A convolutional neural network-based auto-segmentation pipeline for breast cancer imaging

Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline

SSTR2 in Nasopharyngeal Carcinoma:Relationship with Latent EBV Infection and Potential as a Therapeutic Target

SIMPLE SUMMARY: Nasopharyngeal cancer (NPC) is a malignant epithelial tumor endemic to parts of Asia and associated with infection by the Epstein–Barr virus (EBV) in these regions. The cancer is often detected at a late stage which is associated with poor outcomes (63% 5-year survival). Advances for the management of this disease have remained largely stagnant and treatment relies primarily on radiotherapy and chemotherapy, as well as surgery when indicated. Nevertheless, our understanding of its underlying biology has grown rapidly in the past two decades, laying the foundation for the development of improved therapeutics which have the potential to improve outcomes. This review offers a comprehensive, up-to-date summary of this disease, with a focus on the role of somatostatin receptor 2 (SSTR2) in NPC and how this increased knowledge may lead to improved diagnosis and management of this disease. ABSTRACT: Nasopharyngeal carcinoma (NPC) is a malignant epithelial tumor, most commonly located in the pharyngeal recess and endemic to parts of Asia. It is often detected at a late stage which is associated with poor prognosis (5-year survival rate of 63%). Treatment for this malignancy relies predominantly on radiotherapy and/or systemic chemotherapy, which can be associated with significant morbidity and impaired quality of life. In endemic regions NPC is associated with infection by Epstein–Barr virus (EBV) which was shown to upregulate the somatostatin receptor 2 (SSTR2) cell surface receptor. With recent advances in molecular techniques allowing for an improved understanding of the molecular aetiology of this disease and its relation to SSTR2 expression, we provide a comprehensive and up-to-date overview of this disease and highlight the emergence of SSTR2 as a key tumor biomarker and promising target for imaging and therapy

Age exerts a continuous effect in the outcomes of Asian breast cancer patients treated with breast-conserving therapy

Abstract Background Asians are diagnosed with breast cancer at a younger age than Caucasians are. We studied the effect of age on locoregional recurrence and the survival of Asian breast cancer patients treated with breast-conserving therapy. Methods Medical records of 2492 patients treated with breast-conserving therapy between 1989 and 2012 were reviewed. The Kaplan–Meier method was used to estimate locoregional recurrence, breast cancer-free survival, and breast cancer-specific survival rates. These rates were then compared using log-rank tests. Outcomes and age were modeled by Cox proportional hazards. Fractional polynomials were then used to test for non-linear relationships between age and outcomes. Results Patients ≤ 40 years old were more likely to have locoregional recurrence than were older patients (Hazard ratio [HR] = 2.32, P < 0.001). Locoregional recurrence rates decreased year-on-year by 4% for patients with luminal-type breast cancers, compared with 8% for those with triple-negative cancers. Similarly, breast cancer-free survival rates increased year-on-year by 4% versus 8% for luminal-type and triple-negative cancers, respectively. Breast cancer-specific survival rates increased with age by 5% year-on-year. Both breast cancer-free survival and breast cancer-specific survival rates in patients with luminal cancers exhibited a non-linear (“L-shaped”) relationship—where decreasing age at presentation was associated with escalating risks of relapse and death. The influence of age on overall survival was confounded by competing non-cancer deaths in older women, resulting in a “U-shaped” relationship. Conclusions Young Asian breast cancer patients have a continuous year-on-year increase in rates of disease relapse and cancer deaths compared with older patients with no apparent threshold

Evaluation of inter- and intra-observer variations in prostate gland delineation using CT-alone versus CT/TPUS

Background: This study aims to explore the role of four-dimensional (4D) transperineal ultrasound (TPUS) in the contouring of prostate gland with planning computed tomography (CT) images, in the absence of magnetic resonance imaging (MRI). Materials and methods: Five radiation oncologists (ROs) performed two rounds of prostate gland contouring (single-blinded) on CT-alone and CT/TPUS datasets obtained from 10 patients who underwent TPUS-guided external beam radiotherapy. Parameters include prostate volume, DICE similarity coefficient (DSC) and centroid position. Wilcoxon signed-rank test assessed the significance of inter-modality differences, and the intraclass correlation coefficient (ICC) reflected inter- and intra-observer reliability of parameters. Results: Inter-modality analysis revealed high agreement (based on DSC and centroid position) of prostate gland contours between CT-alone and CT/TPUS. Statistical significant difference was observed in the superior-inferior direction of the prostate centroid position (p = 0.011). All modalities yielded excellent inter-observer reliability of delineated prostate volume with ICC &gt; 0.9, mean DSC &gt; 0.8 and centroid position: CT-alone (ICC = 1.000) and CT/TPUS (ICC = 0.999) left-right (L/R); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 0.998) anterior-posterior (A/P); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 1.000) superior-inferior (S/I). Similarly, all modalities yielded excellent intra-observer reliability of delineated prostate volume, ICC &gt; 0.9 and mean DSC &gt; 0.8. Lastly, intra-observer reliability was excellent on both imaging modalities for the prostate centroid position, ICC &gt; 0.9. Conclusion: TPUS does not add significantly to the amount of anatomical information provided by CT images. However, TPUS can supplement planning CT to achieve a higher positional accuracy in the S/I direction if access to CT/MRI fusion is limited

An assessment of the magnitude of intra-fraction movement of head-and-neck IMRT cases and its implication on the action-level of the imaging protocol.

BACKGROUND AND PURPOSE: A planning margin ⩽3mm is employed in some head-and-neck IMRT cases due to the proximity of critical structures. This study aims to explore the need to redefine the action-level in the head-and-neck imaging protocol in consideration of the intra-fraction movement. MATERIAL AND METHODS: This is a local study of 18 patients treated using the same immobilisation system and setup protocol. Post-treatment orthogonal pair of kilovoltage X-ray images was acquired on the first three days of treatment. 106 sets of pre- and post-treatment kV X-ray images acquired over 53 fractions were analysed against the treatment planning DRR for calculation of intra-fraction movement. RESULTS: Individual mean intra-fraction movement in all directions ranged from -1.8 to 1.1mm. Population mean (median) intra-fraction movement in the x-, y-, and z-planes were -0.1mm (0mm), -0.3mm (-0.3mm) and -0.2mm (-0.2mm) respectively. Intra-fraction movement in all three dimensions, x-, y- and z-planes were considered statistically significant (p<0.05). 7 out of 53 fractions (13.2%) were highlighted as the combined magnitude of the intra-fraction motion with the uncorrected pre-treatment setup errors had exceeded the boundaries of given margins. CONCLUSIONS: 3mm-AL was not adequate to account for intra-fraction movement when the CTV-PTV margin was ⩽3mm and should be excluded from the routine imaging protocol and daily image-guided radiotherapy should be employed. Adjusting the action-level to 2mm would allow a more confident approach in delivery of the prescribed dose in head-and-neck IMRT cases

Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer

Background: Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors’ response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. Methods: The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. Results: The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). Conclusions: Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.W.L.N. is supported by the National Medical Research Council Fellowship (NMRC/MOH-000166-00)