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

Radiomic signatures derived from baseline 18F FDG PET/CT imaging can predict tumor-infiltrating lymphocyte values in patients with primary breast cancer

To determine the value of radiomics data extraction from baseline 18F FDG PET/CT in the prediction of tumor-infiltrating lymphocytes (TILs) among patients with primary breast cancer (BC).We retrospectively evaluated 74 patients who underwent baseline 18F FDG PET/CT scans for BC evaluation between October 2020 and April 2022. Radiomics data extraction resulted in a total of 131 radiomic features from primary tumors. TILs status was defined based on histological analyses of surgical specimens and patients were categorized as having low TILs or moderate & high TILs. The relationships between TILs groups and tumor features, patient characteristics and molecular subtypes were examined. Features with a correlation coefficient of less than 0.6 were analyzed by logistic regression to create a predictive model. The diagnostic performance of the model was calculated via receiver operating characteristics (ROC) analysis.Menopausal status, histological grade, nuclear grade, and four radiomics features demonstrated significant differences between the two TILs groups. Multivariable logistic regression revealed that nuclear grade and three radiomics features (Morphological COMShift, GLCM Correlation, and GLSZM Small Zone Emphasis) were independently associated with TIL grouping. The diagnostic performance analysis of the model showed an AUC of 0.864 (95% CI: 0.776-0.953; p < 0.001). The sensitivity, specificity, PPV, NPV and accuracy values of the model were 69.6%, 82.4%, 64%, 85.7% and 78.4%, respectivelyThe pathological TIL scores of BC patients can be predicted by using radiomics feature extraction from baseline 18F FDG PET/CT scans.

Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis

BACKGROUND

The liver, as the main target organ for hematogenous metastasis of colorectal cancer, early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients. Herein, this study aims to investigate the application value of a combined machine learning (ML) based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis (MLM).

AIM

To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.

METHODS

We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023. All participants were randomly assigned to the training or validation queue in a 7:3 ratio. We first apply generalized linear regression model (GLRM) and random forest model (RFM) algorithm to construct an MLM prediction model in the training queue, and evaluate the discriminative power of the MLM prediction model using area under curve (AUC) and decision curve analysis (DCA). Then, the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups.

RESULTS

Among the 301 patients included in the study, 16.28% were ultimately diagnosed with MLM through pathological examination. Multivariate analysis showed that carcinoembryonic antigen, and magnetic resonance imaging radiomics were independent predictors of MLM. Then, the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation. The prediction performance of GLRM in the training and validation queue was 0.765 [95% confidence interval (CI): 0.710-0.820] and 0.767 (95%CI: 0.712-0.822), respectively. Compared with GLRM, RFM achieved superior performance with AUC of 0.919 (95%CI: 0.868-0.970) and 0.901 (95%CI: 0.850-0.952) in the training and validation queue, respectively. The DCA indicated that the predictive ability and net profit of clinical RFM were improved.

CONCLUSION

By combining multiparameter magnetic resonance imaging with the effectiveness and robustness of ML-based predictive models, the proposed clinical RFM can serve as an insight tool for preoperative assessment of MLM risk stratification and provide important information for individual diagnosis and treatment of rectal cancer patients.

Radiomics in ophthalmology: a systematic review

BACKGROUND

Radiomics holds great potential in medical image analysis for various ophthalmic diseases. In recent times, there have been numerous endeavors in this area of research. This systematic review aims to provide a comprehensive assessment of the strengths and limitations of radiomics in ophthalmology.

METHOD

Conforming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic review with a pre-registered protocol (PROSPERO: CRD42023446317). We explored the PubMed, Embase, and Cochrane databases for original studies on this topic and made a comprehensive descriptive integration. Furthermore, the included studies underwent quality assessment by the radiomics quality score (RQS).

RESULTS

A total of 41 articles from an initial search of 227 studies were finally selected for further analysis. These articles included research across five disease categories and covered seven imaging modalities. The radiomics models demonstrated robust performance, with area under the curve (AUC) values mostly falling within 0.7-1.0. The moderate RQS (mean score: 11.17/36) indicated that most studies were retrospectively, single-center analyses without external validation.

CONCLUSIONS

Radiomics holds promising utility in the field of ophthalmology, assisting diagnosis, early-stage screening, and prognostication of treatment response. Artificial intelligence algorithms significantly contribute to the construction of radiomics models in ophthalmology. This study highlights the strengths and challenges of radiomics in ophthalmology and suggests potential avenues for future improvement.

