Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway

Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing prognosis and immunotherapy response. Current TIME evaluation relies on invasive biopsies, limiting its clinical application. This study hypothesized that computed tomography (CT)-based deep learning (DL) radiomics models can non-invasively predict key TIME biomarkers: Tumor-stroma ratio (TSR), tumor-infiltrating lymphocytes (TILs), and immune score (IS).

AIM

To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.

METHODS

In this retrospective study, preoperative CT images of 315 pathologically confirmed CRC patients (220 in training cohort and 95 in validation cohort) were analyzed. Manually delineated regions of interest were used to extract DL features. Predictive models (DenseNet-121/169) for TSR, TILs, IS, and TIME classification were constructed. Performance was evaluated via receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The DL-DenseNet-169 model achieved area under the curve (AUC) values of 0.892 [95% confidence interval (CI): 0.828-0.957] for TSR and 0.772 (95%CI: 0.674-0.870) for TIME score. The DenseNet-121 model yielded AUC values of 0.851 (95%CI: 0.768-0.933) for TILs and 0.852 (95%CI: 0.775-0.928) for IS. Calibration curves demonstrated strong prediction-observation agreement, and DCA confirmed clinical utility across threshold probabilities (P < 0.05 for all models).

CONCLUSION

CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation, enabling personalized immunotherapy strategies in CRC management.

A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal Cancer

RATIONALE AND OBJECTIVES

To develop and validate a computed tomography-based radiomics nomogram for cancer-specific survival (CSS) prediction in curatively resected colorectal cancer (CRC), and its performance was compared with the American Joint Committee on Cancer (AJCC) staging and clinical-pathological models.

MATERIALS AND METHODS

A total of 794 patients with curatively resected CRC from a prospective cancer registry program were included and randomly divided into the training (n = 556) and validation (n = 238) cohorts. A radiomics signature (RS) predicting CSS was constructed with a hybrid automatic machine learning strategy, and the prognostic value was assessed with Kaplan-Meier (KM) survival analysis. The performance of the established models was assessed by the discrimination, calibration, and clinical utility.

RESULTS

A 10-feature-based RS with independent prognostic value was developed. KM survival curves showed that high-risk patients defined by RS had a worse CSS than the low-risk patients (log-rank P<0.001). The radiomics nomogram integrating the RS and clinical-pathological factors had the optimal performance in predicting CSS in terms of Harrell's concordance index (0.803 [95% confidence interval: 0.761-0.845] for the primary cohort, 0.772 [95% confidence interval: 0.702-0.841] for the validation cohort), time-dependent receiver operating curves (time-ROC) (the area under the time-ROC curves [AUC] at three years were 84.06±2.86 and at five years were 86.35±2.19 in the primary cohort, the AUC at three years were 77.6±4.76, and at five years were 84±3.66 in the validation cohort), calibration curves and decision curve analysis, in comparison with the AJCC staging model, clinical-pathological model, and the RS alone.

CONCLUSION

The radiomics nomogram integrating the RS and clinical-pathological factors could be a valuable individualized predictor of the CSS for curatively resected CRC patients.

Artificial Intelligence for Drug Discovery: An Update and Future Prospects

Artificial intelligence (AI) has become a pivotal tool for medical image analysis, significantly enhancing drug discovery through improved diagnostics, staging, prognostication, and response assessment. At a high level, AI-driven image analysis enables the quantification and synthesis of previously qualitative imaging characteristics, facilitating the identification of novel disease-specific biomarkers, patient risk stratification, prognostication, and adverse event prediction. In addition, AI can assist in response assessment by capturing changes in imaging "phenotype" over time, allowing for optimized treatment plans based on real-time analysis. Integrating this emerging technology into drug discovery pipelines has the potential to accelerate the identification and development of new pharmaceuticals by assisting in target identification and patient selection, as well as reducing the incidence, and therefore cost, of failed trials through high-throughput, reproducible, and data-driven insights. Continued progress in AI applications will shape the future of medical imaging, ultimately fostering more efficient, accurate, and tailored drug discovery processes. Herein, we offer a comprehensive overview of how AI enhances medical imaging to inform drug development and therapeutic strategies.

Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization

BACKGROUND

This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE).

METHODS

In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve.

RESULTS

Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1-2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1-1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805-0.861), internal validation cohort (0.823; 95% CI 0.771-0.876) and external validation cohort (0.846; 95% CI 0.768-0.924). The calibration curve exhibited a satisfactory correspondence.

CONCLUSION

A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis.

Node Reporting and Data System Combined With Computed Tomography Radiomics Can Improve the Prediction of Nonenlarged Lymph Node Metastasis in Gastric Cancer

OBJECTIVES

To investigate the diagnostic performance of Node Reporting and Data System (Node-RADS) combined with computed tomography (CT) radiomics for assessing nonenlargement regional lymph nodes in gastric cancer (GC).

METHODS

Preoperative CT images were retrospectively collected from 376 pathologically confirmed of gastric adenocarcinoma from January 2019 to December 2023, with 605 lymph nodes included for analysis. They were divided into training (n = 362) and validation (n = 243) sets. Radiomics features were extracted from venous-phase, and the radiomics score was obtained. Clinical information, CT parameters, and Node-RADS classification were collected. A combined model was built using machine-learning approach and tested in validation set using receiver operating characteristic curve analysis. Further validation was conducted in different subgroups of lymph node short-axis diameter (SD) range.

