Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases

Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer

BACKGROUND

Neoadjuvant immunochemotherapy (nICT) has emerged as a popular treatment approach for advanced gastric cancer (AGC) in clinical practice worldwide. However, the response of AGC patients to nICT displays significant heterogeneity, and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes.

AIM

To establish a radiomic model to predict the response of AGC patients to nICT.

METHODS

Patients with AGC who received nICT (n = 60) were randomly assigned to a training cohort (n = 42) or a test cohort (n = 18). Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT. An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature. The performance of all the models was assessed through receiver operating characteristic curve analysis, decision curve analysis (DCA) and the Hosmer-Lemeshow goodness-of-fit test.

RESULTS

The radiomic nomogram could accurately predict the response of AGC patients to nICT. In the test cohort, the area under curve was 0.893, with a 95% confidence interval of 0.803-0.991. DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models.

CONCLUSION

A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC. This tool can assist clinicians in treatment-related decision-making.

MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer

BACKGROUND

The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making.

METHODS

A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared.

RESULTS

KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471-19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778-0.907) and the validation set (AUC: 0.805; 95%CI: 0.692-0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models.

CONCLUSIONS

Our study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis.

CLINICAL TRIAL NUMBER

Not applicable.

Establishment of a neutrophil extracellular trap-related prognostic signature for colorectal cancer liver metastasis and expression validation of CYP4F3

Liver metastasis stands as the primary contributor to mortality among patients diagnosed with colorectal cancer (CRC). Neutrophil extracellular traps (NETs) emerge as pivotal players in the progression and metastasis of cancer, showcasing promise as prognostic biomarkers. Our objective is to formulate a predictive model grounded in genes associated with neutrophil extracellular traps and identify novel therapeutic targets for combating CRLM. We sourced gene expression profiles from the Gene Expression Omnibus (GEO) database. Neutrophil extracellular trap-related gene set was obtained from relevant literature and cross-referenced with the GEO datasets. Differentially expressed genes (DEGs) were identified through screening via the least absolute shrinkage and selection operator regression and random forest modeling, leading to the establishment of a nomogram and subtype analysis. Subsequently, a thorough analysis of the characteristic gene CYP4F3 was undertaken, and our findings were corroborated through immunohistochemical staining. We identified seven DEGs (ATG7, CTSG, CYP4F3, F3, IL1B, PDE4B, and TNF) and established nomograms for the occurrence and prognosis of CRLM. CYP4F3 is highly expressed in CRC and colorectal liver metastasis (CRLM), exhibiting a negative correlation with CRLM prognosis. It may serve as a potential therapeutic target for CRLM. A novel prognostic signature related to NETs has been developed, with CYP4F3 identified as a risk factor and potential target for CRLM.

Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma

BACKGROUND

To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression.

METHODS

We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis.

RESULTS

For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated.

DATA CONCLUSION

To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

Ct-based intratumoral and peritumoral radiomics for predicting prognosis in osteosarcoma: A multicenter study

PURPOSE

To evaluate the performance of CT-based intratumoral, peritumoral and combined radiomics signatures in predicting prognosis in patients with osteosarcoma.

METHODS

The data of 202 patients (training cohort:102, testing cohort:100) with osteosarcoma admitted to the two hospitals from August 2008 to February 2022 were retrospectively analyzed. Progression free survival (PFS) and overall survival (OS) were used as the end points. The radiomics features were extracted from CT images, three radiomics signatures（RSintratumoral, RSperitumoral, RScombined）were constructed based on intratumoral, peritumoral and combined radiomics features, respectively, and the radiomics score (Rad-score) were calculated. Kaplan-Meier survival analysis was used to evaluate the relationship between the Rad-score with PFS and OS, the Harrell's concordance index (C-index) was used to evaluate the predictive performance of the radiomics signatures.

RESULTS

Finally, 8, 6, and 21 features were selected for the establishment of RSintratumoral, RSperitumoral, and RScombined, respectively. Kaplan-Meier survival analysis confirmed that the Rad-scores of the three RSs were significantly correlated with the PFS and OS of patients with osteosarcoma. Among the three radiomics signatures, RScombined had better predictive performance, the C-index of PSF prediction was 0.833 in the training cohort and 0.814 in the testing cohort, the C-index of OS prediction was 0.796 in the training cohort and 0.764 in the testing cohort.

CONCLUSIONS

CT-based intratumoral, peritumoral and combined radiomics signatures can predict the prognosis of patients with osteosarcoma, which may assist in individualized treatment and improving the prognosis of osteosarcoma patients.

Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study

RATIONALE AND OBJECTIVES

The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs).

