Tumor Contour Research Articles

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma.

Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network

PurposeThis study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time.MethodsWe used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases. The proposed MedFusionGAN consisted of one generator network and one discriminator network trained in an adversarial scenario. Content, style, and L1 losses were used for training the generator to preserve the texture and structure information of the MRI and CT images.ResultsThe MedFusionGAN successfully generates fused images with MRI soft-tissue and CT bone contrast. The results of the MedFusionGAN were quantitatively and qualitatively compared with seven traditional and eight deep learning (DL) state-of-the-art methods. Qualitatively, our method fused the source images with the highest spatial resolution without adding the image artifacts. We reported nine quantitative metrics to quantify the preservation of structural similarity, contrast, distortion level, and image edges in fused images. Our method outperformed both traditional and DL methods on six out of nine metrics. And it got the second performance rank for three and two quantitative metrics when compared with traditional and DL methods, respectively. To compare soft-tissue contrast, intensity profile along tumor and tumor contours of the fusion methods were evaluated. MedFusionGAN provides a more consistent, better intensity profile, and a better segmentation performance.ConclusionsThe proposed end-to-end unsupervised method successfully fused MRI and CT images. The fused image could improve targets and OARs delineation, which is an important aspect of radiotherapy treatment planning.

Tumor Segmentation in Colorectal Ultrasound Images Using an Ensemble Transfer Learning Model: Towards Intra-Operative Margin Assessment

Tumor boundary identification during colorectal cancer surgery can be challenging, and incomplete tumor removal occurs in approximately 10% of the patients operated for advanced rectal cancer. In this paper, a deep learning framework for automatic tumor segmentation in colorectal ultrasound images was developed, to provide real-time guidance on resection margins using intra-operative ultrasound. A colorectal ultrasound dataset was acquired consisting of 179 images from 74 patients, with ground truth tumor annotations based on histopathology results. To address data scarcity, transfer learning techniques were used to optimize models pre-trained on breast ultrasound data for colorectal ultrasound data. A new custom gradient-based loss function (GWDice) was developed, which emphasizes the clinically relevant top margin of the tumor while training the networks. Lastly, ensemble learning methods were applied to combine tumor segmentation predictions of multiple individual models and further improve the overall tumor segmentation performance. Transfer learning outperformed training from scratch, with an average Dice coefficient over all individual networks of 0.78 compared to 0.68. The new GWDice loss function clearly decreased the average tumor margin prediction error from 1.08 mm to 0.92 mm, without compromising the segmentation of the overall tumor contour. Ensemble learning further improved the Dice coefficient to 0.84 and the tumor margin prediction error to 0.67 mm. Using transfer and ensemble learning strategies, good tumor segmentation performance was achieved despite the relatively small dataset. The developed US segmentation model may contribute to more accurate colorectal tumor resections by providing real-time intra-operative feedback on tumor margins.

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC.

Liver tumour detection and classification using partial differential technique algorithm with enhanced convolutional classifier

The image of liver which is the area of interest in this work is obtained from abdominal CT scan. It also contains details of other abdominal organs such as pancreas, spleen, stomach, gall bladder, intestine etc. Since all these organs are of soft tissues, the pixel intensity values differ marginally in the CT scan output and the organs overlap each other at their boundaries. Hence it is very difficult to trace out the exact contour of liver and liver tumor. The overlapping and obscure boundaries are to be avoided for proper diagnosis. Image segmentation process helps to meet this requirement. The normal perception of the CT image can be improved by suitable segmentation techniques. This will help the physician to extract more information from the image and give an accurate diagnosis and better treatment. The projected images are processed using the Partial Differential Technique (PDT) to isolate the liver from the other organs. The Level Set Methodology (LSM) is then used to separate the cancerous tissue from the healthy tissue around it. The classification of stages may be done with the assistance of an Enhanced Convolutional Classifier. The classification of LSM is evaluated by producing many metrics of accuracy, sensitivity, and specificity using an Improved Convolutional classifier. Compared to the two current algorithms, the proposed technique has a sensitivity and specificity of 96% and 93%, respectively, with 95% confidence intervals of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity, and specificity respectively.