RESULTS

Node-RADS score, SD, maximum diameter of thickness of tumor, and radiomics were identified as the most predictive factors. The results demonstrated that the integrated model combining SD, maximum diameter of thickness of tumor, Node-RADS, and radiomics outperformed the model excluding radiomics, yielding an area under the receiver operating characteristic curve of 0.82 compared with 0.79, with a statistically significant difference ( P  < 0.001). Subgroup analysis based on different SDs of lymph nodes also revealed enhanced diagnostic accuracy when incorporating the radiomics score for the 4- to 7.9-mm subgroups, all P  < 0.05. However, for the 8- to 9.9-mm subgroup, the combination of the radiomics did not significantly improve the prediction, with an area under the receiver operating characteristic curve of 0.85 versus 0.85, P  = 0.877.

CONCLUSION

The integration of radiomics scores with Node-RADS assessments significantly enhances the accuracy of lymph node metastasis evaluation for GC. This combined model is particularly effective for lymph nodes with smaller standard deviations, yielding a marked improvement in diagnostic precision.

CLINICAL RELEVANCE STATEMENT

The findings of this study indicate that a composite model, which incorporates Node-RADS, radiomics features, and conventional parameters, may serve as an effective method for the assessment of nonenlarged lymph nodes in GC.

Radiomics for Treatment Planning in Liver Cancers

This Surgical Innovation delves into the transformative potential of radiomics in liver cancer treatment, highlighting its advancements in pretreatment prognosis, noninvasive tumor profiling, and treatment response prediction, thus paving the way for more personalized therapeutic strategies.

Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer

Background/Objectives: Radiomic features exhibit a correlation with tumor size on pretreatment images. However, on post-treatment images, this association is influenced by treatment efficacy and varies between responders and non-responders. This study introduces a novel model, called baseline-referenced Delta radiomics, which integrates the association between radiomic features and tumor size into Delta radiomics to predict chemotherapy response in liver metastases from breast cancer (BC) and colorectal cancer (CRC). Materials and Methods: A retrospective study analyzed contrast-enhanced computed tomography (CT) scans of 83 BC patients and 84 CRC patients. Among these, 57 BC patients with 106 liver lesions and 37 CRC patients with 109 lesions underwent post-treatment imaging after systemic chemotherapy. Radiomic features were extracted from up to three lesions per patient following manual segmentation. Tumor response was assessed by measuring the longest diameter and classified according to RECIST 1.1 criteria as progressive disease (PD), partial response (PR), or stable disease (SD). Classification models were developed to predict chemotherapy response using pretreatment data only, Delta radiomics, and baseline-referenced Delta radiomics. Model performance was evaluated using confusion matrix metrics. Results: Baseline-referenced Delta radiomics performed comparably or better than established radiomics models in predicting tumor response in chemotherapy-treated patients with liver metastases. The sensitivity, specificity, and balanced accuracy in predicting response ranged from 0.66 to 0.97, 0.81 to 0.97, and 80% to 90%, respectively. Conclusions: By integrating the relationship between radiomic features and tumor size into Delta radiomics, baseline-referenced Delta radiomics offers a promising approach for predicting chemotherapy response in liver metastases from breast and colorectal cancer.

Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review

BACKGROUND/OBJECTIVES

This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers.

METHODS

The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers.

RESULTS

forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool.

CONCLUSIONS

AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma

Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This multicenter retrospective study included 1039 patients with LA-NPC (779 male and 260 female patients; mean age, 44 years ± 11 [SD]) diagnosed between December 2011 and January 2016. A radiomics-clinical prognostic model (model RC) was developed using pre- and post-IC MRI acquisitions and other clinical factors using graph convolutional neural networks. The concordance index (C-index) was used to evaluate model performance in predicting disease-free survival (DFS). The survival benefits of concurrent chemoradiation therapy (CCRT) were analyzed in model-defined risk groups. Results The C-indexes of model RC for predicting DFS were significantly higher than those of TNM staging in the internal (0.79 vs 0.53) and external (0.79 vs 0.62, both P < .001) testing cohorts. The 5-year DFS for the model RC-defined low-risk group was significantly better than that of the high-risk group (90.6% vs 58.9%, P < .001). In high-risk patients, those who underwent CCRT had a higher 5-year DFS rate than those who did not (58.7% vs 28.6%, P = .03). There was no evidence of a difference in 5-year DFS rate in low-risk patients who did or did not undergo CCRT (91.9% vs 81.3%, P = .19). Conclusion Serial MRI before and after IC can effectively help predict survival in LA-NPC. The radiomics-clinical prognostic model developed using a graph convolutional network-based deep learning method showed good risk discrimination capabilities and may facilitate risk-adapted therapy. Keywords: Nasopharyngeal Carcinoma, Deep Learning, Induction Chemotherapy, Serial MRI, MR Imaging, Radiomics, Prognosis, Radiation Therapy/Oncology, Head/Neck Supplemental material is available for this article. © RSNA, 2025.

Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases

Objectives

This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients.

Methods

We included 150 CRLM patients who underwent first-line doublet chemotherapy, dividing them into a training cohort (n=112) and a test cohort (n=38). We manually delineated three-dimensional tumor volumes, selecting the largest liver metastasis for measurement, using pretreatment portal-phase CT images and extracted 107 radiomics features. Treatment response was classified as responder (complete or partial response) or non-responder (stable or progressive disease), based on the best overall response according to RECIST criteria, version 1.1. Employing Random Forest and Boruta algorithms, we identified significant features for responder-non-responder differentiation. Radiomics signatures were developed and validated in the training cohort using five-fold cross-validation, and performance was assessed using the area under the curve (AUC).