MATERIALS AND METHODS

We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions. After tumor segmentation, three volume of interest (VOI) types were obtained: VOItumor and peritumor, VOIwhole, and VOIinterface. Clinical and combined models (incorporating radiomics and clinical features) were also established. To address imbalances in training dataset, Synthetic Minority Oversampling Technique was employed.

RESULTS

A total of 475 patients (DGs: n = 338, CAGs: n = 137) were analyzed. The VOIinterface model demonstrated the best performance for differentiating CAGs from DGs, achieving an area under the curve (AUC) of 0.806 and area under the precision-recall curve (PRAUC)of 0.894 in the cross-validation set. Using analysis of variance (ANOVA) feature selector and Support Vector Machine (SVM) classifier, seven features were selected. The model achieved an AUC and AUPRC of 0.912 and 0.972 in the internal validation dataset, and 0.897 and 0.930 in the external validation dataset. The combined model, incorporating interface radiomics and clinical features, showed improved performance in the external validation set, with an AUC of 0.94 and PRAUC of 0.959.

CONCLUSION

Radiomics models incorporating the peritumoral area demonstrate greater potential for distinguishing CAGs from DGs compared to intratumoral models. These findings may hold promise for evaluating tumor nature before surgery and improving clinical management of glioma patients.

Endoscopic Rectal Ultrasound-Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal Cancer

OBJECTIVES

To develop and validate an ultrasound-based radiomics model to predict synchronous liver metastases (SLM) in rectal cancer (RC) patients preoperatively.

METHODS

Two hundred and thirty-nine RC patients were included in this study and randomly divided into training and validation cohorts. A total of 5936 radiomics features were calculated on the basis of ultrasound images to build a radiomic model and obtain a radiomics score (Rad-score) using logistic regression. Meanwhile, clinical characteristics were collected to construct a clinical model. The radiomics-clinical model was developed and validated by integrating the radiomics features with the selected clinical characteristics. The performances of three models were evaluated and compared through their discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics model was developed based on 13 radiomic features. The radiomics-clinical model, which incorporated Rad-score, CEA, and CA199, exhibited favorable discrimination and calibration with areas under the receiver operating characteristic curve (AUC) of 0.920 (95% CI: 0.874-0.965) in the training cohorts and 0.855 (95% CI: 0.759-0.951) in the validation cohorts. And the AUC of the radiomics-clinical model was 0.849 (95% CI: 0.771-0.927) for the training cohorts and 0.780 (95% CI: 0.655-0.905) for the validation cohorts, the clinical model was 0.811 (95% CI: 0.718-0.905) for the training cohorts and 0.805 (95% CI: 0.645-0.965) for the validation cohorts. Moreover, decision curve analysis (DCA) further confirmed the clinical utility of the radiomics-clinical model.

CONCLUSIONS

The radiomics-clinical model performed satisfactory predictive performance, which can help improve clinical diagnosis performance and outcome prediction for SLM in RC patients.

Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study

OBJECTIVE

To evaluate the efficacy of the CT-based intratumoral, peritumoral, and combined radiomics signatures in predicting progression-free survival (PFS) of patients with chondrosarcoma (CS).

METHODS

In this study, patients diagnosed with CS between January 2009 and January 2022 were retrospectively screened, and 214 patients with CS from two centers were respectively enrolled into the training cohorts (institution 1, n = 113) and test cohorts (institution 2, n = 101). The intratumoral and peritumoral radiomics features were extracted from CT images. The intratumoral, peritumoral, and combined radiomics signatures were constructed respectively, and their radiomics scores (Rad-score) were calculated. The performance of intratumoral, peritumoral, and combined radiomics signatures in PFS prediction in patients with CS was evaluated by C-index, time-dependent area under the receiver operating characteristics curve (time-AUC), and time-dependent C-index (time C-index).

RESULTS

Eleven, 7, and 16 features were used to construct the intratumoral, peritumoral, and combined radiomics signatures, respectively. The combined radiomics signature showed the best prediction ability in the training cohort (C-index, 0.835; 95%; confidence interval [CI], 0.764-0.905) and the test cohort (C-index, 0.800; 95% CI, 0.681-0.920). Time-AUC and time C-index showed that the combined signature outperformed the intratumoral and peritumoral radiomics signatures in the prediction of PFS.

CONCLUSION

The CT-based combined signature incorporating intratumoral and peritumoral radiomics features can predict PFS in patients with CS, which might assist clinicians in selecting individualized surveillance and treatment plans for CS patients.

CRITICAL RELEVANCE STATEMENT

Develop and validate CT-based intratumoral, peritumoral, and combined radiomics signatures to evaluate the efficacy in predicting prognosis of patients with CS.

KEY POINTS

• Reliable prognostic models for preoperative chondrosarcoma are lacking. • Combined radiomics signature incorporating intratumoral and peritumoral features can predict progression-free survival in patients with chondrosarcoma. • Combined radiomics signature may facilitate individualized stratification and management of patients with chondrosarcoma.