CLINICAL RELEVANCE STATEMENT

Radiomics represents a valuable approach for generating innovative imaging markers, enhancing efficiency in clinical diagnosis and treatment, and aiding decision-making in clinical contexts of many ophthalmic diseases, thereby improving overall patient prognosis.

KEY POINTS

Radiomics has attracted extensive attention in the field of ophthalmology. Articles included five disease categories over seven imaging modalities, consistently yielding AUCs mostly above 0.7. Current research has few prospective and multi-center studies, underlining the necessity for future high-quality studies.

Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence

BACKGROUND

Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm.

METHODS

A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient's tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs.

RESULTS

The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms.

CONCLUSION

The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation.

Texture Analysis and Prediction of Response to Neoadjuvant Treatment in Patients with Locally Advanced Rectal Cancer

Background: The purpose of this study is to determine the relationship between the texture analysis extracted from preoperative rectal magnetic resonance (MR) studies and the response to neoadjuvant treatment. Materials and Methods: In total, 88 patients with rectal adenocarcinoma who underwent staging MR between 2017 and 2022 were retrospectively enrolled. After the completion of neoadjuvant treatment, they underwent surgical resection. The tumour regression grade (TRG) was collected. Patients with TRG 1–2 were classified as responders, while patients with TRG 3 to 5 were classified as non-responders. A texture analysis was conducted using LIFEx software (v 7.6.0), where T2-weighted MR sequences on oriented axial planes were uploaded, and a region of interest (ROI) was manually drawn on a single slice. Features with a Spearman correlation index > 0.5 have been discarded, and a LASSO feature selection has been applied. Selected features were trained using bootstrapping. Results: According to the TRG classes, 49 patients (55.8%) were considered responders, while 39 (44.2) were non-responders. Two features were associated with the responder class: GLCM_Homogeneity and Discretized Histo Entropy log 2. Regarding GLCM_Homogeneity, the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were 0.779 (95% CIs = 0.771–0.816), 86% (80–90), and 67% (60–71). Regarding Discretized Histo Entropy log 2, we found 0.775 AUC (0.700–0.801), 80% sensitivity (74–83), and 63% specificity (58–69). Combining both radiomics features the radiomics signature diagnostic accuracy increased (AUC = 0.844). Finally, the AUC of 1000 bootstraps were 0.810. Conclusions: Texture analysis can be considered an advanced tool for determining a possible correlation between pre-surgical MR data and the response to neoadjuvant therapy.

Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use

Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to (a) provide a perspective on the translational potential and potential impact of radiomics in oncology; (b) explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; (c) discuss strategies to overcome these challenges and mistakes; and (d) propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. Keywords: Radiomics, Oncology, Cancer Management, Artificial Intelligence © RSNA, 2024.

Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation

OBJECTIVES

Independent internal and external validation of three previously published CT-based radiomics models to predict local tumor progression (LTP) after thermal ablation of colorectal liver metastases (CRLM).

MATERIALS AND METHODS

Patients with CRLM treated with thermal ablation were collected from two institutions to collect a new independent internal and external validation cohort. Ablation zones (AZ) were delineated on portal venous phase CT 2-8 weeks post-ablation. Radiomics features were extracted from the AZ and a 10 mm peri-ablational rim (PAR) of liver parenchyma around the AZ. Three previously published prediction models (clinical, radiomics, combined) were tested without retraining. LTP was defined as new tumor foci appearing next to the AZ up to 24 months post-ablation.

RESULTS

The internal cohort included 39 patients with 68 CRLM and the external cohort 52 patients with 78 CRLM. 34/146 CRLM developed LTP after a median follow-up of 24 months (range 5-139). The median time to LTP was 8 months (range 2-22). The combined clinical-radiomics model yielded a c-statistic of 0.47 (95%CI 0.30-0.64) in the internal cohort and 0.50 (95%CI 0.38-0.62) in the external cohort, compared to 0.78 (95%CI 0.65-0.87) in the previously published original cohort. The radiomics model yielded c-statistics of 0.46 (95%CI 0.29-0.63) and 0.39 (95%CI 0.28-0.52), and the clinical model 0.51 (95%CI 0.34-0.68) and 0.51 (95%CI 0.39-0.63) in the internal and external cohort, respectively.

CONCLUSION

The previously published results for prediction of LTP after thermal ablation of CRLM using clinical and radiomics models were not reproducible in independent internal and external validation.

CLINICAL RELEVANCE STATEMENT

Local tumour progression after thermal ablation of CRLM cannot yet be predicted with the use of CT radiomics of the ablation zone and peri-ablational rim. These results underline the importance of validation of radiomics results to test for reproducibility in independent cohorts.