Results

Among the patients, 91 (61%) were responders and 59 (39%) were non-responders. Variable selection with Boruta revealed three key parameters ("DependenceVariance," "ClusterShade," and "RunVariance"). In the training cohort, individual CT texture parameter AUCs ranged from 0.4 to 0.65, while the machine learning analysis incorporating all valid parameters exhibited a significantly higher AUC of 0.94 (p<0.01). The validation cohort also demonstrated strong predictive accuracy, with an AUC of 0.87 for treatment response.

Conclusions

This study highlights the potential of CT radiomics-driven machine learning in predicting chemotherapy responses among CRLM patients.

Node Reporting and Data System 1.0 (Node-RADS) for the Assessment of Oncological Patients' Lymph Nodes in Clinical Imaging

The assessment of lymph node (LN) involvement with clinical imaging is a key factor in cancer staging. Node Reporting and Data System 1.0 (Node-RADS) was introduced in 2021 as a new system specifically tailored for classifying and reporting LNs on computed tomography (CT) and magnetic resonance imaging scans. The aim of this review is to compile the scientific evidence that has emerged since the introduction of Node-RADS, with a specific focus on its diagnostic performance and reliability. Node-RADS's performance has been evaluated in various cancer types and anatomical sites, revealing a trend where higher Node-RADS scores correspond to a greater probability of metastatic LN with better diagnostic performances compared to using short axis diameter alone. Moreover, Node-RADS exhibits encouraging diagnostic value for both Node-RADS ≥ 3 and Node-RADS ≥ 4 cutoffs in predicting metastatic LN. In terms of Node-RADS scoring reliability, preliminary studies show promising but partially conflicting results, with agreement levels, mostly between two readers, ranging from fair to almost perfect. This review highlights a wide variation in methodologies across different studies. Thus, to fully realize the potential of Node-RADS in clinical practice, future studies should comprehensively evaluate its diagnostic accuracy, category-specific malignancy rates, and inter-observer agreement. Finally, although limited, promising evidence has suggested the following: a potential prognostic role for Node-RADS; the possible value of diffusion-weighted imaging for LNs classified as Node-RADS ≥ 3; a correlation between Node-RADS and certain texture features in CT; and improved diagnostic performance when Node-RADS is integrated into radiomics or clinical models.

CT-based radiogenomics of intrahepatic cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is an aggressive disease with increasing incidence and its genetic alterations could be the target of systemic therapies.

AIMS

To elucidate if radiomics extracted from computed tomography (CT) may non-invasively predict ICC genetic alterations.

METHODS

All consecutive patients with a diagnosis of a mass-forming ICC (01/2016-06/2022) were considered. Inclusion criteria were availability of a high-quality contrast-enhanced CT and molecular profiling by NGS or FISH for FGFR2 fusion/rearrangement. The CT scan at diagnosis was considered. Genetic analyses were performed on surgical specimens (resectable patients) or biopsies (unresectable ones). The radiomic features were extracted using the LifeX software. Multivariate predictive models of the commonest genetic alterations were built.

RESULTS

In the 90 enrolled patients (58 NGS/32 FISH, median age 65 years), the most common genetic alterations were FGFR2 (20/90), IDH1 (10/58), and KRAS (9/58). At internal validation, the combined clinical-radiomic models achieved the best performance for the prediction of FGFR2 (AUC = 0.892) and IDH1 status (AUC = 0.819), outperforming the pure clinical and radiomic models. The radiomic model for predicting KRAS mutations achieved an AUC = 0.767 (vs. 0.660 of the clinical model) without further improvements with the addition of clinical features.

CONCLUSIONS

CT-based radiomics provides a reliable non-invasive prediction of ICC genetic status with a major impact on therapeutic strategies.

Classification of Neoadjuvant Therapy Response in Patients With Colorectal Liver Metastases Using Contrast-Enhanced Ultrasound-With Histological Pathology as the Gold Standard

OBJECTIVE

To evaluate the response to neoadjuvant therapy in patients with colorectal liver metastases (CRLMs) using ultrasound(US) and contrast-enhanced ultrasound(CEUS), with correction to the tumor regression grade (TRG) of pathological results.

METHODS

This study included patients with resectable CRLMs admitted from February to December 2022. After at least 4 cycles neoadjuvant therapy, all the patients received US and CEUS examinations within two weeks before hepatectomy. CEUS clips were postprocessed with color parameter imaging (CPI) and microflow imaging (MFI) analysis. Logistic regression analyses were used to develop an evaluation Nomogram. Ultrasound-based model was constructed to discriminate between the response (TRG1/2/3) and nonresponse (TRG4/5) groups at the lesion level. The model's predictive ability was evaluated using the C index and calibration curve, with decision curve analysis assessing the Nomogram's added value.

RESULTS

The study analyzed 105 CRLM lesions (the lesion with the highest diameter analyzed for each patient), with 43.8% showing a response to therapy. Univariate analysis identified calcification on US (p = 0.039), CEUS enhancement degree (p < 0.001), CEUS enhancement pattern (p<0.001), CEUS washout type (p < 0.001), CEUS necrosis (p < 0.001), CPI feeding artery (p = 0.003) and MFI pattern (p < 0.001) were significantly associated with TRG. The multivariate analysis showed CEUS enhancement pattern (p = 0.026), CEUS washout type (p = 0.018) and CEUS necrosis (p = 0.005) were independently associated with the neoadjuvant therapy response. A Nomogram with the three independent predictors was developed, with an AUC of 0.898.

