Early Response Assessment in Lung Cancer Patients using Spatio-temporal CBCT Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉克普尔分校）；Tata Medical Center, Kolkata（塔塔医疗中心（加尔各答））；Arden Cancer Centre（阿登癌症中心）；Department of Computer Science and Engineering（计算机科学与工程系）；Department of Radiation Oncology（放射肿瘤学系）；Department of Nuclear Medicine（核医学系）；Department of Medical Physics（医学物理系）
We report a model to predict patient's radiological response to curative radiation therapy (RT) for non-small-cell lung cancer (NSCLC).
Cone-Beam Computed Tomography images acquired weekly during the six-week course of RT were contoured with the Gross Tumor Volume (GTV) by senior radiation oncologists for 53 patients (7 images per patient).
Deformable registration of the images yielded six deformation fields for each pair of consecutive images per patient.
Jacobian of a field provides a measure of local expansion/contraction and is used in our model.
Delineations were compared post-registration to compute unchanged ($U$), newly grown ($G$), and reduced ($R$) regions within GTV.
The mean Jacobian of these regions $μ_U$, $μ_G$ and $μ_R$ are statistically compared and a response assessment model is proposed.
A good response is hypothesized if $μ_R < 1.0$, $μ_R < μ_U$, and $μ_G < μ_U$.
For early prediction of post-treatment response, first, three weeks' images are used.
Our model predicted clinical response with a precision of $74\%$.
Using reduction in CT numbers (CTN) and percentage GTV reduction as features in logistic regression, yielded an area-under-curve of 0.65 with p=0.005.
Combining logistic regression model with the proposed hypothesis yielded an odds ratio of 20.0 (p=0.0).
Deformable registration of spatiotemporal Cone-Beam Computed Tomography (CBCT) images taken sequentially during the radiation treatment course yields a deformation field for a pair of images. The Jacobian of this field at any voxel provides a measure of the expansion or contraction of a unit volume. We analyze the Jacobian at different sections of the tumor volumes obtained from delineation done by radiation oncologists for lung cancer patients. The delineations across the temporal sequence are compared post registration to compute tumor areas namely, unchanged (U), newly grown (G), and reduce…
Purpose: CBCT-based adaptive radiotherapy requires daily images for accurate dose calculations. This study investigates the feasibility of applying a single convolutional network to facilitate CBCT-to-CT synthesis for head-and-neck, lung, and breast cancer patients. Methods: Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. CBCTs were registered to planning CTs according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an u…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Tata Medical Center（塔塔医学中心）；Arden Cancer Centre（雅顿癌症中心）；University Hospitals Coventry and Warwickshire（考文垂与沃里克郡大学医院）
Prediction of survivability in a patient for tumor progression is useful to estimate the effectiveness of a treatment protocol. In our work, we present a model to take into account the heterogeneous nature of a tumor to predict survival. The tumor heterogeneity is measured in terms of its mass by combining information regarding the radiodensity obtained in images with the gross tumor volume (GTV). We propose a novel feature called Tumor Mass within a GTV (TMG), that improves the prediction of survivability, compared to existing models which use GTV. Weekly variation in TMG of a patient is comp…
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Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss
The Hong Kong Polytechnic University（香港理工大学）；Sun Yat-Sen University（中山大学）；Fourth Military Medical University（中国人民解放军空军军医大学）；Xijing Hospital（西京医院）；Queen Mary Hospital（玛丽医院）；Guangzhou University of Chinese Medicine（广州中医药大学）；Soochow University（苏州大学）
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