Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis

Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy

PURPOSE

Predicting nonalcoholic fatty liver disease (NAFLD) following pancreaticoduodenectomy (PD) is challenging, which delays therapeutic intervention and makes its prevention difficult. We conducted this study to assess the potential application of preoperative computed tomography (CT) radiomics for predicting NAFLD.

METHODS

The subjects of this retrospective study were 186 patients with PD from a single institution. We extracted the predictors of NAFLD after PD statistically from conventional clinical and radiomic features of the estimated remnant pancreas and whole liver region on preoperative nonenhanced CT images. Based on these predictors, we developed a machine-learning predictive model, which integrated clinical and radiomic features. A comparative model used only clinical features as predictors.

RESULTS

The incidence of NAFLD after PD was 43.5%. The variables of the clinicoradiomic model included one shape feature of the pancreas, two texture features of the liver, and sex; the variables of the clinical model were age, sex, and chemoradiotherapy. The accuracy%, precision%, recall%, F1 score, and area under the curve of the two models were 75.0, 72.7, 66.7, 69.6, and 0.80; and 69.6, 68.4, 54.2, 60.5, and 0.69, respectively.

CONCLUSIONS

Preoperative CT-derived radiomic features from the pancreatic and liver regions are promising for the prediction of NAFLD post-PD. Using these features enhances the predictive model, enabling earlier intervention for high-risk patients.

Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review

BACKGROUND

Risk evaluation of lymph node metastasis for early-stage (T1 and T2) colorectal cancers is critical for determining therapeutic strategies. Traditional methods of lymph node metastasis prediction have limited accuracy. This systematic review aimed to review the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers.

METHODS

A comprehensive search was performed of papers that evaluated the potential of artificial intelligence in predicting lymph node metastasis in early-stage colorectal cancers. Studies were appraised using the Joanna Briggs Institute tools. The primary outcome was summarizing artificial intelligence models and their accuracy. Secondary outcomes included influential variables and strategies to address challenges.

RESULTS

Of 3190 screened manuscripts, 11 were included, involving 8648 patients from 1996 to 2023. Due to diverse artificial intelligence models and varied metrics, no data synthesis was performed. Models included random forest algorithms, support vector machine, deep learning, artificial neural network, convolutional neural network and least absolute shrinkage and selection operator regression. Artificial intelligence models' area under the curve values ranged from 0.74 to 0.9993 (slide level) and 0.9476 to 0.9956 (single-node level), outperforming traditional clinical guidelines.

CONCLUSION

Artificial intelligence models show promise in predicting lymph node metastasis in early-stage colorectal cancers, potentially refining clinical decisions and improving outcomes.

PROSPERO REGISTRATION NUMBER

CRD42023409094.

Ultrasound-based radiomic analysis of the peripheral nerves for differentiation between CIDP and POEMS syndrome

BACKGROUND

Demyelinating peripheral neuropathy is characteristic of both polyneuropathy, organomegaly, endocrinopathy, M-protein, and skin changes (POEMS) syndrome and chronic inflammatory demyelinating polyneuropathy (CIDP). We hypothesized that the different pathogeneses underlying these entities would affect the sonographic imaging features.

PURPOSE

To investigate whether ultrasound (US)-based radiomic analysis could extract features to describe the differences between CIDP and POEMS syndrome.

MATERIAL AND METHODS

In this retrospective study, we evaluated nerve US images from 26 with typical CIDP and 34 patients with POEMS syndrome. Cross-sectional area (CSA) and echogenicity of the median and ulnar nerves were evaluated in each US image of the wrist, forearm, elbow, and mid-arm. Radiomic analysis was performed on these US images. All radiomic features were examined using receiver operating characteristic analysis. Optimal features were selected using a three-step feature selection method and were inputted into XGBoost to build predictive machine-learning models.

RESULTS

The CSAs were more enlarged in patients with CIDP than in those with POEMS syndrome without significant differences, except for that of the ulnar nerve at the wrist. Nerve echogenicity was significantly more heterogeneous in patients with CIDP than in those with POEMS syndrome. The radiomic analysis yielded four features with the highest area under the curve (AUC) value of 0.83. The machine-learning model showed an AUC of 0.90.

