Iron-based nanomaterials (INMs), due to their particular magnetic property, excellent biocompatibility, and functionality, have been developed into powerful tools in both tumor diagnosis and therapy. We give an overview here on how INMs such as iron oxide nanoparticles, element-doped nanocomposites, and iron-based organic frameworks (MOFs) display versatility for tumor imaging and therapy improvement. In terms of imaging, INMs improve the sensitivity and accuracy of techniques such as magnetic resonance imaging (MRI) and photoacoustic imaging (PAI) and support the development of multimodal imaging platforms. Regarding treatment, INMs play a key role in advanced strategies such as immunotherapy, magnetic hyperthermia, and synergistic combination therapy, which effectively overcome tumor-induced drug resistance and reduce systemic toxicity. The integration of INMs with artificial intelligence (AI) and radiomics further expands its capabilities for precise tumor identification, and treatment optimization, and amplifies treatment monitoring. INMs now link materials science with advanced computing and clinical innovations to enable next-generation cancer diagnostics and therapeutics.

The aim of this study was to conduct a systematic review and meta-analysis to comprehensively evaluate the performance and methodological quality of artificial intelligence (AI) in predicting recurrence after single first-line treatment for liver cancer.

A rigorous and systematic evaluation was conducted on the AI studies related to recurrence after single first-line treatment for liver cancer, retrieved from the PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases. The area under the curve (AUC), sensitivity (SENC), and specificity (SPEC) of each study were extracted for meta-analysis.

Six percutaneous ablation (PA) studies, 16 surgical resection (SR) studies, and 5 transarterial chemoembolization (TACE) studies were included in the meta-analysis for predicting recurrence after hepatocellular carcinoma (HCC) treatment, respectively. Four SR studies and 2 PA studies were included in the meta-analysis for recurrence after intrahepatic cholangiocarcinoma (ICC) and colorectal cancer liver metastasis (CRLM) treatment. The pooled SENC, SEPC, and AUC of AI in predicting recurrence after primary HCC treatment via PA, SR, and TACE were 0.78, 0.90, and 0.92; 0.81, 0.77, and 0.86; and 0.73, 0.79, and 0.79, respectively. The values for ICC treated with SR and CRLM treated with PA were 0.85, 0.71, 0.86 and 0.69, 0.63,0.74, respectively.

This systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment.

Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.

To develop a contrast-enhanced computed tomography (CECT) based radiomics model using machine learning method and assess its ability of preoperative prediction for the early recurrence of hepatocellular carcinoma (HCC).

A total of 297 patients confirmed with HCC were assigned to the training dataset and test dataset based on the 8:2 ratio, and the follow-up period of the patients was from May 2012 to July 2017. The lesion sites were manually segmented using ITK-SNAP, and the pyradiomics platform was applied to extract radiomic features. We established the machine learning model to predict the early recurrence of HCC. The accuracy, AUC, standard deviation, specificity, and sensitivity were applied to evaluate the model performance.

1,688 features were extracted from the arterial phase and venous phase images, respectively. When arterial phase and venous phase images were employed correlated with clinical factors to train a prediction model, it achieved the best performance (AUC with 95% CI 0.8300(0.7560-0.9040), sensitivity 89.45%, specificity 79.07%, accuracy 82.67%, p value 0.0064).

The CECT-based radiomics may be helpful to non-invasively reveal the potential connection between CECT images and early recurrence of HCC. The combination of radiomics and clinical factors could boost model performance.

In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.

To investigate the potential of the gut microbiome as a biomarker for predicting the early recurrence of HBV-related hepatocellular carcinoma (HCC), we enrolled 124 patients diagnosed with HBV-associated HCC and 82 HBV-related hepatitis, and 86 healthy volunteers in our study, collecting 292 stool samples for 16S rRNA sequencing and 35 tumor tissue samples for targeted metabolomics. We performed an integrated bioinformatics analysis of gut microbiome and tissue metabolome data to explore the gut microbial-liver metabolite axis associated with the early recurrence of HCC. We constructed a predictive model based on the gut microbiota and validated its efficacy in the temporal validation cohort. Dialister, Veillonella, the Eubacterium coprostanoligenes group, and Lactobacillus genera, as well as the Streptococcus pneumoniae and Bifidobacterium faecale species, were associated with an early recurrence of HCC. We also found that 23 metabolites, including acetic acid, glutamate, and arachidonic acid, were associated with the early recurrence of HCC. A comprehensive analysis of the gut microbiome and tissue metabolome revealed that the entry of gut microbe-derived acetic acid into the liver to supply energy for tumor growth and proliferation may be a potential mechanism for the recurrence of HCC mediated by gut microbe. We constructed a nomogram to predict early recurrence by combining differential microbial species and clinical indicators, achieving an AUC of 78.0%. Our study suggested that gut microbes may serve as effective biomarkers for predicting early recurrence of HCC, and the gut microbial-tumor metabolite axis may explain the potential mechanism by which gut microbes promote the early recurrence of HCC.

Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors.

The PubMed database was searched for papers published in the English language no earlier than January 2018.

We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective.

It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.

To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.

The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice.

Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.

The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

Early recurrence (ER) affects the long-term survival prognosis of patients with hepatocellular carcinoma (HCC). Many previous studies have utilized CT/MRI-based radiomics to predict ER after radical treatment, achieving high predictive value. However, the diagnostic performance of radiomics for the preoperative identification of ER remains uncertain. Therefore, we aimed to perform a meta-analysis to investigate the predictive performance of radiomics for ER in HCC.

A systematic literature search was conducted in PubMed, Web of Science (including MEDLINE), EMBASE and the Cochrane Central Register of Controlled Trials to identify studies that utilized radiomics methods to assess ER in HCC. Data were extracted and quality assessed for retrieved studies. Statistical analyses included pooled data, tests for heterogeneity, and publication bias. Meta-regression and subgroup analyses were performed to investigate potential sources of heterogeneity.

The analysis included fifteen studies involving 3,281 patients focusing on preoperative CT/MRI-based radiomics for the prediction of ER in HCC. The combined sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic were 75% (95% CI: 65-82), 78% (95% CI: 68-85), and 83% (95% CI: 79-86), respectively. The combined positive likelihood ratio, negative likelihood ratio, diagnostic score, and diagnostic odds ratio were 3.35 (95% CI: 2.41-4.65), 0.33 (95% CI: 0.25-0.43), 2.33 (95% CI: 1.91-2.75), and 10.29 (95% CI: 6.79-15.61), respectively. Substantial heterogeneity was observed among the studies (I²=99%; 95% CI: 99-100). Meta-regression showed imaging equipment contributed to the heterogeneity of specificity in subgroup analysis (P= 0.03).

Preoperative CT/MRI-based radiomics appears to be a promising and non-invasive predictive approach with moderate ER recognition performance.