Development of a fibroblast activation protein-targeted PET/NIR dual-modality probe and its application in head and neck cancer.

Purpose: The combination of near-infrared (NIR) and positron emission tomography (PET) imaging presents an opportunity to utilize the benefits of dual-modality imaging for tumor visualization. Based on the observation that fibroblast activation protein (FAP) is upregulated in cancer-associated fibroblasts (CAFs) infiltrating all solid tumors, including head and neck squamous cell carcinoma (HNSCC), we developed the novel PET/NIR probe [68Ga]Ga-FAP-2286-ICG. Preclinically, the specificity, biodistribution and diagnostic properties were evaluated. Methods: Cell uptake assays were completed with the U87MG cell to evaluate the specificity of the [68Ga]Ga-FAP-2286-ICG. The tumor-targeting efficiency, biodistribution and optimal imaging time window of the [68Ga]Ga-FAP-2286-ICG were studied in mice bearing U87MG xenografts. HNSCC tumor-bearing mice were used to evaluate the feasibility of [68Ga]Ga-FAP-2286-ICG for tumor localization and guided surgical resection of HNSCC tumors. Results: The in vitro experiments confirmed that [68Ga]Ga-FAP-2286-ICG showed good stability, specific targeting of the probe to FAP, and the durable retention effect in high-expressing FAP tumors U87MGcell. Good imaging properties such as good tumor uptake, high tumor-to-background ratios (5.44 ± 0.74) and specificity, and tumor contouring were confirmed in studies with mice bearing the U87MG xenograft. PET/CT imaging of the probe in head and neck cancer-bearing mice demonstrated specific uptake of the probe in the tumor with a clear background. Fluorescence imaging further validated the value of the probe in guiding surgical resection and achieving precise removal of the tumor and residual lesions. Conclusion: In a preclinical model, these attractive [68Ga]Ga-FAP-2286-ICG PET/NIR imaging acquired in head and neck cancer make it a promising FAP-targeted multimodal probe for clinical translation.

2S-BUSGAN: A Novel Generative Adversarial Network for Realistic Breast Ultrasound Image with Corresponding Tumor Contour Based on Small Datasets.

Deep learning (DL) models in breast ultrasound (BUS) image analysis face challenges with data imbalance and limited atypical tumor samples. Generative Adversarial Networks (GAN) address these challenges by providing efficient data augmentation for small datasets. However, current GAN approaches fail to capture the structural features of BUS and generated images lack structural legitimacy and are unrealistic. Furthermore, generated images require manual annotation for different downstream tasks before they can be used. Therefore, we propose a two-stage GAN framework, 2s-BUSGAN, for generating annotated BUS images. It consists of the Mask Generation Stage (MGS) and the Image Generation Stage (IGS), generating benign and malignant BUS images using corresponding tumor contours. Moreover, we employ a Feature-Matching Loss (FML) to enhance the quality of generated images and utilize a Differential Augmentation Module (DAM) to improve GAN performance on small datasets. We conduct experiments on two datasets, BUSI and Collected. Moreover, results indicate that the quality of generated images is improved compared with traditional GAN methods. Additionally, our generated images underwent evaluation by ultrasound experts, demonstrating the possibility of deceiving doctors. A comparative evaluation showed that our method also outperforms traditional GAN methods when applied to training segmentation and classification models. Our method achieved a classification accuracy of 69% and 85.7% on two datasets, respectively, which is about 3% and 2% higher than that of the traditional augmentation model. The segmentation model trained using the 2s-BUSGAN augmented datasets achieved DICE scores of 75% and 73% on the two datasets, respectively, which were higher than the traditional augmentation methods. Our research tackles imbalanced and limited BUS image data challenges. Our 2s-BUSGAN augmentation method holds potential for enhancing deep learning model performance in the field.

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy

BackgroundAccurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model.MethodsTo aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance.ResultsThe average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05).ConclusionsThe introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.

MS-CFNet: a multi-scale clinical studying-based and feature extraction-guided network for breast fibroadenoma segmentation in ultrasonography.