The Role and Potential Mechanism of Complement Factor D in Fibromyalgia Development

Purpose

Fibromyalgia (FM) is a syndrome characterized by chronic musculoskeletal pain. Its clinical symptoms include both somatic and psychiatric symptoms, making the treatment of FM extremely challenging. The cause of FM is still unknown, and some patients do not improve their symptoms even after long-term active treatment. Thus, the development of new targeted therapies is important to reduce pain and improve quality of life for FM patients.

Methods

In this study, we screened genes and secreted factors that play key roles in FM through bioinformatics and big data analysis. Furthermore, we performed CCK-8, qRT-PCR, glucose, ATP and lactate content testing, dual luciferase reporter gene assay to investigate the potential mechanism of complement factor D in fibromyalgia development.

Results

In bioinformatics and big data analysis, we identified CFD was negatively correlated with the pro-inflammatory factor IL-6 and positively correlated with the anti-inflammatory factor IL-4, which suggested that CFD may be an anti-inflammatory factor. Through cellular assays, we verified that CFD reversed the decrease in IL-4 expression and the increase in IL-6 expression in BV2 cells caused by ATP.

Conclusion

In summary, based on bioinformatic methods and big data mining we obtained a new target CFD for FM, and further experiments verified that CFD has significant inhibition of ATP-induced neuropathic pain. These findings provide a new theoretical basis for the clinical translation of CFD.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

BACKGROUND

Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM.

METHODS

A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened.

RESULTS

We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT).

CONCLUSIONS

Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.

Analysis of the role of glucose metabolism-related genes in dilated cardiomyopathy based on bioinformatics

BACKGROUND

Dilated cardiomyopathy (DCM) is a prevalent condition with diverse etiologies, including viral infection, autoimmune response, and genetic factors. Despite the crucial role of energy metabolism in cardiac function, therapeutic targets for key genes in DCM's energy metabolism remain scarce.

METHODS

Our study employed the GSE79962 and GSE42955 datasets from the Gene Expression Omnibus (GEO) database for myocardial tissue sample collection and target gene identification via differential gene expression screening. Using various R packages, GSEA software, and the STRING database, we conducted data analysis, gene set enrichment, and protein-protein interaction predictions. The least absolute shrinkage and selection operator (LASSO) and Support Vector Machine (SVM) algorithms aided in feature gene selection, while the predictive model's efficiency was evaluated via the receiver operating characteristic (ROC) curve analysis. We used the non-negative matrix factorization (NMF) method for molecular typing and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm for predicting immune cell infiltration.

RESULTS

The DLAT and LDHA genes may regulate the immune microenvironment of DCM by influencing activated dendritic cells, activated mast cells, and M0 macrophages, respectively. The BPGM, DLAT, PGM2, ADH1A, ADH1C, LDHA, and PFKM genes may regulate m6A methylation in DCM by affecting the ZC3H13, ALKBH5, RBMX, HNRNPC, METTL3, and YTHDC1 genes. Further regulatory mechanism analysis suggested that PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C could be involved in the development of cardiomyopathy by regulating the Toll-like receptor signaling pathway.

CONCLUSIONS

PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C may serve as potential targets for guiding the diagnosis, treatment, and follow-up of DCM.

Safety and efficacy of cold endoscopic mucosal resection vs conventional endoscopic mucosal resection for treatment of 10-19 mm colorectal polyps

BACKGROUND

Colorectal polyps are important precancerous lesions in colorectal cancer, which require timely endoscopic treatment. For different extents of polyps, different therapies have different efficacy and safety. This study explored the efficacy and safety of two different methods for treating 10-19 mm colorectal polyps, with an aim to provide guidance for clinical treatment of colorectal polyps.

AIM

To compare the safety and efficacy of cold endoscopic mucosal resection (C-EMR) and conventional endoscopic mucosal resection (EMR) in the treatment of 10-19 mm colorectal polyps.

METHODS

A total of 192 patients with 371 colorectal polyps measuring 10-19 mm, who were hospitalized at the First Affiliated Hospital of Huzhou University from January 2021 to December 2022, were included in this study. The patients underwent either C-EMR or conventional EMR for the colorectal polyps. Age, gender, intestinal preparation score, polyp diameter, location, Paris endoscopic classification, histopathological type, complete resection rate, complete histological resection rate, incidence of complications, resection time, number of hemostatic clips used, and postoperative recurrence were compared between the two groups of patients.

