Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy.

Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framework integrating pretreatment CT scans and H&E-stained biopsy images, for improved decision-making regarding neoadjuvant chemotherapy. We have constructed and tested iSCLM using retrospective data from 2,387 patients across 10 medical centers and evaluated its discriminative ability in a prospective cohort (132 patients; ChiCTR2300068917).

PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma.

Objectives: The clinical decision-making regarding choosing surgery alone (SA) or surgery followed by postoperative adjuvant chemotherapy (SPOCT) in esophageal squamous cell carcinoma (ESCC) remains controversial. We aim to propose a pre-therapy PET/CT image-based deep learning approach to improve the survival benefit and clinical management of ESCC patients.

Methods: This retrospective multicenter study included 837 ESCC patients from three institutions.

EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC.

We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset.

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC.

Purpose: To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC).

Methods: Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively.

A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.

Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges.

LWMA-Net: Light-weighted morphology attention learning for human embryo grading.

Visual inspection of embryo morphology is routinely used in embryo assessment and selection. However, due to the complexity of morphologies and large inter- and intra-observer variances among embryologists, manual evaluations remain to be subjective and time-consuming. Thus, we proposed a light-weighted morphology attention learning network (LWMA-Net) for automatic assistance on embryo grading.

Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma.

Purpose: We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naïve patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models.

Methods: EfficientNetV2 was used to build a prognosis model for treatment-naïve patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and validation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis.

A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study.

Background: For clinical decision making, it is crucial to identify patients with stage IV non-small cell lung cancer (NSCLC) who may benefit from tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). In this study, a deep learning-based system was designed and validated using pre-therapy computed tomography (CT) images to predict the survival benefits of EGFR-TKIs and ICIs in stage IV NSCLC patients.

Methods: This retrospective study collected data from 570 patients with stage IV EGFR-mutant NSCLC treated with EGFR-TKIs at five institutions between 2010 and 2021 (data of 314 patients were from a previously registered study), and 129 patients with stage IV NSCLC treated with ICIs at three institutions between 2017 and 2021 to build the ICI test dataset.

Automatic Lung Nodule Segmentation and Intra-Nodular Heterogeneity Image Generation.

Automatic segmentation of lung nodules on computed tomography (CT) images is challenging owing to the variability of morphology, location, and intensity. In addition, few segmentation methods can capture intra-nodular heterogeneity to assist lung nodule diagnosis. In this study, we propose an end-to-end architecture to perform fully automated segmentation of multiple types of lung nodules and generate intra-nodular heterogeneity images for clinical use.

Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study.

Purpose: To noninvasively evaluate the use of intratumoral and peritumoral regions from full-field digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) magnetic resonance imaging (MRI) images separately and combined to predict the Ki-67 level based on radiomics.

Procedures: A total of 209 patients with pathologically confirmed breast cancer were consecutively enrolled from September 2017 to March 2021, who underwent DM, DBT, DCE-MRI, and DW MRI scans. Radiomics features were calculated from intratumoral and peritumoral regions in each modality and selected with the least absolute shrinkage and selection operator (LASSO) regression.

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020.

Purpose: To evaluate the general rules and future trajectories of deep learning (DL) networks in medical image analysis through bibliometric and hot spot analysis of original articles published between 2012 and 2020.

Methods: Original articles related to DL and medical imaging were retrieved from the PubMed database. For the analysis, data regarding radiological subspecialties; imaging techniques; DL networks; sample size; study purposes, setting, origins and design; statistical analysis; funding sources; authors; and first authors' affiliation was manually extracted from each article.

Decoding intra-tumoral spatial heterogeneity on radiological images using the Hilbert curve.

Background: Current intra-tumoral heterogeneous feature extraction in radiology is limited to the use of a single slice or the region of interest within a few context-associated slices, and the decoding of intra-tumoral spatial heterogeneity using whole tumor samples is rare. We aim to propose a mathematical model of space-filling curve-based spatial correspondence mapping to interpret intra-tumoral spatial locality and heterogeneity.

Methods: A Hilbert curve-based approach was employed to decode and visualize intra-tumoral spatial heterogeneity by expanding the tumor volume to a two-dimensional (2D) matrix in voxels while preserving the spatial locality of the neighboring voxels.

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

Purpose: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma.

Methods: A total of 665 patients with lung adenocarcinoma (528/137) were recruited from two different institutions. In the training set, an 18-layer convolutional neural network (CNN) and fivefold cross-validation strategy were used to establish a CNN model.

Predictive Radiomic Models for the Chemotherapy Response in Non-Small-Cell Lung Cancer based on Computerized-Tomography Images.

The heterogeneity and complexity of non-small cell lung cancer (NSCLC) tumors mean that NSCLC patients at the same stage can have different chemotherapy prognoses. Accurate predictive models could recognize NSCLC patients likely to respond to chemotherapy so that they can be given personalized and effective treatment. We propose to identify predictive imaging biomarkers from pre-treatment CT images and construct a radiomic model that can predict the chemotherapy response in NSCLC.

Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies.

Purpose: To summarize the data of previously reported medical imaging features on human malignancies to provide a scientific basis for more credible imaging feature selection for future studies.

Materials And Methods: A search was performed in PubMed from database inception through March 23, 2018, for studies clearly stating the decoding of medical imaging features for malignancy-related objectives and/or hypotheses. The Newcastle-Ottawa scale was used for quality assessment of the included studies.

Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.

Purpose: To investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19.

Methods: Between 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists.

Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.

Importance: An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (NSCLC) is lacking.

Objective: To propose a clinically applicable large-scale bidirectional generative adversarial network for predicting the efficacy of EGFR-TKI therapy in patients with NSCLC.

Design, Setting, And Participants: This diagnostic/prognostic study enrolled 465 patients from January 1, 2010, to August 1, 2017, with follow-up from February 1, 2010, to June 1, 2020.

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

Purpose: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework.

Methods: A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA.

End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.

Purpose: In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time.

Methods: From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years).

A review of original articles published in the emerging field of radiomics.

Purpose: To determine the characteristics of and trends in research in the emerging field of radiomics through bibliometric and hotspot analyses of relevant original articles published between 2013 and 2018.

Methods: We evaluated 553 original articles concerning radiomics, published in a total of 61 peer-reviewed journals between 2013 and 2018. The following information was retrieved for each article: radiological subspecialty, imaging technique(s), machine learning technique(s), sample size, study setting and design, statistical result(s), study purpose, software used for feature calculation, funding declarations, author number, first author's affiliation, study origin, and journal name.

Radiomics for the prediction of EGFR mutation subtypes in non-small cell lung cancer.

Purpose: This retrospective study was designed to investigate the ability of radiomics to predict the mutation status of epidermal growth factor receptor (EGFR) subtypes (19Del and L858R) in patients with non-small cell lung cancer (NSCLC).

Methods: In total, 312 patients with NSCLC were included, and 580 radiomic features were extracted from the computed tomography images of each patient. In the training set, univariate analysis was performed on the clinical and radiomic features; logistic regression models were established using a 5-fold cross validation strategy for the prediction of EGFR subtypes 19Del and L858R.