KEY POINTS

• Previous research suggests CT radiomics models have the potential to predict local tumour progression after thermal ablation in colorectal liver metastases, but independent validation is lacking. • In internal and external validation, the previously published models were not able to predict local tumour progression after ablation. • Radiomics prediction models should be investigated in independent validation cohorts to check for reproducibility.

Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective

OBJECTIVE

Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies.

METHODS

We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC.

RESULTS

Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets.

CONCLUSIONS

Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.

Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.

Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features

PURPOSE

To evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models.

METHODS

This study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on "real-world" radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with "real-world" radiomic training data to evaluate their impact on the predictive model's performance.

RESULTS

A prediction model was generated using only "real-world" radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to "real-world" data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n_samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so.

CONCLUSION

Synthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity.

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor

BACKGROUND

Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy.

AIM

To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions.

METHODS

A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test.

RESULTS

The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility.

CONCLUSION

Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Application research of radiomics in colorectal cancer: A bibliometric study

BACKGROUND

Radiomics has shown great potential in the clinical field of colorectal cancer (CRC). However, few bibliometric studies have systematically analyzed existing research in this field. The purpose of this study is to understand the current research status and future development directions of CRC.

METHODS

Search the English documents on the application of radiomics in the field of CRC research included in the Web of Science Core Collection from its establishment to October 2023. VOSviewer and CiteSpace software were used to conduct bibliometric and visual analysis of online publications related to countries/regions, authors, journals, references, and keywords in this field.

RESULTS

A total of 735 relevant documents published from Web of Science Core Collection to October 2023 were retrieved, and a total of 419 documents were obtained based on the screening criteria, including 376 articles and 43 reviews. The number of publications is increasing year by year. Among them, China publishes the most relevant documents (n = 238), which is much higher than Italy (n = 69) and the United States (n = 63). Tian Jie is the author with the most publications and citations (n = 17, citations = 2128), GE Healthcare is the most productive institution (n = 26), Frontiers in Oncology is the journal with the most publications (n = 60), and European Radiology is the most cited journal (n = 776). Hot spots for the application of radiomics in CRC include magnetic resonance, neoadjuvant chemoradiotherapy, survival, texture analysis, and machine learning. These directions are the current hot spots for the application of radiomics research in CRC and may be the direction of continued development in the future.

CONCLUSION

Through bibliometric analysis, the application of radiomics in CRC has been increasing year by year. The application of radiomics improves the accuracy of preoperative diagnosis, prediction, and prognosis of CRC. The results of bibliometrics analysis provide a valuable reference for the research direction of radiomics. However, radiomics still faces many challenges in the future, such as the single nature of the data source which may affect the comprehensiveness of the results. Future studies can further expand the data sources and build a multicenter public database to more comprehensively reflect the research status and development trend of CRC radiomics.

Do treated rectal tumors appear differently on MRI after chemotherapy versus chemoradiotherapy?

PURPOSE

Increasing studies have focused on neoadjuvant chemotherapy (NCT) in rectal cancer. However, few studies explored the differences in radiographic variation between patients treated with NCT and neoadjuvant chemoradiotherapy (NCRT).

METHODS

Stage II/III rectal cancer patients from March 2016 to December 2019 meeting the criteria treated with NCRT or NCT were included. MRI features, including tumor location, longitudinal length, DWI signal, MRI tumor regression grade (mrTRG), and radiomic texture features, before and after neoadjuvant treatments were reviewed.

RESULTS

116 patients with NCRT and 61 with NCT were analyzed. Among these patients, 46 patients in the NCRT group and 18 in the NCT group were responders with pathological TRG0-1. Within these responders, the mean tumor longitudinal length regression rate (TLRR) of the NCT group was 60.08 ± 11.17%, which was significantly higher than the 50.73 ± 15.28% of the NCRT group (p = 0.010). The proportion of high signal in the DWI image after NCT was higher than that of the NCRT group (88.89% vs 50.00%, p = 0.004). NCT responders had significantly higher median change rates than those of NCRT responders in 11 radiomic features, especially those shape features.

CONCLUSION

MRI images change differently between responders treated with NCRT and those with NCT in rectal cancer. The tumor volumetry and some radiomic features change more obviously in NCT responders, and the tumor signal changes more obviously in NCRT responders. During the evaluation of the response of the tumor to the neoadjuvant treatments, images of patients should be treated differently.

Prognostic models for predicting insomnia treatment outcomes: A systematic review

OBJECTIVE

To identify and critically evaluate models predicting insomnia treatment response in adult populations.

