Pretreatment Computed Tomography Research Articles

Prediction of Radiation Therapy Induced Cardiovascular Toxicity from Pretreatment CT Images in Patients with Thoracic Malignancy via an Optimal Biomarker Approach.

Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes. The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT. Manual segmentations of 10 cardiovascular objects of interest were performed on pre-treatment non-contrast-enhanced CT simulation images in 125 patients with thoracic malignancy (41 who developed cardiotoxicity and 84 who did not after RT). 1078 features describing morphology, image intensity, and texture for each of these objects were extracted and the top 5 features among them that were most uncorrelated and showed the best ability to discriminate between cardiotoxicity/ no cardiotoxicity were determined. The best combination among all possible combinations among these 5 features that yielded the highest accuracy of prediction on a training data set was selected, an SVM classifier was then trained on this combination, and tested for prediction accuracy on an independent data set. Prediction accuracy was quantified for the optimal features derived from each object. The best feature combination based on 5 CT-based features derived from the left ventricle had the highest testing prediction accuracy of 0.88 among all objects. Prediction accuracies over all objects ranged from 0.76-0.88. Heart, Left Atrium, Aortic Arch, Thoracic Aorta, and Descending Thoracic Aorta showed the next best accuracy of 0.84. Most optimal features were texture properties based on the co-occurrence matrix. It is feasible to predict future cardiotoxicity following RT with high accuracy in individual patients with thoracic malignancy from available pre-treatment CT images via machine learning techniques.

Association of Body Composition Parameters with the Short- and Long-Term Efficacy of Neoadjuvant Immunotherapy Combined with Chemotherapy in Advanced Gastric Cancer

Background Immunotherapy has become a prevalent strategy in the neoadjuvant treatment of advanced gastric cancer (AGC). This study investigates the predictive value of computed tomography (CT)-derived body composition parameters on the efficacy of neoadjuvant immunotherapy for AGC. Methods Data on 103 patients with resectable AGC who received neoadjuvant immunotherapy combined with chemotherapy at a teaching hospital between March 2020 and August 2022 were collected. Body composition parameters, including the subcutaneous adipose index (SAI), visceral adipose index (VAI), and skeletal muscle index (SMI), were calculated from pretreatment CT images. Logistic regression and Cox proportional hazards models assessed the impact of these parameters on pathological responses and survival outcomes following treatment. Results Of the patients, 34 (33.0%) achieved a major pathological response (MPR). Higher SAI, VAI, and SMI values were significantly linked to an increased likelihood of achieving MPR (p < 0.05). Multivariate regression analysis revealed that only SAI independently predicted MPR (OR 1.042, 95% CI 1.009–1.077, p = 0.013). Furthermore, patients with a high SAI had significantly improved 2-year overall survival (76.9% vs. 54.9%, log-rank p = 0.012) and 2-year event-free survival (71.2% vs. 51.0%, log-rank p = 0.022) compared to those with low SAI. The survival benefit associated with high SAI was partly due to its higher MPR rate (mediating proportion: 37.5%, 95% CI: 12%–110%). Conclusion Pretreatment SAI independently correlates with MPR and better oncological outcomes in patients with AGC receiving neoadjuvant immunotherapy.

Near-Normalized Maxillomandibular Relationship and Upper Airway in Infants with Robin Sequence Treated with Stanford Orthodontic Airway Plate: Computed Tomography Study.

To assess craniofacial and upper airway growth in infants with Robin sequence (RS) during the 1st year of life when their severe upper airway obstruction was treated non-surgically with the Stanford orthodontic airway plate treatment program (SOAP). Retrospective longitudinal cohort study comparing SOAP-treated infants with RS (treatment group) with age-matched healthy controls (HC) using computed tomography (CT). Single tertiary referral hospital. Twelve SOAP-treated infants with RS. SOAP. Craniofacial skeletal cephalometric parameters and three-dimensional airway metrics. No infants required mandibular distraction or tracheostomy surgery after SOAP. The pre-treatment CT was acquired at the mean age (±SD) of 1.3 months (±0.7). The treatment lasted for 4.8 months (±0.9). Post-treatment CT was acquired at 12.9 months (±2.3) of age. The pre-treatment obstructive apnea hypopnea index decreased from 29.21 events/hour (±14.80) to 4.11 events/hour (±2.21) at post-treatment. The total mandibular length increased from 40.58 mm (±2.98) to 62.15 mm (±2.77) at post-treatment (p < 0.001) resulting in the growth velocity of 51.02% (±11.20) or 20.52 mm (±3.95) per year. The maxillary and mandibular lengths of the treatment group at post-treatment were shorter than those of HC although the maxillomandibular relationship was not different (p = 0.618). The upper airway volume and minimal cross-sectional area were also not different (p = 0.083, p = 0.254, respectively). The maxillomandibular relationship and upper airway of the SOAP-treated infants with RS were near-normalized at post-treatment, comparable to age-matched healthy infants.

