Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer

Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study

OBJECTIVES

To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.

METHODS

A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2DBB), and 3D bounding box (3DBB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves.

RESULTS

For radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3DBB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2DBB (p = 0.0372) and 3D models (p = 0.0380) for pDFS.

CONCLUSION

Radiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction.

CRITICAL RELEVANCE STATEMENT

For quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries.

KEY POINTS

There is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.

Different impacts of adipose tissue dynamics on prognosis in patients with resectable locally advanced rectal cancer treated with and without neoadjuvant treatment

Background

Body composition is recognized to be associated with clinical outcomes in patients with locally advanced rectal cancer (LARC). This study aimed to determine the prognostic role of regional adipose tissue distribution in patients with resectable LARC treated with or without neoadjuvant chemoradiotherapy (nCRT).

Methods

This retrospective study included 281 consecutive patients who underwent radical surgery for LARC with or without preoperative nCRT between 2013 and 2019. Patients underwent contrast-enhanced CT scans before nCRT and before surgery. Visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (aSAT), and gluteal subcutaneous adipose tissue (gSAT) were quantified on the CT images. The association of adipose tissue distribution with progression-free survival (PFS) was analyzed using Cox proportional hazards analysis.

Results

A total of 102 nCRT-treated and 179 primarily resected patients were included. During a median follow-up period of 24 months, 74 (26.3%) patients experienced local recurrence or metastasis. Multivariable analysis showed that VAT was associated with PFS in all patients (hazard ratio [HR] 1.28, 95% confidence interval [CI] 1.04-1.57; P = 0.021). This association was only maintained in primarily resected patients (HR 1.31, 95% CI 1.02-1.69; P = 0.037). For patients receiving preoperative nCRT, VAT was not significantly associated with PFS, while the dynamic change in gSAT (ΔgSAT) between nCRT and surgery was associated with PFS (HR 0.43, 95%CI 0.27-0.69, P = 0.001).

Conclusion

Visceral obesity is an adverse prognostic factor in patients with resectable LARC treated by primary resection, while increased gluteal subcutaneous adiposity during preoperative nCRT may indicate favorable clinical outcomes.

Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study

Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

A multicenter study: predicting KRAS mutation and prognosis in colorectal cancer through a CT-based radiomics nomogram

PURPOSE

To establish a CT-based radiomics nomogram for preoperative prediction of KRAS mutation and prognostic stratification in colorectal cancer (CRC) patients.

METHODS

In a retrospective analysis, 408 patients with confirmed CRC were included, comprising 168 cases in the training set, 111 cases in the internal validation set, and 129 cases in the external validation set. Radiomics features extracted from the primary tumors were meticulously screened to identify those closely associated with KRAS mutation. Subsequently, a radiomics nomogram was constructed by integrating these radiomics features with clinically significant parameters. The diagnostic performance was assessed through the area under the receiver operating characteristic curve (AUC). Lastly, the prognostic significance of the nomogram was explored, and Kaplan-Meier analysis was employed to depict survival curves for the high-risk and low-risk groups.

RESULTS

A radiomics model was constructed using 19 radiomics features significantly associated with KRAS mutation. Furthermore, a nomogram was developed by integrating these radiomics features with two clinically significant parameters (age, tumor location). The nomogram achieved AUCs of 0.834, 0.813, and 0.811 in the training set, internal validation set, and external validation set, respectively. Additionally, the nomogram effectively stratified patients into high-risk (KRAS mutation) and low-risk (KRAS wild-type) groups, demonstrating a significant difference in overall survival (P < 0.001). Patients categorized in the high-risk group exhibited inferior overall survival in contrast to those classified in the low-risk group.

CONCLUSIONS

The CT-based radiomics nomogram demonstrates the capability to effectively predict KRAS mutation in CRC patients and stratify their prognosis preoperatively.

Added value of DCER-features to clinicopathologic model for predicting metachronous metastases in rectal cancer patients

RATIONALE AND OBJECTIVES

The study aimed to investigate whether dynamic contrast-enhanced MRI parameters and preoperative radiological features (DCER-Features) add value to the clinicopathologic model for predicting metachronous metastases in rectal cancer patients.

MATERIALS AND METHODS

From January 2014 to December 2020, 859 patients in the PACS system were retrospectively screened. Of the initial 722 patients with surgically confirmed rectal cancer and no synchronous metastases, 579 patients were excluded for various reasons such as lack of clinicopathological or radiological information. 143 patients were finally included in this study. And 73 Patients of them developed metachronous metastasis within five years. After stepwise multiple regression analyses, we constructed three distinct models. Model 1 was developed solely based on clinicopathological factors, and model 2 incorporated clinicopathological characteristics along with DCE-MRI parameters. Finally, model 3 was built on all available factors, including clinicopathological characteristics, DCE-MRI parameters, and radiological features based on rectal magnetic resonance imaging. The radiological features assessed in this study encompass tumor imaging staging, location, and circumferential resection margin (CRM) for primary tumors, as well as the number of visible lymph nodes and suspected metastatic lymph nodes. Receiver operating characteristic (ROC) and decision curve analysis (DCA) were conducted to evaluate whether the diagnostic efficiency was improved.

RESULTS

The performance of model 3 (including clinicopathologic characteristics and DCER-Features) was the best (AUC: 0.856, 95% CI 0.778-0.886), whereas it was 0.796 (95% CI 0.720-0.828) for model 2 and 0.709 (95% CI 0.612-0.778) for model 1 (DeLong test: model 1 vs model 2, p = 0.004; model 2 vs model 3, p = 0.037; model 1 vs model 3, p < 0.001). The decision curves indicated that the net benefit of model 3 was higher than the other two models at each referral threshold. The calibration plot of the three models revealed an excellent predictive accuracy.

CONCLUSION

This study suggests that DCER-Features have added value for the clinicopathological model to predict metachronous metastasis in patients with rectal cancers.