METHODS

Pubmed, EMBASE, and PsychInfo databases were searched from January 2000 to January 2023 to identify studies reporting the development or validation of multivariable models predicting insomnia treatment outcomes in adults. Data were extracted according to CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) guidelines and study quality was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Eleven studies describing 53 prediction models were included and appraised. Treatment response was most frequently assessed using wake after sleep onset (n = 10; 18.9%), insomnia severity index (n = 10; 18.9%), and sleep onset latency (n = 9, 17%). Dysfunctional Beliefs About Sleep (DBAS) score was the most common predictor in final models (n = 33). R2 values ranged from 0.06 to 0.80 for models predicting continuous response and area under the curve (AUC) ranged from 0.73 to 0.87 for classification models. Only two models were internally validated, and none were externally validated. All models were rated as having a high risk of bias according to PROBAST, which was largely driven by the analysis domain.

CONCLUSION

Prediction models may be a useful tool to assist clinicians in selecting the optimal treatment strategy for patients with insomnia. However, no externally validated models currently exist. These results highlight an important gap in the literature and underscore the need for the development and validation of modern, methodologically rigorous models.

Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma

BACKGROUND

Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC.

METHODS

Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA).

RESULTS

Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility.

CONCLUSIONS

The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.

Radiogenomics: Contemporary Applications in the Management of Rectal Cancer

Radiogenomics, a sub-domain of radiomics, refers to the prediction of underlying tumour biology using non-invasive imaging markers. This novel technology intends to reduce the high costs, workload and invasiveness associated with traditional genetic testing via the development of 'imaging biomarkers' that have the potential to serve as an alternative 'liquid-biopsy' in the determination of tumour biological characteristics. Radiogenomics also harnesses the potential to unlock aspects of tumour biology which are not possible to assess by conventional biopsy-based methods, such as full tumour burden, intra-/inter-lesion heterogeneity and the possibility of providing the information of tumour biology longitudinally. Several studies have shown the feasibility of developing a radiogenomic-based signature to predict treatment outcomes and tumour characteristics; however, many lack prospective, external validation. We performed a systematic review of the current literature surrounding the use of radiogenomics in rectal cancer to predict underlying tumour biology.

Vesical imaging reporting and data system (VI-RADS) could predict the survival of bladder-cancer patients who received radical cystectomy

Vesical Imaging Reporting and Data System (VI-RADS) shows good potential in determining muscle-invasive bladder cancer (MIBC) patients. However, whether VI-RADS could predict the prognosis of radical cystectomy (RC) patients has not been reported. Our purpose is to determine whether VI-RADS contributed to predict oncologic outcomes. In this retrospective study, we analysed the information of bladder cancer patients who admitted to our centre from June 2012 to June 2022. All patients who underwent multiparametric magnetic resonance imaging (mpMRI) and underwent RC were included. VI-RADS scoring was performed by two radiologists blinded to the clinical data. Patients' clinical features, pathology data, and imaging information were recorded. Kaplan-Meier method was used to estimate patients' overall survival (OS) and progression-free survival (PFS). Log-rank test was used to assess statistical differences. COX regression analysis was used to estimate risk factors. Ultimately, we included 219 patients, with 188 males and 31 females. The median age was 66 (IQR = 61-74.5) years. The VI-RADS scores were as follows: VI-RADS 1, 4 (1.8%); VI-RADS 2, 68 (31.1%); VI-RADS 3, 40 (18.3%); VI-RADS 4, 69 (31.5%); and VI-RADS 5, 38 (17.4%). Patients with VI-RADS ≥ 3 had poorer OS and PFS than those with VI-RADS < 3. The AUC of VI-RADS predicting 3-year OS was 0.804, with sensitivity of 0.824 and negative predictive value of 0.942. Multivariate COX analysis showed that VI-RADS ≥ 3 was risk factors for OS (HR = 3.517, P = 0.003) and PFS (HR = 4.175, P < 0.001). In the MIBC subgroup, patients with VI-RADS ≥ 4 had poorer OS and PFS. In the non-muscle invasive bladder cancer (NMIBC) subgroup, the prognosis of patients with VI-RADS ≥ 3 remained poorer. VI-RADS scores could effectively predict the survival of patients after RC.

Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer

OBJECTIVES

To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset.

METHODS

Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97).

RESULTS

After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables.

CONCLUSIONS

Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset).

CLINICAL RELEVANCE STATEMENT

Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization.

KEY POINTS

This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.

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.

A different perspective on 18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findings

OBJECTIVE

We aimed to determine the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) based primary tumoral and peritumoral radiomics in the prediction of tumor deposits (TDs), tumor budding (TB) and extramural venous invasion (EMVI) of colorectal cancer (CRC).