RESULTS

There was no significant difference in age, sex, intestinal preparation score, Paris endoscopic classification, histopathological classification, size, location, complete resection rate, or complete histological resection rate between the two groups (P > 0.05). Immediate intraoperative bleeding occurred in 5.5% (10/181) of polyps and 7.4% (7/95) of patients in the C-EMR group, and the corres-ponding percentages in the EMR group were 2.6% (5/190) and 3.1% (3/97); the difference was not statistically significant between the two groups (P > 0.05). Postoperative delayed bleeding occurred in 0.6% (1/181) of polyps and 1.1% (1/95) of patients in the C-EMR group, and the corresponding percentages in the EMR group were 5.8% (11/190) and 7.2% (7/97); the difference was statistically significant between the two groups (P < 0.05). There was no occurrence of delayed perforation during or after surgery in either group. The average resection time was significantly shorter in the C-EMR group than that of the conventional EMR group [(3.15 ± 0.61) min vs (3.46 ± 0.42) min, P < 0.05]. The average number of hemostatic clips used in the C-EMR group was (1.31 ± 0.88), which was significantly less than that of the conventional EMR group (1.65 ± 0.61; P < 0.05). A total of 164 polyps were followed for 9 to 23 mo in the two groups of patients. The total recurrence rate after resection was 2.44%, and the recurrence rate of C-EMR (2.53%) was higher than that of the conventional EMR group (2.35%), but with no statistical significance (P > 0.05).

CONCLUSION

C-EMR and conventional EMR have similar therapeutic effects in the treatment of 10-19 mm colorectal polyps, but C-EMR has a shorter treatment time, lower incidence of delayed bleeding, and higher safety and economic benefit ratio.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Background and objective

For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.

Methods

In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS).

Results

For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis).

Conclusions

This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

Prognostic value of radiomics-based hyperdense middle cerebral artery sign for patients with acute ischemic stroke after thrombectomy strategy

Background and purpose

The purpose of this study was to evaluate the prognostic value of radiomics-based hyperdense middle cerebral artery sign (HMCAS) for patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT) and to establish prediction models to identify patients who may benefit more from MT.

Methods

In this retrospective study, a total of 102 consecutive patients who presented with HMCAS on non-contrast computed tomography (NCCT) at admission and underwent MT in our hospital between January 2019 and December 2020 were recruited. Among them, 46 experienced favorable outcomes (modified Rankin Scale [mRS] ≤ 2) at 3 months of follow-up. All patients were categorized into two sets, namely, the training set (n = 81) and the test set (n = 21). Radiomics features (RFs) and clinical features (CFs) in the training set were selected by statistical analysis to create models. The models' discriminative ability was quantified using the area under the curve (AUC) and confirmed by decision curve analyses.

Results

The prediction model established using CFs before MT includes baseline National Institutes of Health Stroke Scale (NIHSS) and neutrophil-to-lymphocyte ratio (NLR) [AUC [95% confidence interval (CI)] = 0.596 (0.312-0.881)]. A total of 1,389 RFs were extracted from each hyperdense territory and 8 RFs were left to build the radiomics model [RM; AUC (95%CI) = 0.798 (0.598-0.998)]. The model using preoperative CFs and RFs showed good performance [AUC (95%CI) = 0.817 (0.625-1.000)]. The models using post-operative CFs alone [AUC (95%CI) = 0.856 (0.685-1.000)] or post-operative CFs with RFs [AUC (95%CI) = 0.894 (0.757-1.000)] also showed good discrimination.

Conclusion

The radiomics-based HMCAS might be a promising tool to predict the prognoses of patients with AIS after MT.

Urinary PCA3 a Superior Diagnostic Biomarker for Prostate Cancer among Ghanaian Men

Introduction. Prostate cancer is one of the most commonly diagnosed cancers in men. Prostate-specific antigen (PSA) has been the biomarker of choice for screening and diagnosis of prostate cancer. However, inefficiencies exist with its diagnostic capabilities. This study thus evaluated the diagnostic and prognostic potential of urinary PCA3 as an alternative biomarker for prostate cancer in the Ghanaian population. Methods. A hospital-based cross-sectional study was conducted at the Urology Department of the 37 Military Hospital, Accra, Ghana. A total of 237 participants aged 40 years and above with any form of suspected prostate disorder were recruited into the study after written informed consent was obtained. Total serum PSA levels was measured using the electrochemiluminescence method and transrectal ultrasound-guided systematic core needle biopsies were obtained from each study participant. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic accuracies of serum PSA, DRE, and PCA3 as diagnostic tools for prostate cancer. These three diagnostic tools were also evaluated in various combinations to ascertain the combinations with the best diagnostic accuracy. Results. Prostate cancer was diagnosed in 26.6% of the participants. Benign prostate hyperplasia and prostatitis were diagnosed in 48.5% and 24.9% participants, respectively. DRE had a sensitivity of 93.7% and a specificity of 12.1%. PSA had a sensitivity of 92.1% and a specificity of 16.1%. PCA3 had a sensitivity of 57.1% and a specificity of 85.6% and showed a better accuracy ( $\text{AUC}=83.0$ ) compared to PSA ( $\text{AUC}=60.0$ ) and DRE ( $\text{AUC}=65.0$ ) as individual diagnostic tools. The combination of DRE+PCA3 score had the best diagnostic accuracy ( $\text{AUC}=0.80$ ) with a sensitivity and specificity of 60.3% and 80.5%, respectively. Conclusion. The urinary PCA3 assay showed a better diagnostic performance compared to serum PSA and DRE. PCA3 as a stand-alone and in combination with DRE could be a suitable complimentary marker in diagnosis and management of prostate cancer.

