancer at a noncontiguous site diagnosed at least 6 months after completion of radiotherapy. Clinical data were collected, and the Kaplan-Meier method was used to estimate overall survival. In total, 1512 patients were studied. The majority of patients had index oropharyngeal cancer (86%). In all, 130 (9%) patients developed a SPM. The risk of SPM increased exponentially with time with 5-, 10-, and 15-year rates of 4, 10, and 25%. Half of SPMs were within the head and neck or thoracic regions. SPM rates were significantly higher (p < 0.0001) in current smokers and former smokers than never smokers with 5-, 10-, and 15-year risk being: never smoker (2, 4, 14%), former smokers with <10-pack year (5, 10, 23%), former smokers with ≥10-pack year (5, 14, 35%), and current smokers (6, 18, 32%). In total, 102 (78%) had subsequent curative-intent therapy. The 5-year overall survival from SPM was 44%. The majority of SPMs were in those with significant smoking history reflecting the same risk factor as for the index mucosal HNC. Nearly one in two patients with SPMs were salvaged underscoring the importance of regular surveillance for SPMs.
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Project description:Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)-is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coordinates, necessitating adaptive radiotherapy (ART). We developed a deep learning-based radiomics (DLR) approach to predict early head and neck tumor regression and thereby facilitate ART. Primary gross tumor volume (GTVp) was monitored in 96 patients and nodal GTV (GTVn) in 79 patients during treatment. All patients underwent two computed tomography (CT) scans: one before the start of radiotherapy for initial planning and one during radiotherapy for boost planning. Patients were assigned to regression and nonregression groups according to their median tumor regression rate (ΔGTV/treatment day from initial to boost CT scan). We input a GTV image into the convolutional neural network model, which was pretrained using natural image datasets, via transfer learning. The deep features were extracted from the last fully connected layer. To clarify the prognostic power of the deep features, machine learning models were trained. The models then predicted the regression and nonregression of GTVp and GTVn and evaluated the predictive performance by 0.632 + bootstrap area under the curve (AUC). Predictive performance for GTVp regression was highest using the InceptionResNetv2 model (mean AUC = 0.75) and that for GTVn was highest using NASNetLarge (mean AUC = 0.73). Both models outperformed the handcrafted radiomics features (mean AUC = 0.63 for GTVp and 0.61 for GTVn) or clinical factors (0.64 and 0.67, respectively). DLR may facilitate ART for improved radiation side-effects and target coverage.
| S-EPMC9142601 | biostudies-literature
Project description:PurposeThe goal of this multicenter retrospective study of patients with head and neck malignancies was to evaluate the efficacy and safety of carbon-ion (C-ion) radiotherapy (RT) after photon RT.MethodsWe enrolled 56 patients with head and neck malignancies who underwent re-irradiation (re-RT) using C-ions between November 2003 and March 2019, treated previously with photon RT. The tumors at re-RT were located in the sinonasal cavities (n = 20, 35.7%), skull base (n = 12, 21.4%), and orbit (n = 7, 12.5%). The tumors at the initial RT were located in the sinonasal cavities (n = 13, 23.2%), skull base (n = 9, 16.1%), and orbit (n = 9, 16.1%). The median period between the initial RT and re-RT was 41 (4-568) months. The most common histology of re-RT was squamous cell carcinoma (n = 11, 19.6%). The most commonly used protocol was 57.6 Gy (relative biological effectiveness) in 16 fractions (n = 23, 41.1%). Surgery preceded re-RT in three patients (5.4%). One patient with malignant melanoma received concurrent chemotherapy.ResultsThe 2-year local control, progression-free survival, and overall survival rates were 66.5%, 36.9%, and 67.9%, respectively. The median follow-up time was 28 months. Two patients (3.6%) developed grade ≥ 3 acute toxicities, and 14 (25.0%) developed grade ≥ 3 late toxicities. A single patient had confirmed grade 5 dermatitis with infection.ConclusionRe-RT using C-ions for head and neck malignancies after photon RT is an effective treatment with tolerable toxicity.
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Project description:Background and purposeReduced toxicity while maintaining loco-regional control rates have been reported after reducing planning target volume (PTV) margins for head-and-neck radiotherapy (HNRT). In this context, quantifying anatomical changes to monitor patient treatment is preferred. This retrospective feasibility study investigated the application of deformable image registration (DIR) and Exponentially Weighted Moving Average (EWMA) Statistical Process Control (SPC) charts for this purpose.Materials and methodsDIR between the computed tomography for treatment planning (pCT) images of twelve patients and their daily on-treatment cone beam computed tomography (CBCT) images quantified anatomical changes during treatment. EWMA charts investigated corresponding trends. Uncertainty analysis provided 90% confidence limits which were used to confirm whether a trend previously breached a threshold.ResultsTrends in patient positioning reproducibility occurred before the end of treatment week four in 54% of cases. Using SPC process limits, only 24% of these were confirmed at a 90% confidence level before the end of treatment. Using an a priori clinical limit of 2 mm, absolute changes in patient pose were detected in 39% of cases, of which 82% were confirmed. Soft tissue trends outside SPC process limits occurring before the end of treatment week four were confirmed in 90% of cases.ConclusionStructure specific action thresholds enabled detection of systematic anatomical changes during the first four weeks of treatment. Investigation of the dosimetric impact of the observed deviations is needed to show the efficacy of SPC to timely indicate required treatment adaptation and provide a safety net for PTV margin reduction.
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Project description:Background and purposeHypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET.Materials and methodsTwo independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [18F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures.Secondly, a pre-defined [18F]FMISO-PET tumour-to-muscle-ratio (TMRpeak ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [18F]FMISO-PET was performed using area-under-the-curve (AUC) and Kaplan-Meier analysis including log-rank test.ResultsThe best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [18F]FMISO TMRpeak reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [18F]FMISO-PET significant differences were observed (p = 0.02).ConclusionsNo direct correlation of patient stratification using [18F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients.
| S-EPMC7536307 | biostudies-literature
Project description:The response of the major salivary glands, the parotid glands, to radiation dose is patient-specific. This study was designed to investigate whether parotid gland changes seen in weekly CT during treatment, quantified by delta-radiomics features (Δfeatures), could improve the prediction of moderate-to-severe xerostomia at 12 months after radiotherapy (Xer12m). Parotid gland Δfeatures were extracted from in total 68 planning and 340 weekly CTs, representing geometric, intensity and texture characteristics. Bootstrapped forward variable selection was performed to identify the best predictors of Xer12m. The predictive contribution of the resulting Δfeatures to a pre-treatment reference model, based on contralateral parotid gland mean dose and baseline xerostomia scores (Xerbaseline) only, was evaluated. Xer12m was reported by 26 (38%) of the 68 patients included. The most predictive Δfeature was the contralateral parotid gland surface change, which was significantly associated with Xer12m for all weeks (p < 0.04), but performed best for week 3 (ΔPG-surfacew3; p < 0.001). Moreover, ∆PG-surfacew3 showed a significant predictive contribution in addition to the pre-treatment reference model (likelihood-ratio test; p = 0.003), resulting in a significantly better model performance (AUCtrain = 0.92; AUCtest = 0.93) compared to that of the pre-treatment model (AUCtrain = 0.82; AUCtest = 0.82). These results suggest that mid-treatment parotid gland changes substantially improve the prediction of late radiation-induced xerostomia.
| S-EPMC6713775 | biostudies-literature
Project description:BACKGROUND:Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. METHODS:Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome. RESULTS:Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). CONCLUSIONS:Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. TRIAL REGISTRATION:Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.
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