CONCLUSION

The ultrasound-based model provided accurate evaluation of pathological tumor response to preoperative chemotherapy in patients with CRLM, and may help to decide the individualized treatment strategy.

Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation learning method

BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive subtype of HCC and is associated with poor survival.

PURPOSE

To investigate the performance of a representation learning-based feature fusion strategy that employs a multiphase contrast-enhanced CT (mpCECT)-based latent feature fusion (MCLFF) model for MTM-HCC identification.

METHODS

A total of 206 patients (54 MTM HCC, 152 non-MTM HCC) who underwent preoperative mpCECT with surgically confirmed HCC between July 2017 and December 2022 were retrospectively included from two medical centers. Multiphasic radiomics features were extracted from manually delineated volume of interest (VOI) of all lesions on each mpCECT phase. Representation learning based MCLFF model was built to fuse multiphasic features for MTM HCC prediction, and compared with competing models using other fusion methods. Conventional imaging features and clinical factors were also evaluated and analyzed. Prediction performance was validated by ROC analysis and statistical comparisons on an internal validation and an external testing dataset.

RESULTS

Fusion of radiomics features from the arterial phase (AP) and portal venous phase (PAP) using MCLFF demonstrated superior performance in MTM HCC prediction, with a higher AUC of 0.857 compared with all competing models in the internal validation set. Integration of multiple radiological or clinical features further improved the overall performance, with the highest AUCs of 0.857 and 0.836 respectively achieved in the internal validation and external testing set.

CONCLUSIONS

Multiphasic radiomics features of AP and PVP fused by the MCLFF have demonstrated substantial potential in the accurate prediction of MTM HCC. Clinical factors and Radiological features in mpCECT contribute incremental values to the developed MCLFF strategy.

Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters

Quantitative imaging biomarkers (QIB) are increasingly used in clinical research to advance precision medicine approaches in oncology. Computed tomography (CT) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility. Here, we contribute to the cancer imaging community through The Cancer Imaging Archive (TCIA) by providing investigator-initiated, same-day repeat CT scan images of 32 non-small cell lung cancer (NSCLC) patients, along with radiologist-annotated lesion contours as a reference standard. Each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; GE CT equipment), which spans a wide range of CT imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. This holds considerable value for advancing the development of robust Radiomics, Artificial Intelligence (AI) and machine learning (ML) methods.

Radiomics models to predict bone marrow metastasis of neuroblastoma using CT

BACKGROUND

Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma.

METHODS

We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast-enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics-clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots.

RESULTS

The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95-0.99) on the training set and 0.90 (95% CI: 0.82-0.95) on the validation set. The radiomics-clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89-0.96) on the training set and 0.91 (95% CI: 0.85-0.97) on the validation set.

CONCLUSIONS

MLP-based radiomics and radiomics-clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.

The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review

Artificial intelligence (AI) and image processing are revolutionising the diagnosis and management of liver cancer. Recent advancements showcase AI's ability to analyse medical imaging data, like computed tomography scans and magnetic resonance imaging, accurately detecting and classifying liver cancer lesions for early intervention. Predictive models aid prognosis estimation and recurrence pattern identification, facilitating personalised treatment planning. Image processing techniques enhance data analysis by precise segmentation of liver structures, fusion of information from multiple modalities, and feature extraction for informed decision-making. Despite progress, challenges persist, including the need for standardised datasets and regulatory considerations.

Value of 18F-FDG PET/CT-based radiomics features for differentiating primary lung cancer and solitary lung metastasis in patients with colorectal adenocarcinoma

OBJECTIVE

To investigate the value and applicability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics in differentiating primary lung cancer (PLC) from solitary lung metastasis (SLM) in patients with colorectal cancer (CRC).

MATERIALS AND METHODS

This retrospective study included 103 patients with CRC and solitary pulmonary nodules (SPNs). The least absolute shrinkage and selection operator (LASSO) was used to screen for optimal radiomics features and establish a PET/CT radiomics model. PET/CT Visual and complex models (combining radiomics with PET/CT visual features) were developed. The area under the receiver operating characteristic (ROC) curve (AUC) was used to determine the predictive value and diagnostic efficiency of the models.

RESULTS

The AUC of the PET/CT radiomics model for differentiating PLC from SLM was 0.872 (95% CI: 0.806-0.939), which was not different from that of the visual (0.829 [95% CI: 0.749-0.908; p = .352]). However, the AUC of the complex model (0.936 [95% CI:0.892-0.981]) was significantly higher than that of the PET/CT radiomics (p = .005) and visual model (p = .001). The sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of PET/CT radiomics for differentiating PLC from SLM were 0.720, 0.887, 0.806, 0.857, and 0.770, respectively.

CONCLUSION

PET/CT radiomics can effectively distinguish PLC and SLM in patients with CRC and SPNs and guide the implementation of personalized treatment.

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis

OBJECTIVE

The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases.

METHODS

We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set.

RESULTS

For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone.

CONCLUSION

Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients-surgeons, medical oncologists, and radiation oncologists-on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy

BACKGROUND

Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy.

METHODS

Clinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis.

RESULTS

151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS).