Segmenting breast tumors in ultrasonography is challenging due to the low image quality and presence of artifacts. Radiologists' studying and diagnosis skills are integrated with artificial intelligence to establish a clinical learning-based deep learning network in order to robustly extract and delineate features of breast fibroadenoma. The spatial local feature contrast (SLFC) module captures overall tumor contours, while the channel recursive gated attention (CRGA) module enhances edge perception through high-dimensional information interaction. Additionally, full-scale feature fusion and enhanced deep supervision are applied to improve model stability and performance. To achieve smoother boundaries, we introduce a new loss function (cosh-smooth) that penalizes and finely tunes tumor edges. Our dataset comprises 1016 clinical ultrasound images of breast fibroadenoma with labeled masks, alongside a publicly available dataset of 246 ones. Segmentation performance is evaluated using the Dice similarity coefficient (DSC) and mean intersection over union (MIOU). Extensive experiments demonstrate that our proposed MS-CFNet outperforms state-of-the-art methods. Compared to TransUNet as a baseline model, MS-CFNet improves by 1.47% in DSC and 2.56% in MIOU. The promising result of MS-CFNet is attributed to the integration of radiologists' clinical diagnosis procedure and the bionic mindset, enhancing the network's ability to recognize and segment breast fibroadenomas effectively.

Shifting-field-of-view technique enhancing the inflow effect for identifying tumor/vessel boundaries in MRI for radiotherapy treatment planning.

This study presents two cases of tumors in contact with the inferior vena cava during radiotherapy, and introduces a clinically useful technique for identifying tumor boundaries adjacent to blood vessels by adjusting the position of the field-of-view (FOV) to enhance the inflow effect in magnetic resonance imaging. We named this technique "Shifting-FOV." This method consists of three steps: (1) remove the upper and lower saturation pulses outside the FOV, (2) align the FOV to position the lower edge of the imaging slab as close to the tumor as possible, and (3) manually adjust the table position to locate the tumor at the center of the magnetic field. The proposed method allowed for accurate identification of the tumor/vessel boundaries in both cases. This is a useful technique that can be readily applied to other facilities. Furthermore, images obtained using this technique may enable accurate tumor contouring in radiotherapy treatment planning.

Clinical Impact of Contouring Variability for Prostate Cancer Tumor Boost.

In the FLAME randomized phase III trial, adding a focal radiotherapy (RT) boost to tumors visible on MRI improved prostate cancer disease-free survival, local control, and regional/distant metastasis-free survival without increasing toxicity. In a prospective study (ReIGNITE RT Boost), we found substantial variability in radiation oncologists' attempts to contour prostate cancer tumors on MRI. Participants' accuracy and reliability improved when they used a quantitative MRI biomarker for cancer called the restriction spectrum imaging restriction score (RSIrs). Here, we measure the impact of radiation oncologists' tumor contour attempts on RT plans and predicted probability of biochemical failure. A total of 44 radiation oncologists (participants) from multiple institutions contoured prostate tumors on 30 patient cases, some with only conventional MRI and some with conventional MRI plus RSIrs maps. We developed a knowledge-based planning automated algorithm to generate RT plans with focal tumor boost per the FLAME trial protocol: 77 Gy in 35 fractions to prostate and integrated boost up to 95 Gy to the focal target, provided no normal tissue constraints were violated. We applied this algorithm to each participant's tumor contour and compared dosimetric parameters to those achieved when using the expert-defined tumor (consensus of two radiologists and a radiation oncologist). The primary metric was dose covering 98% of the expert-defined tumor (D98%), which was associated with probability of biochemical failure in a model published with the FLAME trial. In this preliminary analysis, 42 target volumes were analyzed from 20 participants and two patient cases: case 1 was contoured with conventional MRI alone and case 2 with RSIrs. All plans had adequate coverage of the prostate and met all key normal tissue constraints. For case 1 (without RSIrs), the expert's D98% was 87.1 Gy. By comparison, median D98% for participants was 82.2 Gy (IQR 77.8 - 84.6 Gy). Per the FLAME trial model, the predicted probability of biochemical failure at 7 years is 6% for the expert, but participants' plans yielded a median failure probability of 11% (IQR 18 - 9%). For case 2 (with RSIrs), the expert's D98% was 82.8 Gy, while median D98% for participants was 80.6 Gy (IQR 80.0 - 81.0 Gy). Predicted probability of biochemical failure is 12% for the expert-defined target and median 13% (IQR 14 - 13%) for participants. Variability in radiation oncologists' prostate tumor contours can lead to clinically meaningful changes to focal RT boost plans. The probability of biochemical failure for one patient case increased from 6% to a median of 11% when using conventional MRI alone. Use of RSIrs may mitigate this problem by increasing the accuracy and reliability of radiation oncologists' tumor contours.

Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer

Background and purposeTo compare the prediction performance of image features of computed tomography (CT) images extracted by radiomics, self-supervised learning and end-to-end deep learning for local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), tumor-specific survival (TSS), overall survival (OS) and disease-free survival (DFS) of oropharyngeal squamous cell carcinoma (OPSCC) patients after (chemo)radiotherapy. Methods and materialsThe OPC-Radiomics dataset was used for model development and independent internal testing and the UMCG-OPC set for external testing. Image features were extracted from the Gross Tumor Volume contours of the primary tumor (GTVt) regions in CT scans when using radiomics or a self-supervised learning-based method (autoencoder). Clinical and combined (radiomics, autoencoder or end-to-end) models were built using multivariable Cox proportional-hazard analysis with clinical features only and both clinical and image features for LC, RC, LRC, DMFS, TSS, OS and DFS prediction, respectively. ResultsIn the internal test set, combined autoencoder models performed better than clinical models and combined radiomics models for LC, RC, LRC, DMFS, TSS and DFS prediction (largest improvements in C-index: 0.91 vs. 0.76 in RC and 0.74 vs. 0.60 in DMFS). In the external test set, combined radiomics models performed better than clinical and combined autoencoder models for all endpoints (largest improvements in LC, 0.82 vs. 0.71). Furthermore, combined models performed better in risk stratification than clinical models and showed good calibration for most endpoints. ConclusionsImage features extracted using self-supervised learning showed best internal prediction performance while radiomics features have better external generalizability.

Automated Detection, Segmentation, and Tracking of Brain Metastases in Repeated Courses of Stereotactic Radiosurgery Using Integrated Artificial Intelligence.

Salvage stereotactic radiosurgery (SRS) for distant brain metastases has been demonstrated as a safe and effective approach for intracranial recurrences after initial SRS. However, accurate tumor detection and segmentation among responding tumors within the irradiated parenchyma can be challenging. The requirement for the registration and reference to the previous course of SRS is very time-consuming and suffers significant inter and intra-reader variability. Artificial intelligence (AI)-assisted system has been proven to improve the accuracy and efficiency in the clinical flow of de-novo SRS. We hypothesize that an integrated AI system can facilitate an automated tumor contouring process for repeated SRS. Three patients who underwent their third course of SRS to brain metastases were selected for the pioneering works. They have had two sessions of SRS with a mean lesion number of 4 and 3.7, respectively. VBrain, an FDA-approved brain tumor management AI platform, was used to co-registered serial MR scans and automatically identify, track, and contour brain metastases for each course of SRS. The AI also indicated new lesions and treated lesions for each course. Three radiation oncologists experienced in brain SRS contoured the gross tumor volumes (GTVs) of the third course of SRS in two reader modes (assisted then unassisted) with a memory washout period of one week between each section. The segmentation ground truth was established through consensus among the three experts. Lesion-wise sensitivity, contouring accuracy, and consuming time were compared between the two contouring modes. In each patient, there were 15, 11, and 9 metastases, with a median diameter of 4.72 (95% CI: 4.05, 6.91) mm. The mean lesion-wise sensitivity was 96.96±2.47% with AI assistance and 76.90 ± 7.10% without assistance. There were two false-positive lesions in the assisted read, resulting in a low average false-positive rate of 0.67 per patient, while no false positive for the unassisted mode. AI assistance improved contouring accuracy. The median Dice similarity coefficient (DSC) was 0.71 (95% CI: 0.55, 0.87) for assisted contouring and 0.65 (95% CI: 0.46, 0.85) for unassisted contouring. We also use average Hausdorff distance (HD) to measure segmentation results. The mean HD was 0.72± 0.13 mm versus 0.73±0.08 mm for the two contouring modes (p = 0.02) Furthermore, the median contouring time per case was significantly shorter with AI assistance than without assistance (20.8 minutes vs. 29.8 minutes; p < 0.001), corresponding to a 43.2% time-saving. Our results suggest that the integration of an AI-based system into repeated brain SRS can significantly improve the accuracy and efficiency of tumor detection and segmentation. This approach has the potential to streamline the treatment planning process for salvage SRS.