Development and External Validation of a Combined Clinical-Radiomic Model for Predicting Insufficient Hypertrophy of the Future Liver Remnant following Portal Vein Embolization.

This study aimed to develop and externally validate a model for predicting insufficient future liver remnant (FLR) hypertrophy after portal vein embolization (PVE) based on clinical factors and radiomics of pretreatment computed tomography (CT) PATIENTS AND METHODS: Clinical information and CT scans of 241 consecutive patients from three Swedish centers were retrospectively collected. One center (120 patients) was applied for model development, and the other two (59 and 62 patients) as test cohorts. Logistic regression analysis was adopted for clinical model development. A FLR radiomics signature was constructed from the CT images using the support vector machine. A model combining clinical factors and FLR radiomics signature was developed. Area under the curve (AUC) was adopted for predictive performance evaluation RESULTS: Three independent clinical factors were identified for model construction: pretreatment standardized FLR (odds ratio (OR): 1.12, 95% confidence interval (CI): 1.04-1.20), alanine transaminase (ALT) level (OR: 0.98, 95% CI: 0.97-0.99), and PVE material (OR: 0.27, 95% CI: 0.08-0.87). This clinical model showed an AUC of 0.75, 0.71, and 0.68 in the three cohorts, respectively. A total of 833 radiomics features were extracted, and after feature dimension reduction, 16 features were selected for FLR radiomics signature construction. When adding it to the clinical model, the AUC of the combined model increased to 0.80, 0.76, and 0.72, respectively. However, the increase was not significant. Pretreatment CT radiomics showed added value to the clinical model for predicting FLR hypertrophy following PVE. Although not reaching statistically significant, the evolving radiomics holds a potential to supplement traditional predictors of FLR hypertrophy.

Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.

Definitive chemoradiotherapy (dCRT) is the cornerstone for locally advanced non-small cell lung cancer (LA-NSCLC). The study aimed to construct a multi-omics model integrating baseline clinical data, computed tomography (CT) images and genetic information to predict the prognosis of dCRT in LA-NSCLC patients. The study retrospectively enrolled 105 stage III LA-NSCLC patients who had undergone dCRT. The pre-treatment CT images were collected, and the primary tumor was delineated as a region of interest (ROI) on the image using 3D-Slicer, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was employed for dimensionality reduction and selection of features. Genomic information was obtained from the baseline tumor tissue samples. We then constructed a multi-omics model by combining baseline clinical data, radiomics and genomics features. The predictive performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index). The median follow-up time was 30.1 months, and the median progression-free survival (PFS) was 10.60 months. Four features were applied to construct the radiomics model. Multivariable analysis demonstrated the Rad-score, KEAP1 and MET mutations were independent prognostic factors for PFS. The C-index of radiomics model, genomics model and radiogenomics model all performed well in the training group (0.590 vs. 0.606 vs. 0.663) and the validation group (0.599 vs. 0.594 vs. 0.650). The radiomics model, genomics model and radiogenomics model can all predict the prognosis of dCRT for LA-NSCLC, and the radiogenomics model is superior to the single type model.

