A multicenter clinical AI system study for detection and diagnosis of focal liver lesions

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies

GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn of precision medicine and developing technologies have reduced the mortality rates for GI malignancies, underscoring the main role of early detection methods for survival rate improvement. Artificial intelligence (AI) is a new technology that may improve GI cancer screening, treatment, and therapeutic efficiency for better patient care. AI could accelerate the development of targeted therapies by analyzing considerable data from the genome and identifying biomarkers connected with GI tumors. This opens up new avenues toward more tailored and personalized approaches, raising efficacy while reducing undesired side effects. For instance, AI may improve treatment outcomes by accurately predicting patient responses to therapeutic regimens, helping oncologists choose the most effective treatment options. This review will outline the transformative potential of AI in GI oncology by emphasizing the incorporation of AI-based technologies to enhance patient care.

Current State of Evidence for Use of MRI in LI-RADS

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this review, the pathogenesis of hepatocellular carcinoma and the use of MRI in LI-RADS is discussed, including specifically the LI-RADS diagnostic algorithm, its components, and its reproducibility with reference to the latest supporting evidence. The LI-RADS treatment response algorithms are reviewed, including the more recent radiation treatment response algorithm. The application of artificial intelligence, points of controversy, LI-RADS relative to other liver imaging systems, and possible future directions are explored. After reading this article, the reader will have an understanding of the foundation and application of LI-RADS as well as possible future directions.

Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning

To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT) images, the study included 11,013 annotated LNs with a short-axis diameter ≥ 3 mm from 626 head and neck cancer patients across four hospitals. The nnUNet model was used as a baseline, pre-trained on a large-scale head and neck dataset, and then fine-tuned with 4,729 LNs from hospital A for detection and segmentation. Validation was conducted on an internal testing cohort (ITC A) and three external testing cohorts (ETCs B, C, and D), with 1684 and 4600 LNs, respectively. Detection was evaluated via sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), while segmentation was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD95). For detection, the sensitivity, PPV, and FP/vol in ITC A were 54.6%, 69.0%, and 3.4, respectively. In ETCs, the sensitivity ranged from 45.7% at 3.9 FP/vol to 63.5% at 5.8 FP/vol. Segmentation achieved a mean DSC of 0.72 in ITC A and 0.72 to 0.74 in ETCs, as well as a mean HD95 of 3.78 mm in ITC A and 2.73 mm to 2.85 mm in ETCs. No significant sensitivity difference was found between contrast-enhanced and unenhanced CT images (p = 0.502) or repeated CT images (p = 0.815) during adaptive radiotherapy. The model's segmentation accuracy was comparable to that of experienced oncologists. The model shows promise in automatically detecting and segmenting neck LNs in CT images, potentially reducing oncologists' segmentation workload.

Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions

BACKGROUND & AIMS

Accurate multi-classification is a prerequisite for appropriate management of focal liver lesions (FLLs). Ultrasound is the most common imaging examination but lacks accuracy. Contrast-enhanced ultrasound (CEUS) offers better performance but is highly dependent on operator experience. Therefore, we aimed to develop a CEUS-based artificial intelligence (AI) model for FLL multi-classification and evaluate its performance in multicenter clinical tests.

METHODS

Since January 2017 to December 2023, CEUS videos, immunohistochemical biomarkers and clinical information on solid FLLs >1 cm in adults were collected from 52 centers to build and test the model. The model was developed to classify FLLs into six types: hepatocellular carcinoma, hepatic metastasis, intrahepatic cholangiocarcinoma, hepatic hemangioma, hepatic abscess and others. First, Module-Disease, Module-Biomarker and Module-Clinic were built in training set A and a validation set. Then, three modules were aggregated as Model-DCB in training set B and an internal test set. Model-DCB performance was compared with CEUS and MRI radiologists in three external test sets.

RESULTS

In total 3,725 FLLs from 52 centers were divided into training set A (n = 2,088), the validation set (n = 592), training set B (n = 234), the internal test set (n = 110), and external test sets A (n = 113), B (n = 276) and C (n = 312). In external test sets A, B and C, Model-DCB achieved significantly better performance (accuracy from 0.85 to 0.86) than junior CEUS radiologists (0.59-0.73), and comparable performance to senior CEUS radiologists (0.79-0.85) and senior MRI radiologists (0.82-0.86). In multiple subgroup analyses on demographic characteristics, tumor characteristics and ultrasound devices, its accuracy ranged from 0.79 to 0.92.

CONCLUSIONS

CEUS-based Model-DCB provides accurate multi-classification of FLLs. It holds promise for a wide range of populations, especially those in remote areas who have difficulty accessing MRI.

CLINICAL TRIAL

NCT04682886.

IMPACT AND IMPLICATIONS

Ultrasound is the most common imaging examination for screening focal liver lesions (FLLs), but it lacks accuracy for multi-classification, which is a prerequisite for appropriate clinical management. Contrast-enhanced ultrasound (CEUS) offers better diagnostic performance but relies on the experience of radiologists. We developed a CEUS-based model (Model-DCB) that can help junior CEUS radiologists to achieve comparable diagnostic ability as senior CEUS radiologists and senior MRI radiologists. The combination of an ultrasound device, CEUS examination and Model-DCB means that even patients in remote areas can be accurately diagnosed through examination by junior radiologists.

A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors

Primary malignant bone tumors are the third highest cause of cancer-related mortality among patients under the age of 20. X-ray scan is the primary tool for detecting bone tumors. However, due to the varying morphologies of bone tumors, it is challenging for radiologists to make a definitive diagnosis based on radiographs. With the recent advancement in deep learning algorithms, there is a surge of interest in computer-aided diagnosis of primary bone tumors. Nonetheless, the development in this field has been hindered by the lack of publicly available X-ray datasets for bone tumors. To tackle this challenge, we established the Bone Tumor X-ray Radiograph dataset (termed BTXRD) in collaboration with multiple medical institutes and hospitals. The BTXRD dataset comprises 3,746 bone images (1,879 normal and 1,867 tumor), with clinical information and global labels available for each image, and distinct mask and annotated bounding box for each tumor instance. This publicly available dataset can support the development and evaluation of deep learning algorithms for the diagnosis of primary bone tumors.

Automatic Classification of Focal Liver Lesions Based on Multi-Sequence MRI

Accurate and automated diagnosis of focal liver lesions is critical for effective radiological practice and patient treatment planning. This study presents a deep learning model specifically developed for classifying focal liver lesions across eight different MRI sequences, categorizing them into seven distinct classes. The model includes a feature extraction module that derives multi-level representations of the lesions, a feature fusion attention module to integrate contextual information from the various sequences, and an attention-guided data augmentation module to enrich the training dataset. The proposed model achieved a patient-wise classification accuracy of 0.9302 and a lesion-wise accuracy of 0.8592, along with an F1-score of 0.8395, a recall of 0.8296, and a precision of 0.8551. These findings demonstrate the effectiveness of combining multi-sequence MRI with advanced deep learning methodologies, providing a robust tool to support radiologists in accurately classifying liver lesions in clinical settings.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

BACKGROUND

Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options.

METHODOLOGY

We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning.

RESULTS

The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs.

CONCLUSIONS

Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.