METHODS

Our retrospective study included 77 CRC patients who had preoperative 18F-FDG PET/CT between June 2020 and February 2022. A total of 131 radiomic features were extracted from primary tumors and peritumoral areas on PET/CT fusion images. The relationship between TDs, TB, EMVI and T stage in the postoperative pathology of the tumors and radiomic features was investigated. Features with a correlation coefficient (CC) less than 0.8 were analyzed by logistic regression. The area under curve (AUC) obtained from the receiver operating characteristic analysis was used to measure the model performance.

RESULTS

A model was developed from primary tumoral and peritumoral radiomics data to predict T stage (AUC 0.931), and also a predictive model was constructed from primary tumor derived radiomics to predict EMVI (AUC 0.739). Radiomic data derived from the primary tumor was obtained as a predictive prognostic factor in predicting TDs and a peritumoral feature was found to be a prognostic factor in predicting TB.

CONCLUSIONS

Intratumoral and peritumoral radiomics derived from 18F-FDG PET/CT are useful for non-invasive early prediction of pathological features that have important implications in the management of CRC.

A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study

The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.

Development and validation of a competitive risk model in patients with rectal cancer: based on SEER database

BACKGROUND

Rectal cancer is one of the most common malignancies. To predict the specific mortality risk of rectal cancer patients, we constructed a predictive nomogram based on a competing risk model.

METHODS

The information on rectal cancer patients was extracted from the SEER database. Traditional survival analysis and specific death analysis were performed separately on the data.

RESULTS

The present study included 23,680 patients, with 16,580 in the training set and 7100 in the validation set. The specific mortality rate calculated by the competing risk model was lower than that of the traditional survival analysis. Age, Marriage, Race, Sex, ICD-O-3Hist/Behav, Grade, AJCC stage, T stage, N stage, Surgery, Examined LN, RX SUMM-SURG OTH, Chemotherapy, CEA, Deposits, Regional nodes positive, Brain, Bone, Liver, Lung, Tumor size, and Malignant were independent influencing factors of specific death. The overall C statistic of the model in the training set was 0.821 (Se = 0.001), and the areas under the ROC curve for cancer-specific survival (CSS) at 1, 3, and 5 years were 0.842, 0.830, and 0.812, respectively. The overall C statistic of the model in the validation set was 0.829 (Se = 0.002), and the areas under the ROC curve for CSS at 1, 3, and 5 years were 0.851, 0.836, and 0.813, respectively.

CONCLUSIONS

The predictive nomogram based on a competing risk model for time-specific mortality in patients with rectal cancer has very desirable accuracy. Thus, the application of the predictive nomogram in clinical practice can help physicians make clinical decisions and follow-up strategies.

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

BACKGROUND

The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM.

AIM

Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free-text medical record data and structured laboratory data to predict LM in postoperative CRC patients.

METHODS

We used a robust dataset of 1463 patients and leveraged state-of-the-art natural language processing (NLP) and machine learning techniques to construct a two-layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two-tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free-text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score-based nomogram using the top 13 valid predictors identified in our study.

RESULTS

The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs.

CONCLUSION

This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision-making.

Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study

PURPOSE

Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor prognosis. Radiomic features have emerged as promising predictors of the tumor phenotype. However, the role of underlying information surrounding the cancer remains unclear.

MATERIALS AND METHODS

We conducted a retrospective study of 508 patients with NSCLC from three institutions. Radiomics models were built using features from six tumor regions and seven classifiers to predict three prognostically significant tumor phenotypes. The models were evaluated and interpreted by the mean area under the receiver operating characteristic curve (AUC) under nested cross-validation and Shapley values. The best-performing predictive models corresponding to six tumor regions and three tumor phenotypes were identified for further comparative analysis. In addition, we designed five experiments with different voxel spacing to assess the sensitivity of the experimental results to the spatial resolution of the voxels.

RESULTS

Our results demonstrated that models based on 2D, 3D, and peritumoral region features yielded mean AUCs and 95% confidence intervals of 0.759 and [0.747-0.771] for lymphovascular invasion, 0.889 and [0.882-0.896] for pleural invasion, and 0.839 and [0.829-0.849] for T-staging in the testing cohort, which was significantly higher than all other models. Similar results were obtained for the model combining the three regional features at five voxel spacings.

CONCLUSION

Our study revealed the predictive role of the developed methods with multi-regional features for the preoperative assessment of prognostic factors in NSCLC. The analysis of different voxel spacing and model interpretability strengthens the experimental findings and contributes to understanding the biological significance of the radiological phenotype.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.