Pretreatment MRI-Based Radiomics for Prediction of Rectal Cancer Outcome: A Discovery and Validation Study

RATIONALE AND OBJECTIVES

Accurate prediction of local recurrence or distant metastasis is critical for developing individualized therapies for locally advanced rectal cancer (LARC) patients after standard therapy. This study aims to develop and validate a multiparameter MRI-based radiomics signature (RS) for prognostic prediction in LARC patients receiving neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME) and to explore the ability of RS for personalized survival risk stratification.

MATERIALS AND METHODS

In this multi-center study, 454 patients who received nCRT and TME and completed 3 years of follow-up participated. RS was constructed for prognostic prediction based on features extracted from pretreatment multiparameter MRI in a training cohort (TC; n = 298), which was tested in an internal validation cohort (IVC; n = 75) and further validated in an independent external validation cohort (EVC; n = 81). Furthermore, the ability of RS for personalized survival risk stratification was explored using the Kaplan-Meier survival curves.

RESULTS

The RS model showed satisfactory accuracy for prognostic prediction with AUCs of 0.83, 0.81 and 0.82 in the TC, IVC and EVC, respectively. In addition, RS helped to refine risk stratification for LARC patients on the basis of significantly different 3-year disease-free survival rates, independent of their pathological stage, pre-surgery CEA, and even treatment modality.

CONCLUSIONS

The proposed RS can be used not only to predict local recurrence or distant metastasis but also to serve as an effective postoperative survival risk stratification tool for clinicians to facilitate decision-making for LARC patients receiving standard treatment.

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer

OBJECTIVES

To explore the value of multi-parametric MRI (mp-MRI) radiomic model for preoperative prediction of recurrence and/or metastasis (RM) as well as survival benefits in patients with rectal cancer.

METHODS

A retrospective analysis of 234 patients from two centers with histologically confirmed rectal adenocarcinoma was conducted. All patients were divided into three groups: training, internal validation (in-vad) and external validation (ex-vad) sets. In the training set, radiomic features were extracted from T2WI, DWI, and contrast enhancement T1WI (CE-T1) sequence. Radiomic signature (RS) score was then calculated for feature screening to construct a rad-score model. Subsequently, preoperative clinical features with statistical significance were selected to construct a clinical model. Independent predictors from clinical and RS related to RM were selected to build the combined model and nomogram.

RESULTS

After feature extraction, 26 features were selected to construct the rad-score model. RS (OR = 0.007, p < 0.01), MR-detected T stage (mrT) (OR = 2.92, p = 0.03) and MR-detected circumferential resection margin (mrCRM) (OR = 4.70, p = 0.01) were identified as independent predictors of RM. Then, clinical model and combined model were constructed. ROC curve showed that the AUC, accuracy, sensitivity and specificity of the combined model were higher than that of the other two models in three sets. Kaplan-Meier curves showed that poorer disease-free survival (DFS) time was observed for patients in pT3-4 stages with low RS score (p < 0.001), similar results were also found in pCRM-positive patients (p < 0.05).

CONCLUSION

The mp-MRI radiomics model can be served as a noninvasive and accurate predictors of RM in rectal cancer that may support clinical decision-making.

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework

PURPOSE

Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians.

METHODS

A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs.

RESULTS

At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm).

CONCLUSIONS

The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Prediction by a multiparametric magnetic resonance imaging-based radiomics signature model of disease-free survival in patients with rectal cancer treated by surgery

Objective

The aim of this study was to assess the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics signature model to predict disease-free survival (DFS) in patients with rectal cancer treated by surgery.

Materials and methods

We evaluated data of 194 patients with rectal cancer who had undergone radical surgery between April 2016 and September 2021. The mean age of all patients was 62.6 ± 9.7 years (range: 37-86 years). The study endpoint was DFS and 1132 radiomic features were extracted from preoperative MRIs, including contrast-enhanced T1- and T2-weighted imaging and apparent diffusion coefficient values. The study patients were randomly allocated to training (n=97) and validation cohorts (n=97) in a ratio of 5:5. A multivariable Cox regression model was used to generate a radiomics signature (rad score). The associations of rad score with DFS were evaluated using Kaplan-Meier analysis. Three models, namely a radiomics nomogram, radiomics signature, and clinical model, were compared using the Akaike information criterion.

Result

The rad score, which was composed of four MRI features, stratified rectal cancer patients into low- and high-risk groups and was associated with DFS in both the training (p = 0.0026) and validation sets (p = 0.036). Moreover, a radiomics nomogram model that combined rad score and independent clinical risk factors performed better (Harrell concordance index [C-index] =0.77) than a purely radiomics signature (C-index=0.73) or clinical model (C-index=0.70).

Conclusion

An MRI radiomics model that incorporates a radiomics signature and clinicopathological factors more accurately predicts DFS than does a clinical model in patients with rectal cancer.

The role of adjuvant chemotherapy in rectal cancer patients with ypT0-2N0 after neoadjuvant chemoradiotherapy

Background

Neoadjuvant chemoradiotherapy has emerged as the established treatment for locally advanced rectal cancer. Nevertheless, there remains a debate regarding the necessity of adjuvant chemotherapy for patients with locally advanced rectal cancer who exhibit a favorable tumor response (ypT0-2N0) after neoadjuvant chemoradiotherapy and surgery. Thus, the objective of this study is to investigate the impact of adjuvant chemotherapy on the oncological prognosis of rectal cancer patients who have a good response to neoadjuvant chemoradiotherapy.

Materials and methods

The study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Articles were searched in the Web of Science, PubMed, and Cochrane Library databases. The primary outcomes assessed were 5-year overall survival, disease-free survival, cancer-specific survival, recurrence-free survival, local recurrence, and distant metastasis. The data was summarized using a random effects model.