Is it time to move from the Unidimensional RECIST 1.1 Response Assessment Criteria to a Volumetric Evaluation in the Present Era of Image-based Oncology? An Evaluation in Locally Advanced Head Neck Cancers Undergoing Treatment

AimsTumour response assessments, as per Response Evaluation Criteria in Solid Tumours (RECIST 1.1), are based on the sum of diameters (SODs) of the primary tumour (longest diameter) and/or short axis diameter of lymph nodes. This study evaluates the response categorisation as per RECIST 1.1 vs Computed tomography (CT) based volumetric assessment of RECIST (proposed as vRECIST) in locally advanced head and neck cancers (LAHNCs) undergoing treatment. Material and methodsThe pre-treatment SODs and CT estimated tumour volumes were recorded in 45 LAHNCs treated with radiotherapy (RT), chemoradiotherapy (CTRT) or thermochemoradiotherapy (HTCTRT). Tumour responses were assessed independently as per RECIST 1.1 and vRECIST by two radiation oncologists and grouped into complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD). These response groups were evaluated for the likely congruence of the two approaches, as categorised independently by these two observers. ResultsAll patients in stages III (n = 7), IVA (n = 16) and IVB (n = 22) were inoperable and had received either RT alone (n = 1), CTRT (n = 12) or HTCTRT (n = 32). Based on SODs criteria of RECIST 1.1, of the 45 patients, 5 and 40 were grouped as PR and SD by the first observer, while this changed to 34 and 10, respectively and 1 PD, with vRECIST (p < 0.001). Similarly, for the second observer, the 4 PR and 41 SD grouped using RECIST 1.1 were recategorised to 34 PR, 10 SD, and 1 PD by vRECIST (p < 0.001). Thus, a mismatch of 66.6% and 68.8%, respectively, was evident by observers first and second in categorising SD based on SODs of RECIST 1.1 vs PR on vRECIST. ConclusionsTreatment responses in LAHNCs assessed using SODs resulted in significant uncertainties and failed to reflect actual volumetric changes in tumours during treatment. It is perhaps time to consider replacing the SODs of RECIST 1.1 with vRECIST for unequivocal tumour response categorisation in the present era of image-based oncology practice.

MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke : AComparative Study of MT-Retrieved Clots and Clot Analogs.

Assessing clot composition on prethrombectomy computed tomography (CT) imaging may help in stroke treatment planning. In this study we seek to use microCT imaging of fabricated blood clots to understand the relationship between CT radiographic signals and the biological makeup. Clots (n = 10) retrieved by mechanical thrombectomy (MT) were collected, and 6clot analogs of varying RBC composition were made. We performed paired microCT and histological image analysis of all 16clots using aScanCo microCT 100 (4.9 µm resolution) and standard H&E staining (imaged at 40×). From these data types, first order statistic (FOS) radiomics were computed from microCT, and percent composition of RBCs (%RBC) was computed from histology. Polynomial and linear regression (LR) were used to build statistical models based on retrieved thrombus microCT and %RBC that were evaluated for their ability to predict the %RBC of clot analogs from mean HU. Correlation analyses of microCT FOS with composition were completed for both retrieved clots and analogs. The LR model fits relating MT-retrieved clot %RBC with mean (R2 = 0.625, p = 0.006) and standard deviation (R2 = 0.564, p < 0.05) in HUs on microCT were significant. Similarly, LR models relating analog histological %RBC to analog protocol %RBC (R2 = 0.915, p = 0.003) and mean HUs on microCT (R2 = 0.872, p = 0.007) were also significant. When the LR model built using MT-retrieved clots was used to predict analog %RBC from mean HUs, significant correlation was observed between predictions and actual histological %RBC (R2 = 0.852, p = 0.009). For retrieved clots, significant correlations were observed for energy and total energy with %RBC and %FP (|R| > 0.7, q < 0.01). Analogs further demonstrated significant correlation between FOS energy, total energy, variance and %WBC (|R| > 0.9, q < 0.01). MicroCT can be used to build models that predict AIS clot composition from routine CT parameters and help us to better understand radiomic signatures associated with clot composition and first pass outcomes. In future work, such observations can be used to better infer clot composition and inform thrombectomy prognostics from pretreatment CTs.

CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis

This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median follow-up period of 3.7 mo, 18 patients were confirmed to have benefited from the treatment, defined as a 20% improvement from baseline in the estimated glomerular filtration rate. A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype characteristics. Radiomics features, comprising 116 handcrafted features and 78 deep learning features, were extracted from the affected renal and perirenal adipose regions. More features from the latter were correlated with early outcomes, as determined by univariate analysis, and were visually represented in radiomics heatmaps and volcano plots. After using consensus clustering and the least absolute shrinkage and selection operator method for feature selection, five machine learning models were evaluated. Logistic regression yielded the highest leave-one-out cross-validation accuracy of 0.780 (95%CI: 0.660–0.880) for the renal signature, while the support vector machine achieved 0.865 (95%CI: 0.769–0.942) for the perirenal adipose signature. SHapley Additive exPlanations was used to visually interpret the prediction mechanism, and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature, respectively. Multivariate analysis revealed that both signatures served as independent predictive factors. When combined, they achieved an area under the receiver operating characteristic curve of 0.888 (95%CI: 0.784–0.992), indicating that the imaging phenotypes from both regions complemented each other. In conclusion, non-contrast CT-based radiomics can be leveraged to predict the early outcomes of PTRA, thereby assisting in identifying patients with ARAS suitable for this treatment, with perirenal adipose tissue providing added predictive value.

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Purpose. This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients. Methods. A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity. Results. A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5–1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5–2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT. Conclusion. Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

Identifying Lymph Nodes and Their Statuses from Pretreatment Computer Tomography Images of Patients with Head and Neck Cancer Using a Clinical-Data-Driven Deep Learning Algorithm.

Head and neck cancer is highly prevalent in Taiwan. Its treatment mainly relies on clinical staging, usually diagnosed from images. A major part of the diagnosis is whether lymph nodes are involved in the tumor. We present an algorithm for analyzing clinical images that integrates a deep learning model with image processing and attempt to analyze the features it uses to classify lymph nodes. We retrospectively collected pretreatment computed tomography images and surgery pathological reports for 271 patients diagnosed with, and subsequently treated for, naïve oral cavity, oropharynx, hypopharynx, and larynx cancer between 2008 and 2018. We chose a 3D UNet model trained for semantic segmentation, which was evaluated for inference in a test dataset of 29 patients. We annotated 2527 lymph nodes. The detection rate of all lymph nodes was 80%, and Dice score was 0.71. The model has a better detection rate at larger lymph nodes. For those identified lymph nodes, we found a trend where the shorter the short axis, the more negative the lymph nodes. This is consistent with clinical observations. The model showed a convincible lymph node detection on clinical images. We will evaluate and further improve the model in collaboration with clinical physicians.

Deep learning radiomics analysis based on computed tomography for survival prediction in gastric neuroendocrine neoplasm: a multicenter study.

Survival prediction is crucial for patients with gastric neuroendocrine neoplasms (gNENs) to assess the treatment programs and may guide personalized medicine. This study aimed to develop and evaluate a deep learning (DL) radiomics model to predict the overall survival (OS) in patients with gNENs. The retrospective analysis included 162 consecutive patients with gNENs from two hospitals, who were divided into a training cohort, internal validation cohort (The First Affiliated Hospital of Zhengzhou University; n=108), and an external validation cohort (The Henan Cancer Hospital; n=54). DL radiomics analysis was applied to computed tomography (CT) images of the arterial phase and venous phase, respectively. Based on pretreatment CT images, two DL radiomics signatures were developed to predict OS. The combined model incorporating the radiomics signatures and clinical factors was built through the multivariable Cox proportional hazards (CPH) method. The combined model was visualized into a radiomics nomogram for individualized OS estimation. Prediction performance was assessed with the concordance index (C-index) and the Kaplan-Meier (KM) estimator. The DL-based radiomics signatures based on two phases were significantly correlated with OS in the training (C-index: 0.79-0.92; P<0.01), internal validation (C-index: 0.61-0.86; P<0.01), and external validation (C-index: 0.56-0.75; P<0.01) cohorts. The combined model integrating radiomics signatures with clinical factors showed a significant improvement in predictive performance compared to the clinical model in the training (C-index: 0.86 vs. 0.80; P<0.01), internal validation (C-index: 0.77 vs. 0.71; P<0.01), and external validation (C-index: 0.71 vs. 0.66; P<0.01) cohorts. Moreover, the combined model classified patients into high-risk and low-risk groups, and the high-risk group had a shorter OS compared to the low-risk group in the training cohort [hazard ratio (HR) 3.12, 95% confidence interval (CI): 2.34-3.93; P<0.01], which was validated in the internal (HR 2.51, 95% CI: 1.57-3.99; P<0.01) and external validation cohort (HR 1.77, 95% CI: 1.21-2.59; P<0.01). DL radiomics analysis could serve as a potential and noninvasive tool for prognostic prediction and risk stratification in patients with gNENs.

Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study

BackgroundReliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer.MethodSix hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features.ResultsThe dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92–0.96), 0.84 (95% CI, 0.82–0.86), and 0.84 (95% CI, 0.83–0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps.ConclusionThe coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.

Quantification of diffuse parenchymal lung disease in non-small cell lung cancer patients with definitive concurrent chemoradiation therapy for predicting radiation pneumonitis.

We sought to quantify diffuse parenchymal lung disease (DPLD) extent using quantitative computed tomography (CT) analysis and to investigate its association with radiation pneumonitis (RP) development in non-small cell lung cancer (NSCLC) patients receiving definitive concurrent chemoradiation therapy (CCRT). A total of 82 NSCLC patients undergoing definitive CCRT were included in this prospective cohort study. Pretreatment CT scans were analyzed using quantitative CT analysis software. Low-attenuation area (LAA) features based on lung density and texture features reflecting interstitial lung disease (ILD) were extracted from the whole lung. Clinical and dosimetric factors were also evaluated. RP development was assessed using the Common Terminology Criteria for Adverse Events version 5.0. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors for grade ≥3 (≥GR3) RP. RP was identified in 68 patients (73.9%), with nine patients (10.9%) experiencing ≥GR3 RP. Univariable logistic regression analysis identified excess kurtosis and high-attenuation area (HAA)_volume (cc) as significantly associated with ≥GR3 RP. Multivariable logistic regression analysis showed that the combined use of imaging features and clinical factors (forced expiratory volume in 1 second [FEV1], forced vital capacity [FVC], and CHEMO regimen) demonstrated the best performance (area under the receiver operating characteristic curve = 0.924) in predicting ≥GR3 RP. Quantified imaging features of DPLD obtained from pretreatment CT scans would predict the occurrence of RP in NSCLC patients undergoing definitive CCRT. Combining imaging features with clinical factors could improve the accuracy of the predictive model for severe RP.

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.

We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

Prognostic value of 18F-FDG PET in uterine cervical cancer patients with stage IIICr allocated by imaging

The effect on survival of radiographic lymph node metastasis in uterine cervical cancer patients is more important than before, even though its prognostic value not been well investigated. The aim of our study is to evaluate the prognostic potential of 18F-fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) compared with Computed Tomography (CT) in uterine cervical cancer patients with stage IIICr allocated by imaging. Fifty-five patients with biopsy-proven primary cervical cancer underwent definitive radiation therapy for stages IIB–IVB of The International Federation of Gynecology and Obstetrics (FIGO) 2018 classifications. The prognostic performance of pretreatment 18F-FDG PET and CT for assessing lymph node metastasis was evaluated by two experienced readers. The PET and CT findings were correlated with the risk of progression-free survival (PFS) and overall survival (OS). Kaplan–Meier survival curves showed that PFS was significantly worse in patients with positive lymph nodes on 18F-FDG PET than in those patients with negative lymph nodes on 18F-FDG PET (p = 0.003), whereas there was no significant difference in PFS between patients with lymph nodes sized ≥ 1 cm and those sized < 1 cm (p = 0.140). Univariate analysis showed that positive lymph nodes on 18F-FDG PET was significantly associated with poor PFS (p = 0.006), whereas lymph node size was not significantly associated with poor PFS (p = 0.145). In multivariate analysis, positive lymph nodes on 18F-FDG PET was significantly associated with poor PFS (p = 0.006) and was an independent prognostic factor for PFS. 18F-FDG PET offers high prognostic value for patients with stage IIICr allocated by imaging compared with CT, suggesting that 18F-FDG PET might be useful in clinical staging decisions and thus promote optimal diagnostic and therapeutic strategies.