Deep Learning for Automated Contouring of Primary Gross Tumor Volumes by MRI for Radiation Therapy of Brain Metastasis

Radiotherapy is one of the most effective methods for the treatment of brain metastases (BMs). Traditional manual delineation of primary gross tumor volumes (GTV) of multiple BMs (especially small metastases) in radiotherapy practice is extremely labor intensive and highly dependent on oncologists' experience, achieving the precise and efficient automatic delineation of BMs is of great significance for efficient and homogeneous one-stop adaptive radiotherapy. We retrospectively collected 62 MRI (non-enhanced T1-weighted sequences) sequences of 50 patients with BMs from January 2020 to July 2021. An automatic model (BUC-Net) for automatic delineation BMs was proposed in this work, which was based on deep learning by combining 3D bottler layer module and the cascade architecture to improve the accuracy and efficient of BMs' automatic delineation, especially for small metastases with tiny size and relatively low contrast. The prosed method was compared with the existing 3D U-Net (U-Net) and 3D U-Net Cascade (U-Net Cascade). The performance of our proposed method was evaluated by Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) with human experts. The automatic segmentation results of BUC-Net evaluated with 310 BMs in 13 test patients was summarized in Table 1. These BMs in each test patient were automatically delineated by two types of contours: as a whole tumor contour (Whole-delineation) and the multiple tumor contours (Multiple-delineation). BUC-Net performed the best mean DSC and HD95, which is significantly outperformed U-Net (Whole-delineation: 0.911 & 0.894 of DSC, Multiple-delineation: 0.794 & 0.754 of DSC, P < 0.05 for both) and U-Net cascade (Whole-delineation: 0.947 & 7.141 of HD95, Multiple-delineation: 0.902 & 1.171 of HD95, P < 0.05 for both); Additionally, BUC-Net achieved the best mean ASD for Whole-delineation and comparable ASD (0.290 & 0.277, P > 0) for Multiple-delineation with U-Net Cascade. Our results showed that the proposed approach is promising for the automatic delineation of BMs in MRI, which can be integrated into a radiotherapy workflow to significantly shorten segmentation time.

Image-guided preplanning workflow for high-dose-rate interstitial brachytherapy for gynecological malignancies

PurposeTo demonstrate image-guided preplan workflows for high-dose-rate (HDR) brachytherapy for advanced gynecological malignancies. METHODS AND MATERIALSTwo different preplanning scenarios are presented: (1) CT- or MRI-based preplan with partial applicator in place; (2) Preplans generated from prior fractions. The first scenario can be applied to Syed-Neblett template-based implants or hybrid brachytherapy applicators, while the second scenario applies to hybrid applicators. Both scenarios use MRI or CT images acquired with the applicator in place to demonstrate tumor and applicator relative locations and therefore, provide the ability to show optimized suggested needle positions including the implant depths before the actual insertion. RESULTSThe preplanning techniques have demonstrated feasibility and shown five areas of potential improvement: (1) shorter procedure time, (2) decreased number of total needles inserted, (3) shorter physician tumor contour time, (4) shorter planning time, and (5) evaluation of appropriateness for brachytherapy. ConclusionsThe use of image-guided brachytherapy preplanning improves clinical efficiency and is recommended for consideration for adaptation into clinical workflows for HDR interstitial and hybrid brachytherapy.

Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI. The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images. FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately. CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours. High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.