Results

A meta-analysis was conducted using 18 retrospective studies published between 2009 and 2023. The studies included 9 from China and 5 from Korea, involving a total of 6566 patients with ypT0-2N0 rectal cancer after neoadjuvant chemoradiotherapy. The pooled data revealed that adjuvant chemotherapy significantly improved 5-year overall survival (OR=1.75, 95% CI: 1.15-2.65, P=0.008), recurrence-free survival (OR=1.73, 95% CI: 1.20-2.48, P=0.003), and reduced distant metastasis (OR=0.68, 95% CI: 0.51-0.92, P=0.011). However, adjuvant chemotherapy did not have a significant effect on disease-free survival, cancer-specific survival, and local recurrence in ypT0-2N0 rectal cancer. Subgroup analysis indicated that adjuvant chemotherapy was beneficial in improving overall survival for ypT1-2N0 rectal cancer (OR=1.89, 95% CI: 1.13-3.19, P=0.003).

Conclusion

The findings of the meta-analysis suggest that adjuvant chemotherapy may provide benefits in terms of oncological outcomes for rectal cancer patients with ypT0-2N0 after neoadjuvant chemoradiotherapy and radical surgery. However, further prospective clinical studies are needed to confirm these findings.

Development and validation of a nomogram for Siewert II esophagogastric junction adenocarcinoma: a retrospective analysis

Background

Due to the complex histological type and anatomical structures, there has been considerable debate on the classification of adenocarcinoma of the esophagogastric junction (AEG), especially Siewert II AEG. Furthermore, neither the American Joint Committee on Cancer (AJCC) 7th tumor-node-metastasis (TNM) [esophageal adenocarcinoma (E) or gastric cancer (G)] nor the AJCC 8th TNM (E or G) accurately predicted the prognosis of patients with Siewert II AEG.

Objective

This study aimed to investigate the factors influencing the survival and prognosis of patients with Siewert II AEG and establish a new and better prognostic predictive model.

Design

A retrospective study.

Methods

Patients with Siewert II AEG, retrieved from the Surveillance, Epidemiology, and End Results (SEER) databases, were assigned to the training set. Patients retrieved from a single tertiary medical center were assigned to the external validation set. Significant variables were selected using univariate and multivariate Cox regression analyses to construct the nomogram. Nomogram models were assessed using the concordance index (C-index), a calibration plot, decision curve analysis (DCA), and external validation.

Results

Age, tumor grade, and size, as well as the T, N, and M stages, were included in the nomograms. For the SEER training set, the C-index of the nomogram was 0.683 (0.665-0.701). The C-index of the nomogram for the external validation set was 0.690 (0.653-0.727). The calibration curve showed good agreement between the nomogram estimations and actual observations in both the training and external validation sets. The DCA showed that the nomogram was clinically useful.

Conclusion

The new predictive model showed significant accuracy in predicting the prognosis of Siewert II AEG.

A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram

OBJECTIVE

Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment.

METHODS

In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression. A Rad-Clinical model for postoperative PFS was further constructed according to the Rad-signature and clinical variables by Cox regression analysis. It was presented as a nomogram and evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. And further evaluate the application value of Rad-Clinical model in clinical stages and targeted sequential therapy of HCC.

RESULTS

Tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, and radiomics score (Rad-score) were found to be independent risk factors for PFS after DEB-TACE treatment for unresectable HCC, with the Rad-Clinical model being the greatest predictor of PFS in these patients (hazard ratio: 2.08; 95% confidence interval: 1.56-2.78; P < 0.001) along with high 6 months, 12 months, 18 months, and 24 months area under the curves of 0.857, 0.810, 0.843, and 0.838, respectively. In addition, compared to the radiomics and clinical nomograms, the Radiological-Clinical nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (45.2%, 95% CI 0.260-0.632, p < 0.05) and integrated discrimination improvement (14.9%, 95% CI 0.064-0.281, p < 0.05). Based on this model, low-risk patients had higher PFS than high-risk patients in BCLC-B and C stages (P = 0.021). Targeted sequential therapy for patients with high and low-risk HCC in BCLC-B stage exhibited significant benefits (P = 0.018, P = 0.012), but patients with high-risk HCC in BCLC-C stage did not benefit much (P = 0.052).

CONCLUSION

The Rad-Clinical model may be favorable for predicting PFS in patients with unresectable HCC treated with DEB-TACE and for identifying patients who may benefit from targeted sequential therapy.

Prediction and related genes of cancer distant metastasis based on deep learning

Cancer metastasis is one of the main causes of cancer progression and difficulty in treatment. Genes play a key role in the process of cancer metastasis, as they can influence tumor cell invasiveness, migration ability and fitness. At the same time, there is heterogeneity in the organs of cancer metastasis. Breast cancer, prostate cancer, etc. tend to metastasize in the bone. Previous studies have pointed out that the occurrence of metastasis is closely related to which tissue is transferred to and genes. In this paper, we identified genes associated with cancer metastasis to different tissues based on LASSO and Pearson correlation coefficients. In total, we identified 45 genes associated with bone metastases, 89 genes associated with lung metastases, and 86 genes associated with liver metastases. Through the expression of these genes, we propose a CNN-based model to predict the occurrence of metastasis. We call this method MDCNN, which introduces a modulation mechanism that allows the weights of convolution kernels to be adjusted at different positions and feature maps, thereby adaptively changing the convolution operation at different positions. Experiments have proved that MDCNN has achieved satisfactory prediction accuracy in bone metastasis, lung metastasis and liver metastasis, and is better than other 4 methods of the same kind. We performed enrichment analysis and immune infiltration analysis on bone metastasis-related genes, and found multiple pathways and GO terms related to bone metastasis, and found that the abundance of macrophages and monocytes was the highest in patients with bone metastasis.

KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients

BACKGROUND

Mutated KRAS may indicate an invasive nature and predict prognosis in locally advanced rectal cancer (LARC). We aimed to establish a radiomic model using pretreatment T2W MRIs to predict KRAS status and explore the association between the KRAS status or model predictions and lung metastasis.