CONCLUSION

The promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model.

Targeting the EGFR signalling pathway in metastatic colorectal cancer

Epidermal growth factor receptor (EGFR) and its activated downstream signalling pathways play a crucial role in colorectal cancer development and progression. After four decades of preclinical, translational, and clinical research, it has been shown that blocking the EGFR signalling pathway at different molecular levels represents a fundamental therapeutic strategy for patients with metastatic colorectal cancer. Nevertheless, the efficacy of molecularly targeted therapies is inescapably limited by the insurgence of mechanisms of acquired cancer cell resistance. Thus, in the era of precision medicine, a deeper understanding of the complex molecular landscape of metastatic colorectal cancer is required to deliver the best treatment choices to all patients. Major efforts are currently ongoing to improve patient selection, improve the efficacy of available treatments targeting the EGFR pathway, and develop novel combination strategies to overcome therapy resistance within the continuum of care of metastatic colorectal cancer.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction

PURPOSE

To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases.

METHODS

Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score).

RESULTS

Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value <  < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value <  < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods.

CONCLUSIONS

Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study

Purpose

To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making.

Methods

A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. Preoperative neuron-specific enolase (NSE) levels were collected as a clinical predictor.

Results

In the training set, the AUCs of the radiomics model (0.878 [95% CI: 0.836, 0.915]) and merged model (0.884 [95% CI: 0.844, 0.919]) significantly outperformed the semantic model (0.718 [95% CI: 0.663, 0.769], p both<.001). In the external test set, the AUCs of the radiomics model (0.787 [95% CI: 0.696, 0.871]), merged model (0.807 [95%CI: 0.720, 0.889]) and semantic model (0.729 [95% CI: 0.631, 0.811]) did not exhibit statistical differences. The radiomics model outperformed NSE in sensitivity in the training set (85.3% vs 20.0%; p <.001) and external test set (88.9% vs 40.7%; p = .002).

Conclusion

The CT radiomics model could non-invasively, effectively and sensitively predict LNEN and LADC presenting as a PSN to assist in treatment strategy selection.

Comparative analysis of preoperative chemoradiotherapy and upfront surgery in the treatment of upper-half rectal cancer: oncological benefits, surgical outcomes, and cost implications

The value of neoadjuvant chemoradiotherapy (CRT) is not absolutely clear for upper-half (> 7-15 cm) rectal cancer. This study aimed to compare the efficacy and safety of radical surgery with preoperative CRT vs. upfront surgery (US) in Chinese patients with stage II and III upper-half rectal cancer. A total of 809 patients with locally advanced upper-half rectal cancer between 2017 and 2021 were enrolled retrospectively (280 treated with CRT and 529 treated with US). Through 1:1 propensity score matching, the CRT (172 patients) and US (172 patients) groups were compared for short-term postoperative results and long-term oncological and functional outcomes. In the entire cohort, patients in the CRT group had a younger age, lower distance from the anal verge (DAV), and higher rates of cT4 stage, cN2 stage, mrCRM positivity, EMVI positivity, CEA elevation, and CA-199 elevation than those in the US group. The 5-year disease-free survival (DFS) was lower in the CRT group than in the US group (76% vs. 84%, p = 0.022), while the 5-year overall survival (OS) was comparable between the two groups (85% and 88%, p = 0.084). The distant metastasis rate was higher in the CRT group than in the US group (12.5% vs. 7.8%, p = 0.028), though the local recurrence rate was similar between the two groups (1.1% and 1.3%, p = 1.000). After performing PSM, the 5-year OS (86% vs. 88% p = 0.312), the 5-year DFS (79% vs. 80%, p = 0.435), the local recurrence rate (1.2% vs. 1.7%, p = 1.000), and the distant metastasis rate (11.0% vs. 9.3%, p = 0.593) were comparable between the two groups. Notable pathological downstaging was observed in the CRT group, with a pathological complete response (PCR) rate of 14.5%. In addition, patients in the CRT group had a lower proportion of pT3 (61.6% vs. 77.9%, p < 0.001), pN + (pN1, 15.1% vs. 30.2%, pN2, 9.3% vs. 20.3%, p < 0.001), stage III (24.4% vs. 50.6%, p < 0.001), perineural invasion (19.8% vs. 32.0%, p = 0.014), and lymphovascular invasion (9.3% vs. 25.6%, p < 0.001) than those in the US group. Postoperative complications and long-term functional results were similar, yet there was a trend toward a higher conversion to laparotomy rate (5 (2.9%) vs. 0 (0.0%), p = 0.061) and higher rates of robotic surgery (11.6% vs. 4.7%, p < 0.001), open surgery (7.0% vs. 0.6%, p < 0.001), diverting stoma (47.1% vs. 25.6%, p < 0.001), and surgery costs (1473.6 ± 106.5 vs. 1140.3 ± 54.3$, p = 0.006) in the CRT group. In addition, EMVI (OR = 2.516, p = 0.001) was the only independent risk factor associated with poor response to CRT, and in subgroup analysis of EMVI + , CRT group patients presented a lower 5-year DFS (72.9% vs. 80.5%, p = 0.025) compared to US group patients. CRT prior to surgery has no additional oncological benefits over US in the treatment of upper-half rectal cancer. In contrast, CRT is associated with increased rates of conversion to laparotomy, stoma creation and higher surgery costs. Surgeons tend to favor robotic surgery in the treatment of complex cases such as radiated upper-half rectal cancers. Notably, EMVI + patients with upper-half rectal cancer should be encouraged to undergo upfront surgery, as preoperative CRT may not provide benefits and may lead to delayed treatment effects.