Radiomics-Clinical AI Model with Probability Weighted Strategy for Prognosis Prediction in Non-Small Cell Lung Cancer

In this study, we propose a radiomics clinical probability-weighted model for the prediction of prognosis for non-small cell lung cancer (NSCLC). The model combines radiomics features extracted from radiotherapy (RT) planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volumes (GTVs). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM) were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). The Radiomic model, clinical factor model, and combined radiomic clinical probability-weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949, respectively. The combined radiomics clinical probability-weighted enhanced model achieved significantly better performance than the radiomic model in 1-year survival prediction (chi-square test, p < 0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases

BACKGROUND

Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM).

METHODS

A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia.

RESULTS

Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61-0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50-0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42-0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46-0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44-0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33-0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33-0.74, p = 0.415) images were not predictive of tumour hypoxia.

CONCLUSIONS

T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms.

CRITICAL RELEVANCE STATEMENT

Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM).

Predicting Drug Treatment Outcomes in Childrens with Tuberous Sclerosis Complex-Related Epilepsy: A Clinical Radiomics Study

BACKGROUND AND PURPOSE

Highly predictive markers of drug treatment outcomes of tuberous sclerosis complex-related epilepsy are a key unmet clinical need. The objective of this study was to identify meaningful clinical and radiomic predictors of outcomes of epilepsy drug treatment in patients with tuberous sclerosis complex.

MATERIALS AND METHODS

A total of 105 children with tuberous sclerosis complex-related epilepsy were enrolled in this retrospective study. The pretreatment baseline predictors that were used to predict drug treatment outcomes included patient demographic and clinical information, gene data, electroencephalogram data, and radiomic features that were extracted from pretreatment MR imaging scans. The Spearman correlation coefficient and least absolute shrinkage and selection operator were calculated to select the most relevant features for the drug treatment outcome to build a comprehensive model with radiomic and clinical features for clinical application.

RESULTS

Four MR imaging-based radiomic features and 5 key clinical features were selected to predict the drug treatment outcome. Good discriminative performances were achieved in testing cohorts (area under the curve = 0.85, accuracy = 80.0%, sensitivity = 0.75, and specificity = 0.83) for the epilepsy drug treatment outcome. The model of radiomic and clinical features resulted in favorable calibration curves in all cohorts.

CONCLUSIONS

Our results suggested that the radiomic and clinical features model may predict the epilepsy drug treatment outcome. Age of onset, infantile spasms, antiseizure medication numbers, epileptiform discharge in left parieto-occipital area of electroencephalography, and gene mutation type are the key clinical factors to predict the epilepsy drug treatment outcome. The texture and first-order statistic features are the most valuable radiomic features for predicting drug treatment outcomes.

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.

CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex

PURPOSE

Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects' sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners.

MATERIALS AND METHODS

We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects' sex and age respectively, and we computed a voting model which combined predictions.

RESULTS

The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects' sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects' age (R2 0.568 on all scanners, MAD 7.232 years), with highest results on a single CT scanner (R2 0.667, MAD 3.296 years).

CONCLUSION

Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects' sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.

Repeatability of radiomics studies in colorectal cancer: a systematic review

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Construction of an enhanced computed tomography radiomics model for non-invasively predicting granzyme A in head and neck squamous cell carcinoma by machine learning

PURPOSE

Classical prognostic indicators of head and neck squamous cell carcinoma (HNSCC) can no longer meet the clinical needs of precision medicine. This study aimed to establish a radiomics model to predict Granzyme A (GZMA) expression in patients with HNSCC.

METHODS

We downloaded transcriptomic data of HNSCC patients from The Cancer Genome Atlas for prognosis analysis and then used corresponding enhanced computed tomography (CT) images from The Cancer Imaging Archive for feature extraction and model construction. We explored the influence of differences in GZMA expression on signaling pathways and analyzed the potential molecular mechanism and its relationship with immune cell infiltration. Subsequently, non-invasive CT radiomics models were established to predict the expression of GZMA mRNA and evaluate the correlation with the radiomics-score (Rad-score), related genes, and prognosis.

RESULTS

We found that GZMA was highly expressed in tumor tissues, and high GZMA expression was a protective factor for overall survival. The degree of B and T lymphocyte and natural killer cell infiltration was significantly correlated with GZMA expression. The receiver operating characteristic curve showed that the Relief GBM and RFE_GBM radiomics models had good predictive ability, and there were significant differences in the Rad-score distribution between the high- and low-GZMA-expression groups.