CONCLUSION

US-based radiomic analysis has high AUC values in differentiating POEM syndrome from CIDP. Machine-learning algorithms further improved the discriminative ability.

High-resolution ultrasound of peripheral neuropathies in rheumatological patients: An overview of clinical applications and imaging findings

Peripheral neuropathies are surprisingly common and can be associated with a number of conditions, including rheumatological diseases. Whether the co-existence of peripheral neuropathies with rheumatological disorders is coincidental or related to a common pathogenic mechanism, these disabling conditions can affect the outcome of rheumatological patients and should be targeted with specific treatment. The clinical presentation of peripheral neuropathy can be multifaceted and difficult to recognize in polysymptomatic patients. However, physicians adopting state-of-art diagnostic strategies, including nerve imaging, may improve the detection rate and management of neuropathies. In particular, a diagnostic approach relying exclusively on clinical history and nerve conduction studies may not be sufficient to disclose the etiology of the nerve damage and its anatomical location and thus requires integration with morphological studies. High-Resolution Ultrasound (HRUS) is increasingly adopted to support the diagnosis and follow-up of both joint disorders in rheumatology and peripheral neuropathies of different etiologies. In this review, the different types of nerve disorders associated with the most common syndromes of rheumatological interest are discussed, focusing on the distinctive sonographic features.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic cancer

Radiogenomics has attracted attention for predicting the molecular biological characteristics of tumors from clinical images, which are originally a collection of numerical values, such as computed tomography (CT) scans. A prediction model using genetic information is constructed using thousands of image features extracted and calculated from these numerical values. In the present study, RNA sequencing of pancreatic ductal adenocarcinoma (PDAC) tissues from 12 patients was performed to identify genes useful in evaluating clinical pathology, and 107 PDAC samples were immunostained to verify the obtained findings. In addition, radiogenomics analysis of gene expression was performed by machine learning using CT images and constructed prediction models. Bioinformatics analysis of RNA sequencing data identified integrin αV (ITGAV) as being important for clinicopathological factors, such as metastasis and prognosis, and the results of sequencing and immunostaining demonstrated a significant correlation (r=0.625, P=0.039). Notably, the ITGAV high‑expression group was associated with a significantly worse prognosis (P=0.005) and recurrence rate (P=0.003) compared with the low‑expression group. The ITGAV prediction model showed some detectability (AUC=0.697), and the predicted ITGAV high‑expression group was also associated with a worse prognosis (P=0.048). In conclusion, radiogenomics predicted the expression of ITGAV in pancreatic cancer, as well as the prognosis.

Prediction of the differences in tumor mutation burden between primary and metastatic lesions by radiogenomics

Tumor mutational burden (TMB) is gaining attention as a biomarker for responses to immune checkpoint inhibitors in cancer patients. In this study, we evaluated the status of TMB in primary and liver metastatic lesions in patients with colorectal cancer (CRC). In addition, the status of TMB in primary and liver metastatic lesions was inferred by radiogenomics on the basis of computed tomography (CT) images. The study population included 24 CRC patients with liver metastases. DNA was extracted from primary and liver metastatic lesions obtained from the patients and TMB values were evaluated by next‐generation sequencing. The TMB value was considered high when it equaled to or exceeded 10/100 Mb. Radiogenomic analysis of TMB was performed by machine learning using CT images and the construction of prediction models. In 7 out of 24 patients (29.2%), the TMB status differed between the primary and liver metastatic lesions. Radiogenomic analysis was performed to predict whether TMB status was high or low. The maximum values for the area under the receiver operating characteristic curve were 0.732 and 0.812 for primary CRC and CRC with liver metastasis, respectively. The sensitivity, specificity, and accuracy of the constructed models for TMB status discordance were 0.857, 0.600, and 0.682, respectively. Our results suggested that accurate inference of the TMB status is possible using radiogenomics. Therefore, radiogenomics could facilitate the diagnosis, treatment, and prognosis of patients with CRC in the clinical setting.