Personalized anti-tumor drug efficacy prediction based on clinical data

Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model. Then, to classify patients into two classes, responsive or non-responsive to a drug, drug efficacy predictors are established by machine learning based on the Latent Dirichlet Allocation topic representation. To evaluate the proposed method, we collected and collated clinical records of lung and bowel cancer patients treated with platinum. Experimental results on the data sets show the efficacy and effectiveness of the proposed method, suggesting the potential value of clinical data in cancer precision medicine. We hope that it will promote the research of drug efficacy prediction based on clinical data.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging

PURPOSE

To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases.

METHODS

Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA).

RESULTS

Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods.

CONCLUSIONS

Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Radiomics and liver: Where we are and where we are headed?

Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.

Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors

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

Ethnic diversity in treatment response for colorectal cancer: proof of concept for radiomics-driven enrichment trials

BACKGROUND

Several barriers hamper recruitment of diverse patient populations in multicenter clinical trials which determine efficacy of new systemic cancer therapies.

PURPOSE

We assessed if quantitative analysis of computed tomography (CT) scans of metastatic colorectal cancer (mCRC) patients using imaging features that predict overall survival (OS) can unravel the association between ethnicity and efficacy.

METHODS

We retrospectively analyzed CT images from 1584 mCRC patients in two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466) collected from August 2006 to March 2013. Primary and secondary endpoints compared RECIST1.1 response at month-2 and delta tumor volume at month-2, respectively. An ancillary study compared imaging phenotype using a peer-reviewed radiomics-signature combining 3 imaging features to predict OS landmarked from month-2. Analysis was stratified by ethnicity.

RESULTS

In total, 1584 patients were included (mean age, 60.25 ± 10.57 years; 969 men). Ethnicity was as follows: African (n = 50, 3.2%), Asian (n = 66, 4.2%), Caucasian (n = 1413, 89.2%), Latino (n = 27, 1.7%), Other (n = 28, 1.8%). Overall baseline tumor volume demonstrated Africans and Caucasians had more advanced disease (p < 0.001). Ethnicity was associated with treatment response. Response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. Overall delta tumor volume at month-2 demonstrated that Latino patients more likely experienced response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023).

CONCLUSION

This study highlights how clinical trials that inadequately represent minority groups may impact associated translational work. In appropriately powered studies, radiomics features may allow us to unravel associations between ethnicity and treatment efficacy, better elucidate mechanisms of resistance, and promote diversity in trials through predictive enrichment.

CLINICAL RELEVANCE STATEMENT

Radiomics could promote clinical trial diversity through predictive enrichment, hence benefit to historically underrepresented racial/ethnic groups that may respond variably to treatment due to socioeconomic factors and built environment, collectively referred to as social determinants of health.

KEY POINTS

•Findings indicate ethnicity was associated with treatment response across all 3 endpoints. First, response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. •Second, the overall delta tumor volume at month-2 demonstrated that Latino patients were more likely to experience response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023).

Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer

PURPOSE

Colorectal cancer is usually accompanied by liver metastases. The prediction of patient evolution is essential for the choice of the appropriate therapy. The aim of this study is to develop and evaluate machine learning models to predict KRAS gene mutations and 2-year disease-specific mortality from medical images.

METHODS

Clinical and follow-up information was collected from patients with metastatic colorectal cancer who had undergone computed tomography prior to liver resection. The dominant liver lesion was segmented in each scan and radiomic features were extracted from the volumes of interest. The 65% of the cases were employed to perform feature selection and to train machine learning algorithms through cross-validation. The best performing models were assembled and evaluated in the remaining cases of the cohort.

RESULTS

For the mortality model development, 101 cases were used as training set (64 alive, 37 deceased) and 35 as test set (22 alive, 13 deceased); while for KRAS mutation models, 55 cases were used for training (31 wild-type, 24 mutated) and 30 for testing (17 wild-type, 13 mutated). The ensemble of top performing models resulted in an area under the receiver operating characteristic curve of 0.878 for mortality and 0.905 for KRAS prediction.

CONCLUSIONS

Predicting the prognosis of patients with metastatic colorectal cancer is useful for making timely decisions about the best treatment options. This study presents a noninvasive method based on quantitative analysis of baseline images to identify factors influencing patient outcomes, with the aim of incorporating these tools as support systems.

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.

CT-based radiomics nomogram for the preoperative prediction of microsatellite instability and clinical outcomes in colorectal cancer: a multicentre study

AIM

To develop and validate a computed tomography (CT)-based radiomics nomogram for preoperative prediction of microsatellite instability (MSI) status and clinical outcomes in colorectal cancer (CRC) patients.

MATERIALS AND METHODS

This retrospective study enrolled 497 CRC patients from three centres. Least absolute shrinkage and selection operator regression was utilised for feature selection and constructing the radiomics signature. Univariate and multivariate logistic regression analyses were employed to identify significant clinical variables. The radiomics nomogram was constructed by integrating the radiomics signature and the identified clinical variables. The performance of the nomogram was evaluated through receiver operating characteristic curves, calibration curves, and decision curve analysis. Kaplan-Meier analysis was performed to investigate the prognostic value of the nomogram.