CONCLUSIONS

GZMA expression can significantly affect the prognosis of patients with HNSCC. Enhanced CT radiomics models can effectively predict the expression of GZMA mRNA.

Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging

Purpose

This study verified the value of magnetic resonance imaging (MRI) to construct a nomogram to preoperatively predict extramural vascular invasion (EMVI) in rectal cancer using MRI characteristics.

Materials and methods

There were 55 rectal cancer patients with EMVI and 49 without EMVI in the internal training group. The external validation group consisted of 54 rectal cancer patients with EMVI and 55 without EMVI. High-resolution rectal T2WI, pelvic diffusion-weighted imaging (DWI) sequences, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were used. We collected the following data: distance between the lower tumor margin and the anal margin, distance between the lower tumor margin and the anorectal ring, tumor proportion of intestinal wall, mrT stage, maximum tumor diameter, circumferential resection margin, superior rectal vein width, apparent diffusion coefficient (ADC), T2WI EMVI score, DWI and DCE-MRI EMVI scores, demographic information, and preoperative serum tumor marker data. Logistic regression analyses were used to identify independent risk factors of EMVI. A nomogram prediction model was constructed. Receiver operating characteristic curve analysis verified the predictive ability of the nomogram. P &lt; 0.05 was considered significant.

Result

Tumor proportion of intestinal wall, superior rectal vein width, T2WI EMVI score, and carbohydrate antigen 19-9 were significant independent predictors of EMVI in rectal cancer and were used to create the model. The areas under the receiver operating characteristic curve, sensitivities, and specificities of the nomogram were 0.746, 65.45%, and 83.67% for the internal training group, respectively, and 0.780, 77.1%, and 71.3% for the external validation group, respectively.

Data conclusion

A nomogram including MRI characteristics can predict EMVI in rectal cancer preoperatively and provides a valuable reference to formulate individualized treatment plans and predict prognosis.

CT-based radiomics to distinguish progressive from stable neuroendocrine liver metastases treated with somatostatin analogues: an explorative study

BACKGROUND

Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment.

PURPOSE

To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment.

MATERIAL AND METHODS

A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model.

RESULTS

In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1-50 months). Radiomics models could not accurately classify response (area under the curve 0.44-0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model.

CONCLUSION

Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

OBJECTIVE

The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features.

METHODS

The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features.

RESULTS

The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other).

CONCLUSIONS

Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space.

KEY POINTS

• Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.

Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients

Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD–MTX-based chemotherapy (15–25%) or experience relapse (25–50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < 10−12). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice.

Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies

PURPOSE FOR REVIEW

This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field's best evidence.

RECENT FINDINGS

We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.

Notch-Jagged1 signaling and response to bevacizumab therapy in advanced colorectal cancer: A glance to radiomics or back to physiopathology?

Introduction

The Notch intracellular domain (NICD) and its ligands Jagged-1(Jag1), Delta-like ligand (DLL-3) and DLL4 play an important role in neoangiogenesis. Previous studies suggest a correlation between the tissue levels of NICD and response to therapy with bevacizumab in colorectal cancer (CRC). Another marker that may predict outcome in CRC is radiomics of liver metastases. The aim of this study was to investigate the expression of NICD and its ligands and the role of radiomics in the selection of treatment-naive metastatic CRC patients receiving bevacizumab.

Methods

Immunohistochemistry (IHC) for NICD, Jag1 and E-cadherin was performed on the tissue microarrays (TMAs) of 111 patients with metastatic CRC treated with bevacizumab and chemotherapy. Both the intensity and the percentage of stained cells were evaluated. The absolute number of CD4+ and CD8+ lymphocytes was counted in three different high-power fields and the mean values obtained were used to determine the CD4/CD8 ratio. The positivity of tumor cells to DLL3 and DLL4 was studied. The microvascular density (MVD) was assessed in fifteen cases by counting the microvessels at 20x magnification and expressed as MVD score. Abdominal CT scans were retrieved and imported into a dedicated workstation for radiomic analysis. Manually drawn regions of interest (ROI) allowed the extraction of radiomic features (RFs) from the tumor.

Results

A positive association was found between NICD and Jag1 expression (p < 0.001). Median PFS was significantly shorter in patients whose tumors expressed high NICD and Jag1 (6.43 months vs 11.53 months for negative cases; p = 0.001). Those with an MVD score ≥5 (CD31-high, NICD/Jag1 positive) experienced significantly poorer survival. The radiomic model developed to predict short and long-term survival and PFS yielded a ROC-AUC of 0.709; when integrated with clinical and histopathological data, the integrated model improved the predictive score (ROC-AUC of 0.823).