Prediction of Tumor PD-L1 Expression in Resectable Non-Small Cell Lung Cancer by Machine Learning Models Based on Clinical and Radiological Features: Performance Comparison With Preoperative Biopsy

ObjectiveWe investigated if PD-L1 expression can be predicted by machine learning using clinical and imaging features. MethodsWe included 117 patients with c-stage I/II non-small cell lung cancer who underwent radical resection. A total of 3951 radiomic features were extracted by defining the tumor (within tumor contour), rim (contour ±3 mm) and exterior (contour +10 mm) on preoperative contrast computed tomography. After feature selection by Boruta algorithm, prediction models of tumor PD-L1 expression (22C3: ≥1%, <1%) of resected specimens were constructed using Random Forest: radiomics, clinical, and combined models. Their performance was evaluated by 5-fold cross-validation, and AUCs were compared using Delong test. Next, study groups were categorized as patients without biopsy (training set), and those with biopsy (test set). Predictive ability of biopsy was compared to each prediction model. ResultsOf 117 patients (66 ± 10 years old, 48% male), 33 (28.2%) had PD-L1≥1%. Mean AUC of PD-L1≥1% for the validation set in radiomics, clinical, and combined models were 0.80, 0.80, and 0.83 (P = .32 vs. clinical model), respectively. The diagnosis of malignancy was made in 22 of 38 (58%) patients with attempted biopsies, and PD-L1 was measurable in 19 of 38 (50%) patients. Diagnostic accuracies of PD-L1≥1% from 19 determinable biopsies and 38 all attempted biopsies were 0.68 and 0.34, respectively. These were out performed by machine learning: 0.71, 0.71, and 0.74 for radiomics, clinical, and combined models, respectively. ConclusionsOur machine learning could be an adjunctive tool in estimating PD-L1 expression prior to neoadjuvant treatment, particularly when PD-L1 is indeterminable with biopsy.

Computer tomography-based shape of tumor contour and texture of tumor heterogeneity are independent prognostic indicators for clinical T1b-T2 renal cell carcinoma.

To evaluate association between computer tomography (CT)-based features of renal cell carcinoma (RCC) and survival outcomes. Data of 958 patients with clinical T1b-T2 RCC who underwent partial/radical nephrectomy from June 2003 to March 2022 were retrospectively evaluated. CT images of patients were reviewed by two radiologists for texture analysis of tumor heterogeneity and shape analysis of tumor contour. Patients were divided into three groups according to patterns of CT-based features: (1) favorable feature group (n = 117); (2) intermediate feature group (n = 606); and (3) unfavorable feature group (n = 235). Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed to evaluate overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). RCCs with unfavorable CT-based feature showed larger size on CT, higher nuclear grade, higher rate of histologic necrosis, and higher rate of capsular invasion than those in the other two groups (all p < 0.001). Unfavorable feature was associated with poorer OS (p = 0.001), CSS (p < 0.001), and RFS (p < 0.001) on Kaplan-Meier analysis. In multivariate analysis, intermediate and unfavorable features were independent predictors for recurrence (hazard ratio [HR] 2.51, 95% confidence interval [CI] 1.09-5.79, p = 0.031 and HR 3.71, 95% CI 1.58-8.73, p = 0.003, respectively), but not for overall death or RCC-specific death. A combination of irregular tumor contour feature with heterogeneous tumor texture feature on CT is associated with poor RFS in clinical T1b-T2 RCC preoperatively.

Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation.

Manually annotating liver tumor contours is a time-consuming and labor-intensive task for clinicians. Therefore, automated segmentation is urgently needed in clinical diagnosis. However, automatic segmentation methods face certain challenges due to heterogeneity, fuzzy boundaries, and irregularity of tumor tissue. In this paper, a novel deep learning-based approach with multi-scale-aware (MSA) module and twin-split attention (TSA) module is proposed for tumor segmentation. The MSA module can bridge the semantic gap and reduce the loss of detailed information. The TSA module can recalibrate the channel response of the feature map. Eventually, we can count tumors based on the segmentation results from a 3D perspective for cancer grading. Extensive experiments conducted on the LiTS2017 dataset show the effectiveness of the proposed method by achieving a Dice index of 85.97% and a Jaccard index of 81.56% over the state of the art. In addition, the proposed method also achieved a Dice index of 83.67% and a Jaccard index of 80.11% in 3Dircadb dataset verification, which further reflects its robustness and generalization ability.

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique.

Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm's mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS' 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.