From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting

BACKGROUND AND OBJECTIVE

Neoadjuvant chemotherapy results in various responses when used to treat locally advanced gastric cancer, we aimed to develop and validate a predictive model of the response to neoadjuvant chemotherapy in patients with gastric cancer.

METHODS

A total of 128 patients with locally advanced gastric cancer who underwent pre-treatment computed tomography (CT) scanning followed by neoadjuvant chemoradiotherapy were included (training cohort: n = 64; validation cohort: n = 64). We built a radiomics score combined with laboratory parameters to create a nomogram for predicting the efficacy of neoadjuvant chemotherapy and calculating scores for risk factors.

RESULTS

The radiomics score system demonstrated good stability and prediction performance for the response to neoadjuvant chemotherapy, with the area under the curve of the training and validation cohorts being 0.8 and 0.64, respectively. The radiomics score proved to be an independent risk factor affecting the efficacy of neoadjuvant chemotherapy. In addition to the radiomics score, four other risk factors were included in the nomogram, namely the platelet-to-lymphocyte ratio, total bilirubin, ALT/AST, and CA199. The model had a C-index of 0.8.

CONCLUSIONS

Our results indicated that radiomics features could be potential biomarkers for the early prediction of the response to neoadjuvant treatment.

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

Background

Radiomics, an emerging field, presents a promising avenue for the accurate prediction of biomarkers in different solid cancers. Lung cancer remains a significant global health challenge, contributing substantially to cancer-related mortality. Accurate assessment of Ki-67, a marker reflecting cellular proliferation, is crucial for evaluating tumor aggressiveness and treatment responsiveness, particularly in non-small cell lung cancer (NSCLC).

Methods

A systematic review and meta-analysis conducted following the preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Two authors independently conducted a literature search until September 23, 2023, in PubMed, Embase, and Web of Science. The focus was on identifying radiomics studies that predict Ki-67 expression in lung cancer. We evaluated quality using both Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. For statistical analysis in the meta-analysis, we used STATA 14.2 to assess sensitivity, specificity, heterogeneity, and diagnostic values.

Results

Ten retrospective studies were pooled in the meta-analysis. The findings demonstrated that the use of computed tomography (CT) scan-based radiomics for predicting Ki-67 expression in lung cancer exhibited encouraging diagnostic performance. Pooled sensitivity, specificity, and area under the curve (AUC) in training cohorts were 0.78, 0.81, and 0.85, respectively. In validation cohorts, these values were 0.78, 0.70, and 0.81. Quality assessment using QUADAS-2 and RQS indicated generally acceptable study quality. Heterogeneity in training cohorts, attributed to factors like contrast-enhanced CT scans and specific Ki-67 thresholds, was observed. Notably, publication bias was detected in the training cohort, indicating that positive results are more likely to be published than non-significant or negative results. Thus, journals are encouraged to publish negative results as well.

Conclusion

In summary, CT-based radiomics exhibit promise in predicting Ki-67 expression in lung cancer. While the results suggest potential clinical utility, additional research efforts should concentrate on enhancing diagnostic accuracy. This could pave the way for the integration of radiomics methods as a less invasive alternative to current procedures like biopsy and surgery in the assessment of Ki-67 expression.

The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms

Tuberculous meningitis (TBM) poses a diagnostic challenge, particularly impacting vulnerable populations such as infants and those with untreated HIV. Given the diagnostic intricacies of TBM, there's a pressing need for rapid and reliable diagnostic tools. This review scrutinizes the efficacy of up-and-coming technologies like machine learning in transforming TBM diagnostics and management. Advanced diagnostic technologies like targeted gene sequencing, real-time polymerase chain reaction (RT-PCR), miRNA assays, and metagenomic next-generation sequencing (mNGS) offer promising avenues for early TBM detection. The capabilities of these technologies are further augmented when paired with mass spectrometry, metabolomics, and proteomics, enriching the pool of disease-specific biomarkers. Machine learning algorithms, adept at sifting through voluminous datasets like medical imaging, genomic profiles, and patient histories, are increasingly revealing nuanced disease pathways, thereby elevating diagnostic accuracy and guiding treatment strategies. While these burgeoning technologies offer hope for more precise TBM diagnosis, hurdles remain in terms of their clinical implementation. Future endeavors should zero in on the validation of these tools through prospective studies, critically evaluating their limitations, and outlining protocols for seamless incorporation into established healthcare frameworks. Through this review, we aim to present an exhaustive snapshot of emerging diagnostic modalities in TBM, the current standing of machine learning in meningitis diagnostics, and the challenges and future prospects of converging these domains.

Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer

In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.

A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing's Sarcoma

Objective

In order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing’s sarcoma (ES) based on machine learning (ML) algorithms.

Methods

Clinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application.

Results

LNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py).

Conclusion

With the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92-1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Simple Summary

Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential.

Abstract

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making.

OBJECTIVE

To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range.

RESULTS

From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77-0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial.

CONCLUSIONS

To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.