METHODS

In this retrospective multicentre study, LARC patients from two institutions between January 2012 and January 2019 were randomly divided into training and testing cohorts. Least absolute shrinkage and selection operator (LASSO) regression and the support vector machine (SVM) classifier were utilized to select significant radiomic features and establish a prediction model, which was validated by radiomic score distribution and decision curve analysis. The association between the model stratification and lung metastasis was investigated by Cox regression and Kaplan‒Meier survival analysis; the results were compared by the log-rank test.

RESULTS

Overall, 103 patients were enrolled (73 and 30 in the training and testing cohorts, respectively). The median follow-up was 38.1 months (interquartile range: 26.9, 49.4). The radiomic model had an area under the curve (AUC) of 0.983 in the training cohort and 0.814 in the testing cohort. Using a cut-off of 0.679 defined by the receiver operating characteristic (ROC) curve, patients with a high radiomic score (RS) had a higher risk for lung metastasis (HR 3.565, 95% CI 1.337, 9.505, p = 0.011), showing similar predictive performances for the mutant and wild-type KRAS groups (HR 3.225, 95% CI 1.249, 8.323, p = 0.016, IDI: 1.08%, p = 0.687; NRI 2.23%, p = 0.766).

CONCLUSIONS

We established and validated a radiomic model for predicting KRAS status in LARC. Patients with high RS experienced more lung metastases. The model could noninvasively detect KRAS status and may help individualize clinical decision-making.

Presurgical MRI-Based Radiomics Models for Predicting Cerebellar Mutism Syndrome in Children With Posterior Fossa Tumors

BACKGROUND

Current studies have indicated that tumoral morphologic features are associated with cerebellar mutism syndrome (CMS), but the radiomics application in CMS is scarce.

PURPOSE

To develop a model for CMS discrimination based on multiparametric MRI radiomics in patients with posterior fossa tumors.

STUDY TYPE

Retrospective.

POPULATION

A total of 218 patients (males 132, females 86) with posterior fossa tumors, 169 of which were included in the MRI radiomics analysis. The MRI radiomics study cohort (169) was split into training (119) and testing (50) sets with a ratio of 7:3.

FIELD/SEQUENCE

All the MRI were acquired under 1.5/3.0 T scanners. T2-weighted image (T2W), T1-weighted (T1W), fluid attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI).

ASSESSMENT

Apparent diffusion coefficient (ADC) maps were generated from DWI. Each MRI dataset generated 1561 radiomics characteristics. Feature selection was performed with univariable logistic analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO) penalized logistic regression. Significant clinical features were selected with multivariable logistic analysis and used to constructed the clinical model. Radiomics models (based on T1W, T2W, FLAIR, DWI, ADC) were constructed with selected radiomics features. The mix model was based on the multiparametric MRI radiomics features.

STATISTICAL TEST

Multivariable logistic analysis was utilized during clinical features selection. Models' performance was evaluated using the area under the receiver operating characteristic (AUC) curve. Interobserver variability was assessed using Cohen's kappa. Significant threshold was set as P < 0.05.

RESULTS

Sex (aOR = 3.72), tumor location (aOR = 2.81), hydrocephalus (aOR = 2.14), and tumor texture (aOR = 5.08) were significant features in the multivariable analysis and were used to construct the clinical model (AUC = 0.79); totally, 33 radiomics features were selected to construct radiomics models (AUC = 0.63-0.93). Seven of the 33 radiomics features were selected for the mix model (AUC = 0.93).

DATA CONCLUSION

Multiparametric MRI radiomics may be better at predicting CMS than single-parameter MRI models and clinical model.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

2.

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer

BACKGROUND

Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients.

METHODS

We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated.

RESULTS

The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful.

CONCLUSIONS

We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

A large-scale study integrating nutritional indicators and clinicopathological parameters to evaluate prognosis, follow-up, and postoperative chemotherapy decisions in rectal cancer patients

OBJECTIVE

The aim of this study was to evaluate the role of nutritional indicators and clinicopathological parameters in predicting the progression and prognosis for pathological stage II-III rectal cancer (RC) patients without neoadjuvant radiotherapy. In addition, we sought to explore the high-risk population who may require postoperative chemotherapy.

METHODS

A total of 894 consecutive RC patients were enrolled in this study. Univariate and multivariate Cox analysis were performed to identify the independent risk factors for PFS and OS. The nomogram and calibration curves were conducted according to multivariable analysis result. Kaplan-Meier survival curves and log-rank tests were performed for different groups. Finally, random survival forest (RSF) model was developed to predict the probability of progression.

RESULTS

Our results revealed that CEA level, pathological stage, tumor deposit, and PNI were independently associated with PFS in RC patients. Similarly, the results indicated that CEA level, pathological stage, tumor deposit, PNI, and NRI were independently associated with OS. RSF model revealed that group 1 had the highest risk of progression at the 12th month of follow-up, group 2 had the highest risk of progression at the 15th month of follow-up, while group 3 had the highest risk of progression at the 9th month of follow-up. Besides, subgroup analysis suggested that the high-risk group needs postoperative adjuvant chemotherapy, while patients in the low- and moderate-risk groups may not need postoperative adjuvant chemotherapy. Finally, we validated our results with the SEER database.

CONCLUSIONS

In conclusion, we demonstrated that preoperative nutritional indicator and clinicopathological parameters could act as auxiliary prognostication tools for RC patients without neoadjuvant radiotherapy. We also established follow-up strategies for different groups of patients. Collectively, incorporating nutritional assessment into risk stratification for RC resection is crucial and should be an integral part of preoperative planning.

A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study

PURPOSE

To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation.

METHODS

558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated.

RESULTS

In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram.

CONCLUSION

A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.

Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study

PURPOSE

Adjuvant therapy is recommended to minimize the risk of distant metastasis (DM) and local recurrence (LR) in patients with locally advanced rectal cancer (LARC). However, its role is controversial. We aimed to develop a pretreatment MRI-based deep learning model to predict LR, DM, and overall survival (OS) over 5 years after surgery and to identify patients benefitting from adjuvant chemotherapy (AC).