Improved prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma using pre-treatment CT radiomics

When planning radiation therapy, late effects due to the treatment should be considered. One of the most common complications of head and neck radiation therapy is hypothyroidism. Although clinical and dosimetric data are routinely used to assess the risk of hypothyroidism after radiation, the outcome is still unsatisfactory. Medical imaging can provide additional information that improves the prediction of hypothyroidism. In this study, pre-treatment computed tomography (CT) radiomics features of the thyroid gland were combined with clinical and dosimetric data from 220 participants to predict the occurrence of hypothyroidism within 2 years after radiation therapy. The findings demonstrated that the addition of CT radiomics consistently and significantly improves upon conventional model, achieving the highest area under the receiver operating characteristic curve (AUCs) of 0.81 ± 0.06 with a random forest model. Hence, pre-treatment thyroid CT imaging provides useful information that have the potential to improve the ability to predict hypothyroidism after nasopharyngeal radiation therapy.

Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy

Background and purposeWe aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC). Materials and methodsA total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO–Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models’ prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan–Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated. ResultsThe mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia. ConclusionMid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.

Longitudinal Radiomics-Based Prediction of Local Recurrence after Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

Computed tomography (CT)-based radiomic analysis is an emerging tool that can help predict recurrence after radiation in non-small cell lung cancer (NSCLC). Most studies have focused on using radiomic features obtained from pre-treatment CT scans but not on post-treatment CTs. Our goal was to determine if radiomic features derived from post-treatment CTs perform better than those from pre-treatment CTs in predicting local recurrence after stereotactic body radiation therapy (SBRT) in patients with early-stage NSCLC. Patients with T1/T2 N0 early-stage NSCLC were retrospectively reviewed. Criteria for selection included patients with biopsy-proven NSCLC, non-ground glass solid lesions, 4-5 fraction SBRT treatments, and non-contrast thin-slice CTs available at pre-treatment baseline and at 3 and 6 months post-SBRT. Clinical and tumor characteristics were compared using Chi-square and Student's t-tests. CT image pre-processing was performed and 107 radiomic features were extracted using 3D Slicer and open-source software. Univariate analysis for local recurrence was determined by Cox regression with death as a competing risk. The Benjamini-Hochberg Procedure was applied to control the false discovery rate. Pearson correlation analysis was used to exclude redundant features (r > 0.8), and multivariate analysis was conducted with Random Forest on the top-performing features found on univariate logistic regression. Models were trained on a class-balanced loss to account for class imbalance. Twenty iterations of stratified 3 k-fold cross-validation were used to select the best model by the area under the receiver operating characteristic curve (AUC). We identified and analyzed 86 patient tumors, including 76 non-local recurrences and 10 local recurrences (49 males, 37 females; age, median 72, range 52-91 years). No differences in age, histology, standardized uptake values on positron emission tomography scans, gross tumor volume, and radiation dose were found between patients with and without local recurrence (all p >0.05). The median time to local recurrence was 18.9 months (range 5.5-45.6 months). At the baseline, 3-month, and 6-month timepoints, 2, 10, and 10 radiomic features predicted local recurrence on Cox regression univariate analysis, respectively (all q <0.01). On multivariate analysis of top-performing radiomic features, the 3-month timepoint performed the best with a mean (±standard deviation) AUC score of 0.82 (±0.13) compared to a mean AUC of 0.73 (±0.086) at baseline and a mean AUC of 0.74 (±0.10) at 6 months. Post-treatment radiomic features at 3 months outperformed pre-treatment radiomic features in predicting local recurrence after SBRT for NSCLC. These results suggest radiomic data from follow-up CTs may be helpful when developing radiomic models to predict local recurrence in patients with NSCLC.

Treatment Response Prediction Using a Combination of Hematological Biomarkers and CT Deep Learning Radiomics in Locally Advanced Esophageal Squamous Cell Carcinoma