The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer

BACKGROUND

Microsatellite instability (MSI) status can be used for the classification and risk stratification of endometrial cancer (EC). This study aimed to investigate whether magnetic resonance imaging (MRI)-based tumor shape features can help assess MSI status in EC before surgery.

METHODS

The medical records of 88 EC patients with MSI status were retrospectively reviewed. Quantitative and subjective shape features based on MRI were used to assess MSI status. Variables were compared using the Student's t-test, χ² test, or Wilcoxon rank-sum test where appropriate. Univariate and multivariate analyses were performed by the logistic regression model. The area under the curve (AUC) was used to estimate the discrimination performance of variables.

RESULTS

There were 23 patients with MSI, and 65 patients with microsatellite stability (MSS) in this study. Eccentricity and shape type showed significant differences between MSI and MSS (P=0.039 and P=0.033, respectively). The AUC values of eccentricity, shape type, and the combination of 2 features for assessing MSI were 0.662 [95% confidence interval (CI): 0.554-0.770], 0.627 (95% CI: 0.512-0.743), and 0.727 (95% CI: 0.613-0.842), respectively. Considering the International Federation of Gynecology and Obstetrics (FIGO) staging, eccentricity maintained a significant difference in stages I-II (P=0.039), while there was no statistical difference in stages III-IV (P=0.601).

CONCLUSIONS

It is possible that MRI-based tumor shape features, including eccentricity and shape type, could be promising markers for assessing MSI status. The features may aid in the preliminary screening of EC patients with MSI.

Development and external validation of prognostic nomograms for liver disease-free and overall survival in locally advanced rectal cancer with neoadjuvant therapy: a post cohort study based on the FOWARC trial

Background

There is still a lack of nomograms that can accurately predict liver metastasis and poor prognosis after neoadjuvant therapy for locally advanced rectal cancer (LARC). Effective nomograms may help clinicians better identify LARC patients with potential high-risk risks, so as to carry out more targeted monitoring, treatment and follow-up.

Methods

The nomograms were based on the FOWARC trial (NCT01211210), which included 302 LARC patients who underwent neoadjuvant treatment before surgery at the Sixth Affiliated Hospital of Sun Yat-sen University from 2011 to 2014. The predictive accuracy and discriminative ability of the nomograms were determined by the concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling and a prospective study on 100 patients in 2017.

Results

The 3-year liver disease-free survival (LDFS) rate after neoadjuvant treatment for LARC was 91.65% (training cohort 92.22%, validation cohort 90.01%). Factors associated with LDFS were hepatitis B virus (HBV) infection, anemia, lymph node number, postoperative T stage and tumor nodule, which were all included in the nomogram for LDFS. The C-indies of the nomogram for LDFS were 0.828 and 0.845 in the training and validation cohorts. The 3-year overall survival (OS) rate was 94.14% (training cohort 94.13%, validation cohort 94.05%). Factors in the nomogram for OS were mesorectal fascia involvement (MRF), postoperative N stage, pathological differentiation, tumor nodule and neural invasion. The C-indies of the nomogram for predicting OS were 0.73 and 0.774 in the training and validation cohorts. The calibration curve for the survival probability showed good agreement between the nomogram predictions and the actual observations.

Conclusions

The nomograms established in this study can effectively predict LDFS and has good clinical application potential for OS in LARC patients treated with neoadjuvant therapy.

Deep learning radiomics for focal liver lesions diagnosis on long-range contrast-enhanced ultrasound and clinical factors

BACKGROUND

Routine clinical factors play an important role in the clinical diagnosis of focal liver lesions (FLLs); however, they are rarely used in computer-assisted diagnosis. Therefore, we developed a deep learning (DL) radiomics model, and investigated its effectiveness in diagnosing FLLs using long-range contrast-enhanced ultrasound (CEUS) cines and clinical factors.