MATERIALS AND METHODS

The multi-survival tasks network (MuST) model was developed in a primary cohort (n = 308) and validated using two external cohorts (n = 247, 245). An AC decision tree integrating the MuST-DM score, perineural invasion (PNI), and preoperative carbohydrate antigen 19-9 (CA19-9) was constructed to assess chemotherapy benefits and aid personalized treatment of patients. We also quantified the prognostic improvement of the decision tree.

RESULTS

The MuST network demonstrated high prognostic accuracy in the primary and two external cohorts for the prediction of three different survival tasks. Within the stratified analysis and decision tree, patients with CA19-9 levels > 37 U/mL and high MuST-DM scores exhibited favorable chemotherapy efficacy. Similar results were observed in PNI-positive patients with low MuST-DM scores. PNI-negative patients with low MuST-DM scores exhibited poor chemotherapy efficacy. Based on the decision tree, 14 additional patients benefiting from AC and 391 patients who received over-treatment were identified in this retrospective study.

CONCLUSION

The MuST model accurately and non-invasively predicted OS, DM, and LR. A specific and direct tool linking chemotherapy decisions and benefit quantification has also been provided.

Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer

BACKGROUND

To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI).

METHODS

In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87).

CONCLUSIONS

Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

BACKGROUND

To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer.

METHODS

Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms.

RESULTS

Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians.

CONCLUSION

The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

A web-based nomogram to predict overall survival for postresection leiomyosarcoma patients with lung metastasis

To investigate the overall survival of post-resection leiomyosarcoma (LMS) patients with lung metastasis, data of post-resection LMS patients with lung metastasis between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The clinical characteristics and survival data for post-resection LMS patients with lung metastasis at Tianjin Medical University Cancer Hospital & Institute (TJMUCH) between October 2010 and July 2018 were collected. Patients derived from the SEER database and TJMUCH were divided into training and validation cohorts, respectively. Univariate and multivariate Cox regression analyses were performed and a nomogram was established. The area under the curve (AUC) and the calibration curve were used to evaluate the nomogram. A web-based nomogram was developed based on the established nomogram. Eventually, 226 patients from the SEER database who were diagnosed with LMS and underwent primary lesion resection combined with lung metastasis were enrolled in the training cohort, and 17 patients from TJMUCH were enrolled in the validation cohort. Sex, race, grade, tumor size, chemotherapy, and bone metastasis were correlated with overall survival in patients with LMS. The C-index were 0.65 and 0.75 in the SEER and Chinese set, respectively. Furthermore, the applicable AUC values of the ROC curve in the SEER cohort to predict the 1-, 3-, 5- years survival rate were 0.646, 0.682, and 0.689, respectively. The corresponding AUC values in the Chinese cohort were 0.970, 0.913, and 0.881, respectively. The calibration curve showed that the nomogram performed well in predicting the overall survival in post-resection LMS patients with lung metastasis. A web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) was established. The web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) is an accurate and personalized tool for predicting the overall survival of post-resection LMS with lung metastasis.

Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI

AIM

To explore the potential of the joint radiomics analysis of positron-emission tomography (PET) and magnetic resonance imaging (MRI) of primary tumours for predicting the risk of synchronous distant metastasis (SDM) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

18F-FDG PET and MRI images of PDAC patients from January 2011 to December 2020 were collected retrospectively. Patients (n=66) who received 18F-FDG PET/CT and MRI were included in a development group. Patients (n=25) scanned with hybrid PET/MRI were incorporated in an external test group. A radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm to select PET-MRI radiomics features of primary PDAC tumours. A radiomics nomogram was developed by combining the radiomics signature and important clinical indicators using univariate and multivariate analysis to assess patients' metastasis risk. The nomogram was verified with the employment of an external test group.

RESULTS

Regarding the development cohort, the radiomics nomogram was found to be better for predicting the risk of distant metastasis (area under the curve [AUC]: 0.93, sensitivity: 87%, specificity: 85%) than the clinical model (AUC: 0.70, p<0.001; sensitivity:70%, specificity: 65%) and the radiomics signature (AUC: 0.89, p>0.05; sensitivity: 65%, specificity:100%). Concerning the external test cohort, the radiomics nomogram yielded an AUC of 0.85.

CONCLUSION

PET-MRI based radiomics analysis exhibited effective prediction of the risk of SDM for preoperative PDAC patients and may offer complementary information and provide hints for cancer staging.

The benefits of adjuvant chemotherapy are associated with the kind of neoadjuvant therapy in stage ypI rectal cancer: evidence based on population analysis

PURPOSE

The oncological role of adjuvant chemotherapy (ACT) remains debated in locally advanced rectal cancer (RC) after neoadjuvant therapy (NAT), especially ypI RC. In this study, we used population-based data to evaluate the benefits of ACT in stage ypI RC after NAT and surgery. Moreover, we tried to differentiate what kind of NAT (radiotherapy alone or chemoradiotherapy) was administered because this may affect the further efficacy of ACT.

METHODS

All patients with stage ypI primary rectal malignancy were diagnosed in the SEER database between 2004 and 2017. The Kaplan-Meier method was applied to estimate the effects of ACT in survival analysis. Cox regression was performed to calculate the hazard ratio (HR) and the prognosis factors of survival. Propensity score matching (PSM) was used to balance the parameters between therapy groups.

RESULTS

The overall cohort's median follow-up time was 105 months. For 5-year OS and CSS, there were no significant differences between the ACT ( +) and ACT (-) groups (p = 0.105; p = 0.788). However, subgroup analyses according to the kind of NAT found that ACT improved overall survival (OS) and cancer-specific survival (CSS) in patients who received neoadjuvant radiotherapy (nRT) (p < 0.001, p = 0.015). Among patients who received neoadjuvant chemoradiotherapy (nCRT), no significant survival benefits were found between the ACT ( +) and ACT (-) groups (p = 0.526, p = 0.288).