To assess the complementary value of hematological biomarkers to deep learning-radiomic models for assessing esophageal squamous cell carcinoma (ESCC) pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), which will help to provide a reference for the following clinical study of esophageal preservation. A total of 157 patients with ESCC were enrolled and divided into a training cohort (n = 111) and a validation cohort (n = 47). Computed tomography (CT) was performed for all patients 2 weeks before and 6 weeks after nCRT. In addition, clinicopathological factors and hematological parameters before nCRT were collected. Deep learning and handcrafted radiomic features were extracted from segmented regions of interest (ROIs) from pretreatment (ROI1) and posttreatment (ROI2) CT, which represented the features of the pre- and posttreatment tumors, respectively. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used for feature selection, and logistic regression (LR) was used as the classifier. The deep learning radiomic nomogram (DLRN) was then developed based on the rad-scores and independent clinicopathological risk factors. The model was assessed using area under the receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis and verified with both 10-fold cross-validation and internal validation using bootstrap resampling with 1000 replicates. Rad-scores were constructed with 8 features, which were finally selected as the most predictive features from ROI 1 and ROI 2. The neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), albumin and rad-scores were incorporated into the DLRN, which achieved good prediction performance, with areas under the ROC curve of 0.888 (95% CI, 0.824-0.951, p <0.05) for the training cohort and 0.873 (95% CI, 0.772-0.974, p <0.05) for the validation cohort. On the training set, the DLRN achieved an AUC of 0.882 in 10-fold cross-validation, and after internal validation, the area under the ROC curve still reached 0.884. The DLRN performed significantly better than the clinical model and radiomics models (p<0.05). IDI and continuous NRI showed significant improvement for the DLRN when incorporating radiomics features and hematological parameters (IDI = 0.3399, P <0.001; continuous NRI = 1.141, P <0.001; categorical NRI = 0.3836, P <0.001). Calibration curves (p > 0.05) and DCA demonstrated that the DLRN was useful for pCR prediction and produced a greater net benefit than the clinical model and radiomics models. Incorporation of radiomics features and hematological parameters into the DLRN improved pCR prediction after nCRT in ESCC. Enhanced pCR predictability may improve patient selection before surgery, providing clinical application value for the use of active surveillance.

Reproducibility and Repeatability of Pelvic Radiomics Features with Daily Imaging on a Novel Biology-Guided Radiotherapy Machine Compared to Daily Imaging on Other Radiotherapy Delivery Systems

Radiomics features extracted from computed tomography (CT) images have the potential of being used as imaging biomarkers in diagnostic and prognostic models for prostate cancer. This study aims to evaluate reproducibility and repeatability of radiomics features using daily CT images from a novel biology-guided radiotherapy (BgRT)-capable machine compared to other daily CT imaging modalities from other radiotherapy delivery systems. We retrospectively selected thirty male patients treated to the pelvic region at our institution. Among the thirty patients, ten patients were treated on a BgRT-capable machine with daily fan-beam kilovoltage CT (kVCT) scans, ten patients were treated on a conventional Linac with daily cone-beam CT (CBCT) scans, and ten patients were treated on helical tomotherapy with daily megavoltage CT (MVCT) scans. The prostate gland and femoral heads were delineated on planning CT images and on CT images at the first treatment fraction. After all the CT images were resampled to have an identical voxel size, 107 radiomics features were calculated for the prostate and femoral heads on both the planning CT images and daily CT images. Concordance correlation coefficient (CCC) and Pearson's correlation coefficient (r) were calculated to evaluate radiomics feature reproducibility between the planning CT images and pre-treatment CT scans at the first treatment fraction. To evaluate radiomics feature repeatability with kVCT images on the BgRT-capable machine, a total of ten treatment sessions with repeat kVCT scans on the BgRT-capable machine were retrieved and radiomics features were compared between the first and the second kVCT scans. For the prostate gland volume, high concordance (CCC > 0.8) was found for 8, 6, and 0 radiomics features with the first-fraction kVCT, CBCT, and MVCT images, respectively, in relation to the planning CT images, while there was strong correlation (r > 0.8) in 38, 22, and 5 radiomics features in the kVCT, CBCT, and MVCT images, respectively, in relation to the planning CT images. For the left and right femoral heads, high concordance (CCC > 0.8) was found in no more than two radiomics features with the kVCT, CBCT, or MVCT images in relation to the planning CT images. Based on repeat kVCT scan data, 97 (90.7%) radiomics features showed high concordance (CCC > 0.8) in repeat kVCT scans with 98 (91.6%) features showing high correlation (Pearson's correlation coefficient > 0.8) for the prostate gland volume. A small number of radiomics features were found to show strong reproducibility between daily CT images on the first fraction and the planning CT images with the three IGRT CT modalities in this study. On the other hand, our results indicated that strong reproducibility was found with more radiomics features with daily kVCT images from the BgRT-capable machine compared to daily CBCT and MVCT images for the prostate gland. Strong repeatability was also found with most radiomics features in daily kVCT images.