RESULTS

The radiomics signature comprised 10 radiomics features associated with MSI status. The nomogram, integrating the radiomics signature and independent predictors (age, location, and thickness), demonstrated favourable calibration and discrimination, achieving areas under the receiver operating characteristic (ROC) curves (AUCs) of 0.89 (95% confidence interval [CI]: 0.83-0.95), 0.87 (95% CI: 0.79-0.95), 0.88 (95% CI: 0.81-0.96), and 0.86 (95% CI: 0.78-0.93) in the training cohort, internal validation cohort, and two external validation cohorts, respectively. The nomogram exhibited superior performance compared to the clinical model (p<0.05). Additionally, survival analysis demonstrated that the nomogram successfully stratified stage II CRC patients based on prognosis (hazard ratio [HR]: 0.357, p=0.022).

CONCLUSION

The radiomics nomogram demonstrated promising performance in predicting MSI status and stratifying the prognosis of patients with CRC.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

Introduction

Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients.

Methods

Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed.

Results

The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not.

Conclusion

Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.

Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible

Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.

Radiomics in colorectal cancer patients

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

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.

Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status

The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate patient management and outcome improvement. However, the disease progression is influenced by multiple factors, such as patient characteristics and treatment strategies, and hence satisfactory performance of OS prediction remains challenging. Accordingly, we proposed a deep learning approach based on comprehensive predictors, including clinical, imaging, and genetic information, to accomplish reliable and personalized OS prediction in patients with BMs after receiving GKRS. Overall 1793 radiomic features extracted from pre-GKRS magnetic resonance images (MRI), clinical information, and epidermal growth factor receptor (EGFR) mutation status were retrospectively collected from 237 BM patients who underwent GKRS. DeepSurv, a multi-layer perceptron model, with 4 different aggregation methods of radiomics was applied to predict personalized survival curves and survival status at 3, 6, 12, and 24 months. The model combining clinical features, EGFR status, and radiomics from the largest BM showed the best prediction performance with concordance index of 0.75 and achieved areas under the curve of 0.82, 0.80, 0.84, and 0.92 for predicting survival status at 3, 6, 12, and 24 months, respectively. The DeepSurv model showed a significant improvement (p < 0.001) in concordance index compared to the validated lung cancer BM prognostic molecular markers. Furthermore, the model provided a novel estimate of the risk-of-death period for patients. The personalized survival curves generated by the DeepSurv model effectively predicted the risk-of-death period which could facilitate personalized management of patients with lung cancer BMs.

Distinguishing multiple primary lung cancers from intrapulmonary metastasis using CT-based radiomics

PURPOSE

To develop CT-based radiomics models that can efficiently distinguish between multiple primary lung cancers (MPLCs) and intrapulmonary metastasis (IPMs).

METHOD

This retrospective study included 127 patients with 254 lung tumors pathologically proved as MPLCs or IPMs between May 2009 and January 2020. Radiomics features of lung tumors were extracted from baseline CT scans. Particularly, we incorporated tumor-focused, refined radiomics by calculating relative radiomics differences from paired tumors of individual patients. We applied the L1-norm regularization and analysis of variance to select informative radiomics features for constructing radiomics model (RM) and refined radiomics model (RRM). The performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). The two radiomics models were compared with the clinical-CT model (CCM, including clinical and CT semantic features). We incorporated both radiomics features to construct fusion model1 (FM1). We also, build fusion model2 (FM2) by combing both radiomics, clinical and CT semantic features. The performance of the FM1 and FM2 were further compared with that of the RRM.

RESULTS

On the validation set, the RM achieved an AUC of 0.857. The RRM demonstrated improved performance (validation set AUC, 0.870) than the RM, and showed significant differences compared with the CCM (validation set AUC, 0.782). Fusion models further led prediction performance (validation set AUC, FM1:0.885; FM2:0.889). There were no significant differences among the performance of the FM1, the FM2 and the RRM.

CONCLUSIONS

The CT-based radiomics models presented good performance on the discrimination between MPLCs and IPMs, demonstrating the potential for early diagnosis and treatment guidance for MPLCs and IPMs.

Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics

Purpose

By using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to use extracted radiomics features to automatically classify of kidney tumors and normal kidney tissues and to establish an automatic classification model.

Methods

CT data were retrieved from the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19) in The Cancer Imaging Archive (TCIA) open access database. Arterial phase-enhanced CT images from 210 cases were used to establish an automatic classification model. These CT images of patients were randomly divided into training (168 cases) and test (42 cases) sets. Furthermore, the radiomics features of gross tumor volume (GTV) and normal kidney tissues in the training set were extracted and screened, and a binary logistic regression model was established. For the test set, the radiomic features and cutoff value of P were consistent with the training set.

Results

Three radiomics features were selected to establish the binary logistic regression model. The accuracy (ACC), sensitivity (SENS), specificity (SPEC), area under the curve (AUC), and Youden index of the training and test sets based on the CT radiomics classification model were all higher than 0.85.

Conclusion

The automatic classification model of kidney tumors and normal kidney tissues based on CT radiomics exhibited good classification ability. Kidney tumors could be distinguished from normal kidney tissues. This study may complement automated tumor delineation techniques and warrants further research.

CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.

High serum LDH and liver metastases are the dominant predictors of primary cancer resistance to anti-PD(L)1 immunotherapy

AIM

Anti-PD-(L)1 immunotherapies improve survival in multiple cancers but remain ineffective for most patients. We applied machine-learning algorithms and multivariate analyses on baseline medical data to estimate their relative impact on overall survival (OS) upon anti-PD-(L)1 monotherapies.