CONCLUSION

Our population-based cohort study suggested that the efficacy of ACT was associated with the kind of NAT. The ACT provides survival benefits in stage ypI RC for patients who received nRT. However, among patients who received nCRT, ACT did not improve long-term survival.

CT-Based Radiomic Score: A Risk Stratifier in Far-Advanced Gastric Cancer Patients

OBJECTIVES

To prolong the survival, the value of a computed tomography-based radiomic score (RS) in stratifying survival and guiding personalized chemotherapy strategies in far-advanced gastric cancer (FGC) was investigated.

MATERIALS AND METHODS

This retrospective multicenter study enrolled 283 FGC patients (cT4a/bNxM0-1) from three centers. Patients from one center were randomly divided into the training (n = 166) and internal validation (n = 83) cohorts, whereas the external validation cohort (n = 34) consisted of patients from the two other centers. The RS was calculated for each patient to predict progression-free survival (PFS). Features from the primary tumor and main metastasis (peritoneum, liver, and lymph node) were integrated in the training cohort and then validated for its ability to stratify PFS and overall survival (OS) in the validation cohort. The association between the RS and efficacy of neoadjuvant intraperitoneal and systemic (NIPS) therapy was also explored.

RESULTS

The RS demonstrated a favorable prognostic ability to predict PFS in all cohorts (training: C-index 0.83, 95% confidence interval [CI]: 0.788-0.872; internal validation: C-index 0.75, 95% CI: 0.682-0.818; external validation: C-index 0.76, 95% CI: 0.669-0.851; all p < 0.05), as well as an excellent ability to stratify the PFS and OS in both the whole population and metastatic subgroups (p < 0.05). Patients with a low score were more likely to undergo surgery after perioperative chemotherapy (p < 0.05). Furthermore, only high-scoring patients with peritoneal metastasis benefited from NIPS.

CONCLUSION

The RS may be an effective risk stratifier for the outcomes of FGC patients and may be used to select patients who can benefit from NIPS therapy.

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer

OBJECTIVES

To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer.

METHODS

A total of 603 patients who underwent NAC were retrospectively included between January 2018 and June 2021 from three different institutions. Four different deep convolutional neural networks (DCNNs) were trained by pretreatment ultrasound images using annotated training dataset (n = 420) and validated in a testing cohort (n = 183). Comparing the predictive performance of these models, the best one was selected for image-only model structure. Furthermore, the integrated DLR model was constructed based on the image-only model combined with independent clinical-pathologic variables. Areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method.

RESULTS

As the optimal basic model, Resnet50 achieved an AUC and accuracy of 0.879 and 82.5% in the validation set. The integrated DLR model, yielding the highest classification performance in predicting response to NAC (AUC 0.962 and 0.939 in the training and validation cohort), outperformed the image-only model and the clinical model and also performed better than two radiologists' prediction (all p < 0.05). In addition, predictive efficacy of the radiologists was improved under the assistance of the DLR model significantly.

CONCLUSION

The pretreatment US-based DLR model could hold promise as a clinical guidance for predicting NAC response of patients with breast cancer, thereby providing benefit of timely treatment strategy adjustment to potential poor NAC responders.

KEY POINTS

• Multicenter retrospective study showed that deep learning radiomics (DLR) model based on pretreatment ultrasound image and clinical parameter achieved satisfactory prediction of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer. • The integrated DLR model could become an effective tool to guide clinicians in identifying potential poor pathological responders before chemotherapy. • The predictive efficacy of the radiologists was improved under the assistance of the DLR model.

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer

BACKGROUND

To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa).

MATERIALS

The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient's prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated.

RESULTS

Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly.

CONCLUSION

The multiple easy-to-use deep learning-based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

Web-based nomogram to predict postresection risk of distant metastasis in patients with leiomyosarcoma: retrospective analysis of the SEER database and a Chinese cohort

OBJECTIVES

This study investigated risk factors and constructed an online tool to predict distant metastasis (DM) risk in patients with leiomyosarcoma (LMS) after surgical resection.

METHODS

Data regarding patients with LMS who underwent surgical resection between 2010 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data were collected regarding patients with LMS who underwent surgical resection at Tianjin Medical University Cancer Hospital and Institute (TJMUCH) between October 2010 and July 2018. Patients were randomly divided into training and validation sets. Logistic regression analyses were performed; a nomogram was established. The area under the curve (AUC) and calibration curve were used to evaluate the nomogram, which served as the basis for a web-based nomogram.

RESULTS

This study included 4461 and 76 patients from the SEER database and TJMUCH, respectively. Age, ethnicity, grade, T stage, N stage, radiotherapy, and chemotherapy were associated with DM incidence. C-index values were 0.815 and 0.782 in the SEER and Chinese datasets, respectively; corresponding AUC values were 0.814 and 0.773, respectively. A web-based nomogram (https://weijunqiang-leimyosarcoma-seer.shinyapps.io/dynnomapp/) was established.

CONCLUSIONS

Our web-based nomogram is an accurate and user-friendly tool to predict DM risk in patients with LMS; it can aid clinical decision-making.

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

Multimodal AI for prediction of distant metastasis in carcinoma patients

Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

Repeatability of radiomics studies in colorectal cancer: a systematic review

BACKGROUND

Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer.

METHODS

The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies.

RESULTS

A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27.

CONCLUSIONS

The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

BACKGROUND

Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.

METHODS

We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.

RESULTS

Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.

CONCLUSION

Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Objective

To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).

Materials and methods

Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.

Results

Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p &lt; 0.001) and external validation (p &lt; 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.

