Diagnostic reliability of low dose multidetector computed tomography and cone beam computed tomography in maxillofacial trauma - an experimental blinded and randomized study

OBJECTIVE: To assess the diagnostic reliability of low dose Multidetector Computed Tomography (MDCT) and Cone Beam Computed Tomography (CBCT) for zygomaticomaxillary fracture diagnosis. METHODS: Unilateral zygomaticomaxillary fractures were inflicted on four out of six fresh frozen human cadaver head specimens. All specimens were scanned using four MDCT and two CBCT imaging protocols of which the radiation exposure was systematically reduced. A blinded diagnostic routine was simulated at which 16 radiologists and 8 oral and maxillofacial (OMF) surgeons performed randomized image assessments. We considered the findings during an open operative approach of the zygomatic region as the gold standard. RESULTS: Zygomaticomaxillary fractures were correctly diagnosed in 90.3% (n = 130) of the image assessments. The zygomatic arch was most often correctly diagnosed (91.0%). The zygomatic alveolar crest showed the lowest degree of correct diagnosis (65.3%). Dose reduction did not significantly affect the objective visualization of fractures of the zygomaticomaxillary complex. The sensitivity and specificity also remained consistent among the low dose scan protocols. Dose reduction did not decrease the ability to assess dislocation, comminution, orbital volume, volume rendering and soft tissues. OMF surgeons considered the low dose protocols sufficient for treatment planning. CONCLUSION: Dose reduction did not decrease the diagnostic reliability of MDCT and CBCT for the diagnosis of zygomaticomaxillary fractures

Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images

One of the primary treatment options for head and neck cancer is (chemo)radiation. Accurate delineation of the contour of the tumors is of great importance in the successful treatment of the tumor and in the prediction of patient outcomes. With this paper we take part in the HECKTOR 2021 challenge and we propose our methods for automatic tumor segmentation on PET and CT images of oropharyngeal cancer patients. To achieve this goal, we investigated different deep learning methods with the purpose of highlighting relevant image and modality related features, to refine the contour of the primary tumor. More specifically, we tested a Co-learning method [1] and a 3D Skip Spatial and Channel Squeeze and Excitation Multi-Scale Attention method (Skip-scSE-M), on the challenge dataset. The best results achieved on the test set were 0.762 mean Dice Similarity Score and 3.143 median of the Hausdorf Distance at 95 %.</p

Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high- or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. The proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. The whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. The C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints.</p

Towards reference values of pericoronary adipose tissue attenuation:impact of coronary artery and tube voltage in coronary computed tomography angiography

Objectives: To determine normal pericoronary adipose tissue mean attenuation (PCATMA) values for left the anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) in patients without plaques on coronary CT angiography (cCTA), taking into account tube voltage influence. Methods: This retrospective study included 192 patients (76 (39.6%) men; median age 49 years (range, 19–79)) who underwent cCTA with third-generation dual-source CT for the suspicion of CAD between 2015 and 2017. We selected patients without plaque on cCTA. PCATMA was measured semi-automatically on cCTA images in the proximal segment of the three main coronary arteries with 10 mm length. Paired t-testing was used to compare PCATMA between combinations of two coronary arteries within each patient, and one-way ANOVA testing was used to compare PCATMA in different kV groups. Results: The overall mean ± standard deviation (SD) PCATMA was − 90.3 ± 11.1 HU. PCATMA in men was higher than that in women: − 88.5 ± 10.5 HU versus − 91.5 ± 11.3 HU (p = 0.001). PCATMA of LAD, LCX, and RCA was − 92.4 ± 11.6 HU, − 88.4 ± 9.9 HU, and − 90.2 ± 11.4 HU, respectively. Pairwise comparison of the arteries showed significant difference in PCATMA: LAD and LCX (p < 0.001), LAD and RCA (p = 0.009), LCX and RCA (p = 0.033). PCATMA of the 70 kV, 80 kV, 90 kV, 100 kV, and 120 kV groups was − 95.6 ± 9.6 HU, − 90.2 ± 11.5 HU, − 87.3 ± 9.9 HU, − 82.7 ± 6.2 HU, and − 79.3 ± 6.8 HU, respectively (p < 0.001). Conclusions: In patients without plaque on cCTA, PCATMA varied by tube voltage, with minor differences in PCATMA between coronary arteries (LAD, LCX, RCA). PCATMA values need to be interpreted taking into account tube voltage setting. Key Points: • In patients without plaque on cCTA, PCATMAdiffers slightly by coronary artery (LAD, LCX, RCA). • Tube voltage of cCTA affects PCATMAmeasurement, with mean PCATMAincreasing linearly with increasing kV. • For longitudinal cCTA analysis of PCATMA, the use of equal kV setting is strongly recommended

Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA

Objectives To investigate the association of pericoronary adipose tissue mean attenuation (PCAT(MA)) with coronary artery disease (CAD) characteristics on coronary computed tomography angiography (CCTA). Methods We retrospectively investigated 165 symptomatic patients who underwent third-generation dual-source CCTA at 70kVp: 93 with and 72 without CAD (204 arteries with plaque, 291 without plaque). CCTA was evaluated for presence and characteristics of CAD per artery. PCAT(MA) was measured proximally and across the most severe stenosis. Patient-level, proximal PCAT(MA) was defined as the mean of the proximal PCAT(MA) of the three main coronary arteries. Analyses were performed on patient and vessel level. Results Mean proximal PCAT(MA) was -96.2 +/- 7.1 HU and -95.6 +/- 7.8HU for patients with and without CAD (p = 0.644). In arteries with plaque, proximal and lesion-specific PCAT(MA) was similar (-96.1 +/- 9.6 HU, -95.9 +/- 11.2 HU, p = 0.608). Lesion-specific PCAT(MA) of arteries with plaque (-94.7 HU) differed from proximal PCAT(MA) of arteries without plaque (-97.2 HU, p = 0.015). Minimal stenosis showed higher lesion-specific PCAT(MA) (-94.0 HU) than severe stenosis (-98.5 HU, p = 0.030). Lesion-specific PCAT(MA) of non-calcified, mixed, and calcified plaque was -96.5 HU, -94.6 HU, and -89.9 HU (p = 0.004). Vessel-based total plaque, lipid-rich necrotic core, and calcified plaque burden showed a very weak to moderate correlation with proximal PCAT(MA). Conclusions Lesion-specific PCAT(MA) was higher in arteries with plaque than proximal PCAT(MA) in arteries without plaque. Lesion-specific PCAT(MA) was higher in non-calcified and mixed plaques compared to calcified plaques, and in minimal stenosis compared to severe; proximal PCAT(MA) did not show these relationships. This suggests that lesion-specific PCAT(MA) is related to plaque development and vulnerability

Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.</p

CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Background: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches.Purpose: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT).Methods: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans.Results: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. Conclusion: MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.</p

Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging:A Transfer Learning Approach

Delineation of Gross Tumor Volume (GTV) is essential for the treatment of cancer with radiotherapy. GTV contouring is a time-consuming specialized manual task performed by radiation oncologists. Deep Learning (DL) algorithms have shown potential in creating automatic segmentations, reducing delineation time and inter-observer variation. The aim of this work was to create automatic segmentations of primary tumors (GTVp) and pathological lymph nodes (GTVn) in oropharyngeal cancer patients using DL. The organizers of the HECKTOR 2022 challenge provided 3D Computed Tomography (CT) and Positron Emission Tomography (PET) scans with ground-truth GTV segmentations acquired from nine different centers. Bounding box cropping was applied to obtain an anatomic based region of interest. We used the Swin UNETR model in combination with transfer learning. The Swin UNETR encoder weights were initialized by pre-trained weights of a self-supervised Swin UNETR model. An average Dice score of 0.656 was achieved on a test set of 359 patients from the HECKTOR 2022 challenge. Code is available at: https://github.com/HC94/swin_unetr_hecktor_2022.</p

PHP48 COST SENSITIVENESS AND PHYSICIAN TREATMENT CHOICES

Objectives To explore the relationship between nodule count and lung cancer probability in baseline low-dose CT lung cancer screening. Materials and Methods Included were participants from the NELSON trial with at least one baseline nodule (3392 participants [45% of screen-group], 7258 nodules). We determined nodule count per participant. Malignancy was confirmed by histology. Nodules not diagnosed as screen-detected or interval cancer until the end of the fourth screening round were regarded as benign. We compared lung cancer probability per nodule count category. Results 1746 (51.5%) participants had one nodule, 800 (23.6%) had two nodules, 354 (10.4%) had three nodules, 191 (5.6%) had four nodules, and 301 (8.9%) had > 4 nodules. Lung cancer in a baseline nodule was diagnosed in 134 participants (139 cancers; 4.0%). Median nodule count in participants with only benign nodules was 1 (Inter-quartile range [IQR]: 1–2), and 2 (IQR 1–3) in participants with lung cancer (p = NS). At baseline, malignancy was detected mostly in the largest nodule (64/66 cancers). Lung cancer probability was 62/1746 (3.6%) in case a participant had one nodule, 33/800 (4.1%) for two nodules, 17/354 (4.8%) for three nodules, 12/191 (6.3%) for four nodules and 10/301 (3.3%) for > 4 nodules (p = NS). Conclusion In baseline lung cancer CT screening, half of participants with lung nodules have more than one nodule. Lung cancer probability does not significantly change with the number of nodules. Baseline nodule count will not help to differentiate between benign and malignant nodules. Each nodule found in lung cancer screening should be assessed separately independent of the presence of other nodules