Association between tumor heterogeneity and progression-free survival in non-small cell lung cancer patients with EGFR mutations undergoing tyrosine kinase inhibitors therapy

Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer

Background

Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited.

Methods

Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People’s Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO).

Results

The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar’s test p = 0.0003).

Conclusions

A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients.

The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

Background and purpose

Radiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown.

Methods

Computed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation.

Results

Hierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models.

Conclusion

We firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.

Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules

Controversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815–0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712–0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.

Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma

BACKGROUND

To investigate the survival prognostic value of the radiomic features of 18F-FDG PET in patients who had EGFR (epidermal growth factor receptor) mutated lung adenocarcinoma and received targeted TKI (tyrosine kinase inhibitor) treatment.

METHODS

Fifty-one patients with stage III-IV lung adenocarcinoma and actionable EGFR mutation who received first-line TKI were retrospectively analyzed. All patients underwent pretreatment 18F-FDG PET/CT, and we calculated the PET-derived radiomic features. Cox proportional hazard model was used to examine the association between the radiomic features and the survival outcomes, including progression-free survival (PFS) and overall survival (OS). A score model was established according to the independent prognostic predictors and we compared this model to the TNM staging system using Harrell's concordance index (c-index).

RESULTS

Forty-eight patients (94.1%) experienced disease progression and 41 patients (80.4%) died. Primary tumor SUV entropy > 5.36, and presence of pleural effusion were independently associated with worse OS (both p < 0.001) and PFS (p = 0.001, and 0.003, respectively). We used these two survival predictors to devise a scoring system (score 0-2). Patients with a score of 1 or 2 had a worse survival than those with a score of 0 (HR for OS: 3.6, p = 0.006 for score 1, and HR: 21.8, p < 0.001 for score 2; HR for PFS: 2.2, p = 0.027 for score 1 and HR: 8.8, p < 0.001 for score 2). Our scoring system surpassed the TNM staging system (c-index = 0.691 versus 0.574, p = 0.013 for OS, and c-index = 0.649 versus 0.517, p = 0.004 for PFS).

CONCLUSIONS

In this preliminary study, combining PET radiomics with clinical risk factors may improve survival stratification in stage III-IV lung adenocarcinoma with actionable EFGR mutation. Our proposed scoring system may assist with optimization of individualized treatment strategies in these patients.

Can Magnetic Resonance Radiomics Analysis Discriminate Parotid Gland Tumors? A Pilot Study

Our purpose is to evaluate the performance of magnetic resonance (MR) radiomics analysis for differentiating between malignant and benign parotid neoplasms and, among the latter, between pleomorphic adenomas and Warthin tumors. We retrospectively evaluated 75 T2-weighted images of parotid gland lesions, of which 61 were benign tumors (32 pleomorphic adenomas, 23 Warthin tumors and 6 oncocytomas) and 14 were malignant tumors. A receiver operating characteristics (ROC) curve analysis was performed to find the threshold values for the most discriminative features and determine their sensitivity, specificity and area under the ROC curve (AUROC). The most discriminative features were used to train a support vector machine classifier. The best classification performance was obtained by comparing a pleomorphic adenoma with a Warthin tumor (yielding sensitivity, specificity and a diagnostic accuracy as high as 0.8695, 0.9062 and 0.8909, respectively) and a pleomorphic adenoma with malignant tumors (sensitivity, specificity and a diagnostic accuracy of 0.6666, 0.8709 and 0.8043, respectively). Radiomics analysis of parotid tumors on conventional T2-weighted MR images allows the discrimination of pleomorphic adenomas from Warthin tumors and malignant tumors with a high sensitivity, specificity and diagnostic accuracy.

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

MR-Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph-Vascular Space Invasion preoperatively

Background

Lymph‐vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI.

Purpose

To develop and validate an axial T₁ contrast‐enhanced (CE) MR‐based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively.

Study Type

Retrospective.

Population

In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio.

Field Strength/Sequence

T₁ CE MRI sequences at 1.5T.

Assessment

Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram.

Statistical Tests

The Mann–Whitney U‐test and the chi‐square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum‐redundancy/maximum‐relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression.

Results

Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non‐LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326–0.8745) in the training cohort and 0.727 (95% CI, 0.5449–0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort.

Data Conclusion

T₁ CE MR‐based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively.

Level of Evidence: 4

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:1420–1426.

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature.

The Role of Tumor Microenvironment in Chemoresistance: To Survive, Keep Your Enemies Closer

Chemoresistance is a leading cause of morbidity and mortality in cancer and it continues to be a challenge in cancer treatment. Chemoresistance is influenced by genetic and epigenetic alterations which affect drug uptake, metabolism and export of drugs at the cellular levels. While most research has focused on tumor cell autonomous mechanisms of chemoresistance, the tumor microenvironment has emerged as a key player in the development of chemoresistance and in malignant progression, thereby influencing the development of novel therapies in clinical oncology. It is not surprising that the study of the tumor microenvironment is now considered to be as important as the study of tumor cells. Recent advances in technological and analytical methods, especially ‘omics’ technologies, has made it possible to identify specific targets in tumor cells and within the tumor microenvironment to eradicate cancer. Tumors need constant support from previously ‘unsupportive’ microenvironments. Novel therapeutic strategies that inhibit such microenvironmental support to tumor cells would reduce chemoresistance and tumor relapse. Such strategies can target stromal cells, proteins released by stromal cells and non-cellular components such as the extracellular matrix (ECM) within the tumor microenvironment. Novel in vitro tumor biology models that recapitulate the in vivo tumor microenvironment such as multicellular tumor spheroids, biomimetic scaffolds and tumor organoids are being developed and are increasing our understanding of cancer cell-microenvironment interactions. This review offers an analysis of recent developments on the role of the tumor microenvironment in the development of chemoresistance and the strategies to overcome microenvironment-mediated chemoresistance. We propose a systematic analysis of the relationship between tumor cells and their respective tumor microenvironments and our data show that, to survive, cancer cells interact closely with tumor microenvironment components such as mesenchymal stem cells and the extracellular matrix.