Conclusion

Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

A boundary-guided transformer for measuring distance from rectal tumor to anal verge on magnetic resonance images

Accurate measurement of the distance from the tumor's lowest boundary to the anal verge (DTAV) provides an important reference value for treatment of rectal cancer, but the standard measurement method (colonoscopy) causes substantial pain. Therefore, we propose a method for automatically measuring the DTAV on sagittal magnetic resonance (MR) images. We designed a boundary-guided transformer that can accurately segment the rectum and tumor. From the segmentation results, we estimated the DTAV by automatically extracting the anterior rectal wall from the tumor's lowest point to the anal verge and then calculating its physical length. Experiments were conducted on a rectal tumor MR imaging (MRI) dataset to evaluate the efficacy of our method. The results showed that our method outperformed surgeons with 6 years of experience (p < 0.001). Furthermore, by referring to our segmentation results, attending and resident surgeons could improve their measurement precision and efficiency.

Pancancer analysis of SKA1 mutation and its association with the diagnosis and prognosis of human cancers

This study aims to explore the role of SKA1 in cancer diagnosis and prognosis and to investigate the mechanism by which SKA1 affects the malignant behaviors of ovarian cancer. Herein, we analyzed the oncogenic role of SKA1 at pan-cancer level by multiple informatics databases and verified the analysis by in vitro experiments. As a result, SKA1 was upregulated across cancers and was related to poor clinical outcome and immune infiltration. Specifically, the constructed nomogram showed superior performance in predicting the prognosis of epithelial ovarian cancer patients. Furthermore, the in vitro experiments revealed that silencing SKA1 significantly inhibited the proliferation, migratory ability and enhanced the cisplatin sensitivity of ovarian cancer cells. Therefore, we explored the oncogenic and potential therapeutic role of SKA1 across cancers through multiple bioinformatic analysis and revealed that SKA1 may promote ovarian cancer progression and chemoresistance to cisplatin by activating the AKT-FOXO3a signaling pathway.

Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.

Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer

Background and purpose

To investigate the value of radiomics features extracted from pre-treatment ¹⁸F-FDG PET/CT in predicting the outcomes of stage III-IV colorectal cancer (CRC), which may assist in clinical management strategies and precise treatment of stage III-IV CRC.

Materials and methods

124 patients with pathologically confirmed stage III-IV CRC who underwent pre-treatment ¹⁸F-FDG PET/CT scans were enrolled in this study. The least absolute shrinkage and selection operator Cox regression (LASSO-Cox) was used to select radiomics features, and the radiomics scores (Rad-scores) were calculated to build radiomics models. The performance of radiomics models was represented by the concordance index (C-index) and compared with clinical models and complex model. The bootstrap resampling method was used to create validation sets. Additionally, nomograms were developed based on complex models.

Results

The C-indices of the radiomics model for predicting PFS and OS were 0.712 (95%CI: 0.680-0.744) and 0.758 (0.728-0.789), respectively. In the clinical model, these values were 0.690 (0.664-0.0.717) and 0.738 (0.709-0.767), respectively. However, in the complex model were 0.734 (0.705-0.762) and 0.780 (0.754-0.807), respectively. The Kaplan-Meier curves demonstrated that the radiomics model could effectively separate patients with stage III-IV stage CRC into high- and low-risk groups (p < 0.001). Multivariate Cox regression analysis confirmed the independent prognostic value of Rad-scores.

Conclusion

Pre-treatment ¹⁸F-FDG PET/CT radiomics features can stratify the risk of patients with stage III-IV CRC and accurately predict their outcomes. These findings could be clinically valuable for precision treatment and management decisions in stage III-IV CRC.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy

Background

The aim of the study was to develop and validate a nomogram for predicting cancer-specific survival (CSS) in lymph- node- positive rectal cancer patients after radical proctectomy.

Methods

In this study, we analyzed data collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. In addition, in a 7:3 randomized design, all patients were split into two groups (development and validation cohorts). CSS predictors were selected via univariate and multivariate Cox regressions. The nomogram was constructed by analyzing univariate and multivariate predictors. The effectiveness of this nomogram was evaluated by concordance index (C-index), calibration plots, and receiver operating characteristic (ROC) curve. Based on the total score of each patient in the development cohort in the nomogram, a risk stratification system was developed. In order to analyze the survival outcomes among different risk groups, Kaplan-Meier method was used.

Results

We selected 4,310 lymph- node- positive rectal cancer patients after radical proctectomy, including a development cohort (70%, 3,017) and a validation cohort (30%, 1,293). The nomogram correlation C-index for the development cohort and the validation cohort was 0.702 (95% CI, 0.687-0.717) and 0.690 (95% CI, 0.665-0.715), respectively. The calibration curves for 3- and 5-year CSS showed great concordance. The 3- and 5-year areas under the curve (AUC) of ROC curves in the development cohort were 0.758 and 0.740, respectively, and 0.735 and 0.730 in the validation cohort, respectively. Following the establishment of the nomogram, we also established a risk stratification system. According to their nomogram total points, patients were divided into three risk groups. There were significant differences between the low-, intermediate-, and high-risk groups (p< 0.05).

Conclusions

As a result of our research, we developed a highly discriminatory and accurate nomogram and associated risk classification system to predict CSS in lymph-node- positive rectal cancer patients after radical proctectomy. This model can help predict the prognosis of patients with lymph- node- positive rectal cancer.

The preoperative geriatric nutritional risk index predicts long-term prognosis in elderly locally advanced rectal cancer patients: a two-center retrospective cohort study

BACKGROUND

The objective is to explore the value of preoperative geriatric nutritional risk index (GNRI) in evaluating long-term prognosis in elderly locally advanced rectal cancer (LARC) patients who accepted neoadjuvant chemoradiotherapy (NCRT) and to compare GNRI with established nutritional markers, including prognostic nutritional index (PNI) and controlling nutritional status (CONUT) score.

METHODS

Preoperative GNRI was retrospectively assessed in 172 LARC patients aged ≥ 60 years who underwent radical resection after NCRT at two centers. Optimal cutoff value of GNRI was determined by X-tile program. The association of GNRI with clinicopathological parameters and nutritional markers was analyzed. The survival ability of markers was evaluated using time-dependent receiver-operating characteristic (ROC) curve analysis. Finally, survival analysis was performed using Kaplan-Meier and Cox regression analysis.