METHODS

Herein, 303 patients with pathologically confirmed FLLs after surgery at three hospitals were retrospectively enrolled and divided into a training cohort (n=203), internal validation (IV) cohort (n=50) from one hospital with the ratio of 4:1, and external validation (EV) cohort (n=50) from the other two hospitals. Four DL radiomics models, namely Four Stream 3D convolutional neural network (FS3DU) (trained with CEUS cines only), FS3DU+A (trained with CEUS cines and alpha fetoprotein), FS3DU+H (trained with CEUS cines and hepatitis), and FS3DU+A+H (trained with CEUS cines, alpha fetoprotein, and hepatitis), were formed based on 3D convolutional neural networks (CNNs). They used approximately 20-s preoperative CEUS cines and/or clinical factors to extract spatiotemporal features for the classification of FLLs and the location of the region of interest. The area under curve of the receiver operating characteristic and diagnosis speed were calculated to evaluate the models in the IV and EV cohorts, and they were compared with those of two radiologists. Two-sided Delong tests were used to calculate the statistical differences between the models and radiologists.

RESULTS

FS3DU+A+H, which incorporated CEUS cines, hepatitis, and alpha fetoprotein, achieved the highest area under curve of 0.969 (95% CI: 0.901-1.000) and 0.957 (95% CI: 0.894-1.000) among radiologists and other models in IV and EV cohorts, respectively. A significant difference was observed when comparing FS3DU and radiologist 2 (all P<0.05). The diagnosis speed of all the models was the same (10.76 s per patient), and it was two times faster than those of the radiologists (radiologist 1: 23.74 and 27.75 s; radiologist 2: 25.95 and 29.50 s in IV and EV cohorts, respectively).

CONCLUSIONS

The proposed DL radiomics demonstrated excellent performance on the benign and malignant diagnosis of FLLs by combining CEUS cines and clinical factors. It could help the individualized characterization of FLLs, and enhance the accuracy of diagnosis in the future.

The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer

PURPOSE

Perineural invasion (PNI) has been recognized as an important prognosis factor in patients with colorectal cancer (CRC). The purpose of this retrospective study was to investigate the value of ¹⁸F-FDG PET/CT-based radiomics integrating clinical information, PET/CT features, and metabolic parameters for preoperatively predicting PNI and outcome in non-metastatic CRC and establish an easy-to-use nomogram.

METHODS

A total of 131 patients with non-metastatic CRC who undergo PET/CT scan were retrospectively enrolled. Univariate analysis was used to compare the differences between PNI-present and PNI-absent groups. Multivariate logistic regression was performed to select the independent predictors for PNI status. Akaike information criterion (AIC) was used to select the best prediction models for PNI status. CT radiomics signatures (RSs) and PET-RSs were selected by maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) regression and radiomics scores (Rad-scores) were calculated for each patient. The prediction models with or without Rad-score were established. According to the nomogram, nomogram scores (Nomo-scores) were calculated for each patient. The performance of different models was assessed with the area under the curve (AUC), specificity, and sensitivity. The clinical usefulness was assessed by decision curve (DCA). Multivariate Cox regression was used to selected independent predictors of progression-free survival (PFS).

RESULTS

Among all the clinical information, PET/CT features, and metabolic parameters, CEA, lymph node metastatic on PET/CT (N stage), and total lesion glycolysis (TLG) were independent predictors for PNI (p < 0.05). Six CT-RSs and 12 PET-RSs were selected as the most valuable factors to predict PNI. The Rad-score calculated with these RSs was significantly different between PNI-present and PNI-absent groups (p < 0.001). The AUC of the constructed model was 0.90 (95%CI: 0.83-0.97) in the training cohort and 0.80 (95%CI: 0.65-0.95) in the test cohort. The nomogram's predicting sensitivity was 0.84 and the specificity was 0.83 in the training cohort. The clinical model's predicting sensitivity and specificity were 0.66 and 0.85 in the training cohort, respectively. Besides, DCA showed that patients with non-metastatic CRC could get more benefit with our model. The results also indicated that N stage, PNI status, and the Nomo-score were independent predictors of PFS in patients with non-metastatic CRC.

CONCLUSION

The nomogram, integrating clinical data, PET/CT features, metabolic parameters, and radiomics, performs well in predicting PNI status and is associated with the outcome in patients with non-metastatic CRC.

Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.

Development and validation of an MRI-based nomogram for the preoperative prediction of tumor mutational burden in lower-grade gliomas

BACKGROUND

High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors. In this study, we aimed to determine the value of magnetic resonance (MR)-based preoperative nomogram in predicting TMB status in lower-grade glioma (LGG) patients.

METHODS

Overall survival (OS) data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by using the Kaplan-Meier method and time-dependent receiver operating characteristic (tdROC) analysis. The magnetic resonance imaging (MRI) data of 168 subjects obtained from The Cancer Imaging Archive (TCIA) were retrospectively analyzed. The correlation was explored by univariate and multivariate regression analyses. Finally, we performed tenfold cross validation. TMB values were retrieved from the supplementary information of a previously published article.

