Lung Cancer Diagnosis Based on Image Fusion and Prediction Using CT and PET Image

Rose, J. Dafni; Jaspin, K.; Vijayakumar, K.

doi:10.1007/978-981-15-6141-2_4

Cited by 18 publications

(3 citation statements)

References 26 publications

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“…For instance, measurements at the center of a group will have a significant degree of membership in that cluster. In contrast, a data point considerably farther from a cluster would have a low grade of membership in that cluster [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

Hosseini,

Shiri,

Ghaffarian

et al. 2024

Ann Nucl Med

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Purpose This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). Methods We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with ‘n_splits’ set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome. Results From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity. Conclusion Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

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Section: Methodsmentioning

confidence: 99%

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

Hosseini,

Shiri,

Ghaffarian

et al. 2024

Ann Nucl Med

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“…In recent years, deep learning as a powerful tool has been widely used in treatment outcome prediction and has achieved great success (Zhu et al 2020). Due to the constraints of GPU memory, many deep learning-based methods employ two-dimensional (2D) slices to predict treatment outcomes (Diamant et al 2019, Le et al 2020, Saha et al 2020, Rose et al 2021. However, since 2D convolutional neural networks (CNNs) take a single slice as input, they inherently fail to utilize the context from adjacent slices and therefore cannot fully use the volumetric information.…”

Section: Introductionmentioning

confidence: 99%

“…To solve the dilemma posed by 2D CNNs lacking volumetric information and 3D CNNs yielding too high a model complexity, in this paper, we proposed an end-to-end multi-modality and multi-view feature extension CNN method (MMFE) to predict LRR in H&N cancer. Multi-modality data are more comprehensive than single-modality data and can provide complementary information on different aspects of the patient (Branstetter et al 2005, Baltrušaitis et al 2018, Lv et al 2019, Rose et al 2021. For example, positron emission tomography (PET) images reveal the molecular metabolism activities within the human body, while computed tomography (CT) reflects the attenuation coefficient to x-rays (Guo et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion

Wang¹,

Guo²,

Zhou³

et al. 2022

Phys. Med. Biol.

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Objective. Locoregional recurrence (LRR) is one of the leading causes of treatment failure in head and neck (H&N) cancer. Accurately predicting LRR after radiotherapy is essential to achieving better treatment outcomes for patients with H&N cancer through developing personalized treatment strategies. We aim to develop an end-to-end multi-modality and multi-view feature extension method (MMFE) to predict LRR in H&N cancer. Approach. Deep learning (DL) has been widely used for building prediction models and has achieved great success. Nevertheless, 2D-based DL models inherently fail to utilize the contextual information from adjacent slices, while complicated 3D models have a substantially larger number of parameters, which require more training samples, memory and computing resources. In the proposed MMFE scheme, through the multi-view feature expansion and projection dimension reduction operations, we are able to reduce the model complexity while preserving volumetric information. Additionally, we designed a multi-modality convolutional neural network that can be trained in an end-to-end manner and can jointly optimize the use of deep features of CT, PET and clinical data to improve the model’s prediction ability. Main results. The dataset included 206 eligible patients, of which, 49 had LRR while 157 did not. The proposed MMFE method obtained a higher AUC value than the other four methods. The best prediction result was achieved when using all three modalities, which yielded an AUC value of 0.81. Significance. Comparison experiments demonstrated the superior performance of the MMFE as compared to other 2D/3D-DL–based methods. By combining CT, PET and clinical features, the MMFE could potentially identify H&N cancer patients at high-risk for LRR such that personalized treatment strategy can be developed accordingly.

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CAD of Brain Abnormalities in MRI Images Using Texture Features

Jaspin,

Selvan,

Vaishali

2024

Communications in Computer and Information Science

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Lung Cancer Diagnosis Based on Image Fusion and Prediction Using CT and PET Image

Cited by 18 publications

References 26 publications

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion

CAD of Brain Abnormalities in MRI Images Using Texture Features

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