RESULTS

GNRI was highly correlated with nutritional markers. An optimal cutoff value for the GNRI was 96. In the time-dependent ROC curve, GNRI demonstrated a stable predictive ability for both disease-free survival (DFS) and overall survival (OS). Multivariate analysis showed that GNRI was the only nutritional marker that independently predicted DFS (HR 2.457, 95% CI 1.066-5.665, P = 0.035) and OS (HR 9.002, 95% CI 3.100-26.146, P < 0.001). As an additional benefit, GNRI was able to stratify survival in subgroups of ypTNM and tumor response.

CONCLUSION

Preoperative GNRI is a promising predictor of long-term survival for elderly LARC patients undergoing NCRT, superior to the established nutritional markers.

MRI-Based Artificial Intelligence in Rectal Cancer

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study

BACKGROUND

This study aimed to develop a radiogenomic prognostic prediction model for colorectal cancer (CRC) by investigating the biological and clinical relevance of intratumoural heterogeneity.

METHODS

This retrospective multi-cohort study was conducted in three steps. First, we identified genomic subclones using unsupervised deconvolution analysis. Second, we established radiogenomic signatures to link radiomic features with prognostic subclone compositions in an independent radiogenomic dataset containing matched imaging and gene expression data. Finally, the prognostic value of the identified radiogenomic signatures was validated using two testing datasets containing imaging and survival information collected from separate medical centres.

RESULTS

This multi-institutional retrospective study included 1601 patients (714 females and 887 males; mean age, 65 years ± 14 [standard deviation]) with CRC from 5 datasets. Molecular heterogeneity was identified using unsupervised deconvolution analysis of gene expression data. The relative prevalence of the two subclones associated with cell cycle and extracellular matrix pathways identified patients with significantly different survival outcomes. A radiogenomic signature-based predictive model significantly stratified patients into high- and low-risk groups with disparate disease-free survival (HR = 1.74, P = 0.003). Radiogenomic signatures were revealed as an independent predictive factor for CRC by multivariable analysis (HR = 1.59, 95% CI:1.03-2.45, P = 0.034). Functional analysis demonstrated that the 11 radiogenomic signatures were predominantly associated with extracellular matrix and immune-related pathways.

CONCLUSIONS

The identified radiogenomic signatures might be a surrogate for genomic signatures and could complement the current prognostic strategies.

Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis

Purpose

The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models.

Methods

We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I² values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity.

Results

We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score.

Conclusions

Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.

Peimine-induced apoptosis and inhibition of migration by regulating reactive oxygen species-mediated MAPK/STAT3/NF-κB and Wnt/β-catenin signaling pathways in gastric cancer MKN-45 cells

Peimine (PM), a natural product extracted from Fritillaria, has anti-inflammatory, drug resistance reversal, and other pharmacological effects. The purpose of this study was to investigate the antitumor effects and the molecular mechanisms of PM using gastric cancer MKN-45 cells. Cell counting kit-8 assays were used to evaluate the viability of gastric cancer cells after treatment with PM. The results showed that PM significantly reduced the activity of gastric cancer cells, and the effect was most obvious in MKN-45 cells. Annexin V-FITC/propidium iodide staining and flow cytometry were used to assess apoptosis of MKN-45 cells after PM treatment. Our results showed that PM-induced apoptosis of MKN-45 cells. Flow cytometry was also used to determine the mitochondrial membrane potential and reactive oxygen species (ROS) levels, and to assess PM-induced cell-cycle arrest. Additionally, Western blot was used to analyze the expression of signaling pathway proteins and the relationship between apoptosis and ROS accumulation. Our findings showed that PM destroyed the mitochondria by diminishing the mitochondrial membrane potential. In addition, PM regulated the mitogen-activated protein kinase (MAPK), signal transducer and activator of transcription 3, and nuclear factor kappa-B signaling pathways by promoting the accumulation of ROS in MKN-45 cells. PM also caused cell-cycle arrest in the G2/M phase by increasing ROS accumulation. Furthermore, PM inhibited cell migration by regulating the Wnt/β-catenin pathway. In conclusion, PM plays an anticancer role through endogenous apoptosis pathways and by inhibiting cell migration, and it has the potential to be a useful treatment for gastric cancers.

Global status of research on radiotherapy for rectal cancer: A bibliometric and visual analysis

Radiotherapy for rectal cancer has received increasing research attention in recent years; however, no bibliometric assessment has been conducted on the progress of research in this field. This study aimed to visualize the research evolution and emerging research hotspots in the field of rectal cancer radiotherapy using bibliometric methods. Data were collected from the Web of Science Core Collection database, including countries, institutions, authors, keywords, and co-citations of references, and the CiteSpace software was used for bibliometric analysis. A total of 5,372 publications on radiotherapy for rectal cancer, published between January 2000 and January 2022, were included. An increasing trend in the number of published articles was observed. There is an overall upward trend in the number of publications published, with the US publishing the most in this field, followed by China and the Netherlands. Italian writer Vincenzo Valentini and German writer R. Sauer ranked first in terms of published articles and co-cited authors, respectively. Literature co-citation and keyword co-occurrence analyses showed that early studies focused on topics such as preoperative radiotherapy, combined radiotherapy and chemotherapy, and total mesorectal excision. In recent years, gradually increasing attention has been paid to short-course radiotherapy, x-ray brachytherapy, and stereotactic systemic radiotherapy. Burst analysis suggested that magnetic resonance (MR)-guided neoadjuvant radiotherapy studies, mechanistic studies, and clinical trials may emerge as new research hotspots. Rectal cancer radiotherapy has been widely studied and the research hotspots have considerably changed in recent years. Future research hotspots may include MR-guided neoadjuvant radiotherapy studies, mechanistic studies, and clinical trials.