RESULTS

The high TMB subtype was associated with the shortest median OS (high vs. low: 50.9 vs. 95.6 months, P<0.05). The tdROC for the high-TMB tumors was 74% (95% CI: 61-86%) for survival at 12 months, and 71% (95% CI: 60-82%) for survival at 24 months. Multivariate logistic regression analysis confirmed that three risk factors [extranodular growth: odds ratio (OR): 8.367, 95% CI: 3.153-22.199, P<0.01; length-width ratio ≥ median: OR: 1.947, 95% CI: 1.025-3.697, P<0.05; frontal lobe: OR: 0.455, 95% CI: 0.229-0.903, P<0.05] were significant independent predictors of high-TMB tumors. The nomogram showed good calibration and discrimination. This model had an area under the curve (AUC) of 0.736 (95% CI: 0.655-0.817). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful. The average accuracy of the tenfold cross validation was 71.6% for high-TMB tumors.

CONCLUSIONS

Our results indicated that a distinct OS disadvantage was associated with the high TMB group. In addition, extranodular growth, nonfrontal lobe tumors and length-width ratio ≥ median can be conveniently used to facilitate the prediction of high-TMB tumors.

Assessment of Primary Colorectal Cancer CT Radiomics to Predict Metachronous Liver Metastasis

Objectives

To establish and validate a machine learning-based CT radiomics model to predict metachronous liver metastasis (MLM) in patients with colorectal cancer.

Methods

In total, 323 patients were retrospectively recruited from two independent institutions to develop and evaluate the CT radiomics model. Then, 1288 radiomics features were extracted to decode the imaging phenotypes of colorectal cancer on CT images. The optimal radiomics features were selected using a recursive feature elimination selector configured by a support vector machine. To reduce the bias caused by an unbalanced dataset, the synthetic minority oversampling technique was applied to resample the minority samples in the datasets. Then, both radiomics and clinical features were used to train the multilayer perceptron classifier to develop two classification models. Finally, a score-level fusion model was developed to further improve the model performance.

Results

The area under the curve (AUC) was 0.78 ± 0.07 for the tumour feature model and 0.79 ± 0.08 for the clinical feature model. The fusion model achieved the best performance, with AUCs of 0.79 ± 0.08 and 0.72 ± 0.07 in the internal and external validation cohorts.

Conclusions

Radiomics models based on baseline colorectal contrast-enhanced CT have high potential for MLM prediction. The fusion model combining radiomics and clinical features can provide valuable biomarkers to identify patients with a high risk of colorectal liver metastases.

Nomogram for predicting occurrence of synchronous liver metastasis in colorectal cancer: a single-center retrospective study based on pathological factors

Purpose

The purpose of this study was to explore the risk factors for synchronous liver metastasis (LM) of colorectal cancer (CRC) and to construct a nomogram for predicting the occurrence of synchronous LM based on baseline and pathological information.

Methods

The baseline and pathological information of 3190 CRC patients were enrolled in the study from the Department of Colorectal Surgery, the Second Affiliated Hospital of Harbin Medical University between 2012 and 2020. All patients were divided into development and validation cohorts with the 1:1 ratio. The characters of LM and none-LM patients in newly diagnosed colorectal cancer were utilized to explore the risk factors for synchronous LM with the univariate and multivariate logistic regression analyses. A predictive nomogram was constructed by using an R tool. In addition, receiver operating characteristic (ROC) curves was calculated to describe the discriminability of the nomogram. A calibration curve was plotted to compare the predicted and observed results of the nomogram. Decision-making curve analysis (DCA) was used to evaluate the clinical effect of nomogram.

Results

The nomogram consisted of six features including tumor site, vascular invasion (VI), T stage, N stage, preoperative CEA, and CA-199 level. ROC curves for the LM nomogram indicated good discrimination in the development (AUC = 0.885, 95% CI 0.854–0.916) and validation cohort (AUC = 0.857, 95% CI 0.821–0.893). The calibration curve showed that the prediction results of the nomogram were in good agreement with the actual observation results. Moreover, the DCA curves determined the clinical application value of predictive nomogram.

Conclusions

The pathologic-based nomogram could help clinicians to predict the occurrence of synchronous LM in postoperative CRC patients and provide a reference to perform appropriate metastatic screening plans and rational therapeutic options for the special population.