METHOD

This prognostic/predictive study retrospectively analysed 33 baseline routine medical variables derived from computed tomography (CT) images, clinical and biological meta-data. 695 patients with a diagnosis of advanced cancer were treated in prospective clinical trials in a single tertiary cancer centre in 3 cohorts including systemic anti-PD-(L)1 (251, 235 patients) versus other systemic therapies (209 patients). A random forest model combined variables to identify the combination (signature) which best estimated OS in patients treated with immunotherapy. The performance for estimating OS [95%CI] was measured using Kaplan-Meier Analysis and Log-Rank test.

RESULTS

Elevated serum lactate dehydrogenase (LDHhi) and presence of liver metastases (LM+) were dominant and independent predictors of short OS in independent cohorts of melanoma and non-melanoma solid tumours. Overall, LDHhiLM+ patients treated with anti-PD-(L)1 monotherapy had a poorer outcome (median OS: 3.1[2.4-7.8] months]) compared to LDHlowLM-patients (median OS: 15.3[8.9-NA] months; P < 0.0001). The OS of LDHlowLM-patients treated with immunotherapy was 28.8[17.9-NA] months (vs 13.1[10.8-18.5], P = 0.02) in the overall population and 30.3[19.93-NA] months (vs 14.1[8.69-NA], P = 0.0013) in patients with melanoma.

CONCLUSION

LDHhiLM+ status identifies patients who shall not benefit from anti-PD-(L)1 monotherapy. It could be used in clinical trials to stratify patients and eventually address this specific medical need.

Imaging standardisation in metastatic colorectal cancer: A joint EORTC-ESOI-ESGAR expert consensus recommendation

BACKGROUND

Treatment monitoring in metastatic colorectal cancer (mCRC) relies on imaging to evaluate the tumour burden. Response Evaluation Criteria in Solid Tumors provide a framework on reporting and interpretation of imaging findings yet offer no guidance on a standardised imaging protocol tailored to patients with mCRC. Imaging protocol heterogeneity remains a challenge for the reproducibility of conventional imaging end-points and is an obstacle for research on novel imaging end-points.

PATIENTS AND METHODS

Acknowledging the recently highlighted potential of radiomics and artificial intelligence tools as decision support for patient care in mCRC, a multidisciplinary, international and expert panel of imaging specialists was formed to find consensus on mCRC imaging protocols using the Delphi method.

RESULTS

Under the guidance of the European Organisation for Research and Treatment of Cancer (EORTC) Imaging and Gastrointestinal Tract Cancer Groups, the European Society of Oncologic Imaging (ESOI) and the European Society of Gastrointestinal and Abdominal Radiology (ESGAR), the EORTC-ESOI-ESGAR core imaging protocol was identified.

CONCLUSION

This consensus protocol attempts to promote standardisation and to diminish variations in patient preparation, scan acquisition and scan reconstruction. We anticipate that this standardisation will increase reproducibility of radiomics and artificial intelligence studies and serve as a catalyst for future research on imaging end-points. For ongoing and future mCRC trials, we encourage principal investigators to support the dissemination of these imaging standards across recruiting centres.

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics' potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.

Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols

Introduction

This study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners.

Materials and methods

Preoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson’s correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The “Kidney Tumor Segmentation 2019” (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist’s.

Results

The training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model’s performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69.

Conclusion

Radiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.

The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model

Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.

The Role of Biomarkers in the Management of Colorectal Liver Metastases

Simple Summary

Colorectal cancer remains one of the most significant sources of cancer-related morbidity and mortality worldwide. The liver is the most common site of metastatic spread. Multiple modalities exist to manage and potentially cure patients with metastatic colorectal cancer. However, reliable biomarkers to assist with clinical decision-making are limited. Recent advances in genomic sequencing technology have greatly expanded our knowledge of colorectal cancer carcinogenesis and significantly reduced the cost and timing of the investigation. In this article, we discuss the current utility of biomarkers in the management of colorectal cancer liver metastases.

Abstract

Surgical management combined with improved systemic therapies have extended 5-year overall survival beyond 50% among patients with colorectal liver metastases (CRLM). Furthermore, a multitude of liver-directed therapies has improved local disease control for patients with unresectable CRLM. Unfortunately, a significant portion of patients treated with curative-intent hepatectomy develops disease recurrence. Traditional markers fail to risk-stratify and prognosticate patients with CRLM appropriately. Over the last few decades, advances in molecular sequencing technology have greatly expanded our knowledge of the pathophysiology and tumor microenvironment characteristics of CRLM. These investigations have revealed biomarkers with the potential to better inform management decisions in patients with CRLM. Actionable biomarkers such as RAS and BRAF mutations, microsatellite instability/mismatch repair status, and tumor mutational burden have been incorporated into national and societal guidelines. Other biomarkers, including circulating tumor DNA and radiomic features, are under active investigation to evaluate their clinical utility. Given the plethora of therapeutic modalities and lack of evidence on timing and sequence, reliable biomarkers are needed to assist clinicians with the development of patient-tailored management plans. In this review, we discuss the current evidence regarding biomarkers for patients with CRLM.

Radiomics in precision medicine for gastric cancer: opportunities and challenges

OBJECTIVES

Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC.

METHODS

We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality.

RESULTS

Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies.

CONCLUSIONS

Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application.

KEY POINTS

• Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.