Prospective study of dual-phase 99mTc-MIBI SPECT/CT nomogram for differentiating non-small cell lung cancer from benign pulmonary lesions

Introduction Artificial intelligence (AI) has been influencing the way oncology has been practiced. Major issues constituting a bottleneck are the lack of data for training purposes, confidentiality preventing development, or the absence of transparency in clarifying how models operate to generate decisions. Novel Models With explainable AI, trust and utilization barriers among clinicians, researchers, and patients can be removed. With the implementation of federated learning, multiple institutions could contribute to crucial dataset’s learning information. Precise diagnosis and prescription of the right drug are essential in preventing unnecessary life losses, and economic burden to the underling system. Focus This review focuses on new AI models that could make medical diagnosis more precise, quicker and convenient, as well as help with the discovery of new drugs and better selection of certain cancer therapies, in particular for those drugs whose results have been inconsistent across different groups of patients. We then speculate on the transformative role AI could play in predicting ADC therapy efficacy. This would ultimately bestow the medical field of an impeccable selection tool. Such colossal methodology, coupled with seeming monitoring of treatment and recovery, may be granting remedies that have been so longed, and deemed necessary. 

AI-Based Cancer Models in Oncology: From Diagnosis to ADC Drug Prediction

Background: The widespread use of high-resolution computed tomography (HRCT) in lung cancer screening has allowed for an increased detection rate of ground-glass nodules (GGNs) in the lung. Hence, obtaining the correct clinical diagnosis of benign and malignant GGNs has become crucial. Objectives: Most artificial intelligence and computer-aided diagnosis (AI-CAD) systems for the classification of pulmonary GGNs fail to extract CT features. This study used HRCT and AI to analyze the CT features of GGNs to improve the prediction of benign and malignant pulmonary GGNs. Patients and Methods: This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. A total of 204 patients with GGNs were recruited and divided into 2 groups according to their pathological results. Group A consisted of 69 cases with precursor glandular lesions (atypical adenomatous hyperplasia [AAH] and adenocarcinoma in situ [AIS]), inflammatory nodules, and benign nodules. Group B consisted of 135 cases with invasive lesions (minimally invasive adenocarcinoma [MIA], invasive adenocarcinoma [IAC], and other malignant lesions). Various CT features were compared between the 2 groups. The diagnostic efficacy of an AI-CAD system and radiologists’ reports for benign and malignant nodules were analyzed. A multivariate logistic regression analysis was performed to determine independent predictors of malignant GGN. A model that combined the AI system and manual extraction of radiological features was constructed. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the model. Results: Significant differences were found between malignant and benign groups according to the following 7 CT features: The GGN size (long and short diameters), vacuole sign, air bronchogram sign, vascular convergence sign, vascular perforator sign, interlobular septal obstruction sign, and spiculation (P &lt; 0.05). The volume and mean CT values of precursor glandular lesions of the lungs were significantly different from those of invasive lesions (P &lt; 0.05). The logistic regression model showed that the sensitivity and specificity of the AI system in diagnosing malignant groups were 0.756 and 0.696, respectively. The sensitivity and specificity of radiologists’ reports in diagnosing the malignant groups were 0.726 and 0.783, respectively. The combination of the 2 had a sensitivity of 0.768 and a specificity of 0.793. Conclusion: Prediction of the nature of GGNs based on CT features, including the vacuole sign, vascular perforator sign, and interlobular septal obstruction sign, were relatively accurate for a preliminary diagnosis. The AI system had a poorer diagnostic accuracy for GGNs than radiologists’ reports of CT images. The combination of AI and radiologists’ reports showed the highest diagnostic efficacy. 

pdf/diagnostic-value-of-combined-high-resolution-computed-44fuxg6hgp.pdf

Diagnostic Value of Combined High-resolution Computed Tomography and Artificial Intelligence-Aided Diagnosis System in Prediction of Benign and Malignant Pulmonary Ground-Glass Nodules

Purpose To develop a predictive model for identifying the higher-risk growth pattern of invasive lung adenocarcinoma using multiphase multidetector computed tomography (MDCT) and 18 F-fluorodeoxyglucose (FDG) PET radiomics. Methods A total of 203 patients with confirmed invasive lung adenocarcinoma between January 2018 and December 2021 were enrolled and randomly divided into training ( n = 143) and testing sets ( n = 60). Patients were classified into two groups according to the predominant growth pattern (lower-risk group: lepidic/acinar; higher-risk group: papillary/solid/micropapillary). Preoperative multiphase MDCT and 18 F-FDG PET images were evaluated. The Artificial Intelligence Kit software was used to extract radiomic features. Five predictive models [arterial phase, venous phase, and plain scan (AVP), PET, AVP-PET, clinical, and radiomic-clinical (Rad-Clin) combined model] were developed. The models’ performance was assessed using receiver-operating characteristic (ROC) curves and compared using the DeLong test. Results Among the radiomics models (AVP, PET, and AVP-PET), the AVP-PET model [area under ROC curve (AUC) = 0.888] outperformed the PET model (AUC = 0.814; P = 0.015) in predicting the higher-risk growth patterns. The combined Rad-Clin model (AUC = 0.923), which integrates AVP-PET radiomics and five independent clinical predictors (gender, spiculation, long-axis diameter, maximum standardized uptake value, and average standardized uptake value), exhibited superior performance in predicting the higher-risk growth pattern compared with radiomic models ( P = 0.043, vs. AVP-PET; P = 0.016, vs. AVP; P = 0.002, vs. PET) or the clinical model alone (constructing based on five clinical predictors; AUC = 0.793; P &lt; 0.001). Conclusion The combined Rad-Clin model can predict the higher-risk growth patterns of invasive adenocarcinoma (IAC). This approach could help determine individual therapeutic strategies for IAC patients by distinguishing predominant growth patterns with high risk. 

Predicting higher-risk growth patterns in invasive lung adenocarcinoma with multiphase multidetector computed tomography and 18F-fluorodeoxyglucose PET radiomics

Chest multi-bone segmentation plays an important role in clinical applications such as metastasis analysis and surgical planning. The global context between bones is critical for accurately identifying bone sub-classes; meanwhile, the influence of local details on bone segmentation accuracy cannot be ignored. However, the global context and local details are challenging to trade off under hardware and computation cost limitations. In this study, we propose Bone-Net that simultaneously leverages global context and local details by employing a two-stage cascaded framework for multi-bone segmentation in chest CT. In the first stage, we feed low-resolution large-scale images to bidirectional feature pyramid network to extract global context and perform multi-bone category prediction. In the second stage, we propose a global-guided gating module to link global context and local details to refine bone segmentation. During inference, we obtained the multi-bone segmentation results by rendering the category information into the segmentation results. Furthermore, we design a novel loss function to alleviate the category confusion problem for adjacent bones. The proposed method is trained and evaluated on a challenging dataset of 250 chest CT scans from various centers and scanners. Experimental results show that the proposed Bone-Net achieves high performance (84.1% of dice similarity coefficient, 87.7% of recall, and 80.7% of precision) and outperforms five benchmarks for multi-bone segmentation.

A Global-Local Cascade Network for Multi-bone Segmentation in Chest CT

Background Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice. Methods Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. Results The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies. Conclusions DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs. 

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study

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A Global-Local Cascade Network for Multi-bone Segmentation in Chest CT

Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study