A Computed Tomography Nomogram for Assessing the Malignancy Risk of Focal Liver Lesions in Patients With Cirrhosis: A Preliminary Study

Purpose

The detection and characterization of focal liver lesions (FLLs) in patients with cirrhosis is challenging. Accurate information about FLLs is key to their management, which can range from conservative methods to surgical excision. We sought to develop a nomogram that incorporates clinical risk factors, blood indicators, and enhanced computed tomography (CT) imaging findings to predict the nature of FLLs in cirrhotic livers.

Method

A total of 348 surgically confirmed FLLs were included. CT findings and clinical data were assessed. All factors with P < 0.05 in univariate analysis were included in multivariate analysis. ROC analysis was performed, and a nomogram was constructed based on the multivariate logistic regression analysis results.

Results

The FLLs were either benign (n = 79) or malignant (n = 269). Logistic regression evaluated independent factors that positively affected malignancy. AFP (OR = 10.547), arterial phase hyperenhancement (APHE) (OR = 740.876), washout (OR = 0.028), satellite lesions (OR = 15.164), ascites (OR = 156.241), and nodule-in-nodule architecture (OR =27.401) were independent predictors of malignancy. The combined predictors had excellent performance in differentiating benign and malignant lesions, with an AUC of 0.959, a sensitivity of 95.24%, and a specificity of 87.5% in the training cohort and AUC of 0.981, sensitivity of 94.74%, and specificity of 93.33% in the test cohort. The C-index was 96.80%, and calibration curves showed good agreement between the nomogram predictions and the actual data.

Conclusions

The nomogram showed excellent discrimination and calibration for malignancy risk prediction, and it may aid in making FLLs treatment decisions.

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases

Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients' survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients' exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies' role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.

Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.

Prognostic Nomogram for Liver Metastatic Colon Cancer Based on Histological Type, Tumor Differentiation, and Tumor Deposit: A TRIPOD Compliant Large-Scale Survival Study

Objective

A proportional hazard model was applied to develop a large-scale prognostic model and nomogram incorporating clinicopathological characteristics, histological type, tumor differentiation grade, and tumor deposit count to provide clinicians and patients diagnosed with colon cancer liver metastases (CLM) a more comprehensive and practical outcome measure.

Methods

Using the Transparent Reporting of multivariable prediction models for individual Prognosis or Diagnosis (TRIPOD) guidelines, this study identified 14,697 patients diagnosed with CLM from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) 21 registry database. Patients were divided into a modeling group (n=9800), an internal validation group (n=4897) using computerized randomization. An independent external validation cohort (n=60) was obtained. Univariable and multivariate Cox analyses were performed to identify prognostic predictors for overall survival (OS). Subsequently, the nomogram was constructed, and the verification was undertaken by receiver operating curves (AUC) and calibration curves.

Results

Histological type, tumor differentiation grade, and tumor deposit count were independent prognostic predictors for CLM. The nomogram consisted of age, sex, primary site, T category, N category, metastasis of bone, brain or lung, surgery, and chemotherapy. The model achieved excellent prediction power on both internal (mean AUC=0.811) and external validation (mean AUC=0.727), respectively, which were significantly higher than the American Joint Committee on Cancer (AJCC) TNM system.

Conclusion

This study proposes a prognostic nomogram for predicting 1- and 2-year survival based on histopathological and population-based data of CLM patients developed using TRIPOD guidelines. Compared with the TNM stage, our nomogram has better consistency and calibration for predicting the OS of CLM patients.

Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases

PURPOSE

Early identification of patients at risk of developing colorectal liver metastases can help personalizing treatment and improve oncological outcome. The aim of this study was to investigate in patients with colorectal cancer (CRC) whether a machine learning-based radiomics model can predict the occurrence of metachronous metastases.

METHODS

In this multicentre study, the primary staging portal venous phase CT of 91 CRC patients were retrospectively analysed. Two groups were assessed: patients without liver metastases at primary staging, or during follow-up of ≥ 24 months (n = 67) and patients without liver metastases at primary staging but developed metachronous liver metastases < 24 months after primary staging (n = 24). After liver parenchyma segmentation, 1767 radiomics features were extracted for each patient. Three predictive models were constructed based on (1) radiomics features, (2) clinical features and (3) a combination of clinical and radiomics features. Stability of features across hospitals was assessed by the Kruskal-Wallis test and inter-correlated features were removed if their correlation coefficient was higher than 0.9. Bayesian-optimized random forest with wrapper feature selection was used for prediction models.

RESULTS

The three predictive models that included radiomics features, clinical features and a combination of radiomics with clinical features resulted in an AUC in the validation cohort of 86% (95%CI 85-87%), 71% (95%CI 69-72%) and 86% (95% CI 85-87%), respectively.

CONCLUSION

A machine learning-based radiomics analysis of routine clinical CT imaging at primary staging can provide valuable biomarkers to identify patients at high risk for developing colorectal liver metastases.

Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on ¹⁸F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.