Discussion

These results show that high NICD and Jag1 expression are associated with progressive disease and early disease progression to anti VEGF-based therapy; the preliminary radiomic analyses show that the integration of quantitative information with clinical and histological data display the highest performance in predicting the outcome of CRC patients.

Changing patterns of neoadjuvant therapy for locally advanced rectal cancer: A narrative review

Standard treatment for patients with locally advanced rectal cancer has been the multidisciplinary approach of neoadjuvant chemoradiotherapy, followed by total mesorectal excision (TME) and postoperative adjuvant chemotherapy. This reduces the local recurrence rate, but the challenge of distant metastasis still persists. The improvement in treatment approach has always been the focus of clinical research and studies have been conducted worldwide in recent years. On one hand, evidence suggests that increasing the intensity of treatment can result in better tumor regression, for example by adding a second drug to the neoadjuvant chemoradiotherapy, or extending the interval between neoadjuvant therapy and surgery, or incorporating chemotherapy and chemoradiotherapy in the neoadjuvant setting. On the other hand, neoadjuvant immunotherapy and selective omission of neoadjuvant radiotherapy may improve the quality of life of patients. In this article, we review the key clinical research progresses in neoadjuvant therapy for locally advanced rectal cancer, hoping to provide some valuable views on the individualized treatment for rectal cancer.

Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer

Background: Radiomic features are increasingly used in CT of NSCLC. However, their robustness with respect to segmentation variability has not yet been demonstrated. The aim of this study was to assess radiomic features agreement across three kinds of segmentation. Methods: We retrospectively included 48 patients suffering from NSCLC who underwent pre-surgery CT. Two expert radiologists in consensus manually delineated three 3D-ROIs on each patient. To assess robustness for each feature, the intra-class correlation coefficient (ICC) across segmentations was evaluated. The ‘sensitivity’ of ICC upon some parameters affecting features computation (such as bin-width for first-order features and pixel-distances for second-order features) was also evaluated. Moreover, an assessment with respect to interpolator and isotropic resolution was also performed. Results: Our results indicate that ‘shape’ features tend to have excellent agreement (ICC > 0.9) across segmentations; moreover, they have approximately zero sensitivity to other parameters. ‘First-order’ features are in general sensitive to parameters variation; however, a few of them showed excellent agreement and low sensitivity (below 0.1) with respect to bin-width and pixel-distance. Similarly, a few second-order features showed excellent agreement and low sensitivity. Conclusions: Our results suggest that a limited number of radiomic features can achieve a high level of reproducibility in CT of NSCLC.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer

OBJECTIVES

To determine the extracellular volume (ECV) fraction derived from equilibrium contrast-enhanced CT for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC).

METHODS

The ECV fraction before NCRT (ECV_pre) and/or ECV after NCRT (ECV_post) of rectal tumors was assessed, and ECV_Δ was calculated as ECV_post - ECV_pre. The histopathologic tumor regression grading (TRG) was assessed. pCR (TRG 0 grade) was defined as the absence of viable tumor cells in the primary tumor and lymph nodes. Demographic and clinicopathological characteristics and ECV fraction were compared between the pCR and non-pCR groups. A mixed model was constructed by logistic regression. The performance for predicting pCR was assessed with the area under the receiver-operator curve (AUC). The AUCs of the different methods were compared by the method proposed by DeLong et al. RESULTS: Seventy-five patients were included; 17 achieved pCR, and 58 achieved non-pCR. The ECV_post (17.05 ± 2.36% vs. 29.94 ± 1.20%; p < 0.001) and ECV_Δ (- 17.01 ± 3.01% vs. 0.44 ± 1.45%; p < 0.001) values in the pCR group were significantly lower than those in the non-pCR group. The mixed model that combined ECV_post with ECV_Δ achieved an AUC of 0.92 (95% confidence interval (CI) = 0.81-0.98), which was higher than that of ECV_post (AUC, 0.91 (95% CI = 0.80-0.97); p = 0.60) or ECV_Δ (AUC, 0.90 (95% CI = 0.79-0.97); p = 0.61).

CONCLUSIONS

ECV_post and ECV_Δ determined by using equilibrium contrast-enhanced CT were useful in distinguishing between pCR and non-pCR patients with LARC who received NCRT.

KEY POINTS

• ECV_post and ECV_Δ (ECV_post - ECV_pre) differed significantly between the non-pCR and pCR groups. • ECV_pre cannot be used to predict the efficacy of neoadjuvant chemoradiotherapy. • ECV_post combined with ECV_Δ had the best performance with an AUC of 0.92 for predicting pCR after NCRT in LARC.

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

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.