Radiological Response Heterogeneity Is of Prognostic Significance in Metastatic Renal Cell Carcinoma Treated with Vascular Endothelial Growth Factor-targeted Therapy

Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Comprehensive Systematic Review of Biomarkers in Metastatic Renal Cell Carcinoma: Predictors, Prognostics, and Therapeutic Monitoring

BACKGROUND

Challenges remain in determining the most effective treatment strategies and identifying patients who would benefit from adjuvant or neoadjuvant therapy in renal cell carcinoma. The objective of this review is to provide a comprehensive overview of biomarkers in metastatic renal cell carcinoma (mRCC) and their utility in prediction of treatment response, prognosis, and therapeutic monitoring in patients receiving systemic therapy for metastatic disease.

METHODS

A systematic literature search was conducted using the PubMed database for relevant studies published between January 2017 and December 2022. The search focused on biomarkers associated with mRCC and their relationship to immune checkpoint inhibitors, targeted therapy, and VEGF inhibitors in the adjuvant, neoadjuvant, and metastatic settings.

RESULTS

The review identified various biomarkers with predictive, prognostic, and therapeutic monitoring potential in mRCC. The review also discussed the challenges associated with anti-angiogenic and immune-checkpoint monotherapy trials and highlighted the need for personalized therapy based on molecular signatures.

CONCLUSION

This comprehensive review provides valuable insights into the landscape of biomarkers in mRCC and their potential applications in prediction of treatment response, prognosis, and therapeutic monitoring. The findings underscore the importance of incorporating biomarker assessment into clinical practice to guide treatment decisions and improve patient outcomes in mRCC.

Delta Radiomics Model Predicts Lesion-Level Responses to Tyrosine Kinase Inhibitors in Patients with Advanced Renal Cell Carcinoma: A Preliminary Result

BACKGROUND

This study aimed to develop and internally validate computed tomography (CT)-based radiomic models to predict the lesion-level short-term response to tyrosine kinase inhibitors (TKIs) in patients with advanced renal cell carcinoma (RCC).

METHODS

This retrospective study included consecutive patients with RCC that were treated using TKIs as the first-line treatment. Radiomic features were extracted from noncontrast (NC) and arterial-phase (AP) CT images. The model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

A total of 36 patients with 131 measurable lesions were enrolled (training: validation = 91: 40). The model with five delta features achieved the best discrimination capability with AUC values of 0.940 (95% CI, 0.890‒0.990) in the training cohort and 0.916 (95% CI, 0.828‒1.000) in the validation cohort. Only the delta model was well calibrated. The DCA showed that the net benefit of the delta model was greater than that of the other radiomic models, as well as that of the treat-all and treat-none criteria.

CONCLUSIONS

Models based on CT delta radiomic features may help predict the short-term response to TKIs in patients with advanced RCC and aid in lesion stratification for potential treatments.

Heterogeneous radiological response to chemotherapy is associated with poor prognosis in advanced non-small-cell lung cancer

Background

A heterogeneous radiological response is frequently observed in cancer patients and could reflect tumor heterogeneity. We investigated the prognostic impact of heterogeneous radiological responses in patients with advanced non‐small‐cell lung cancer (NSCLC) who received platinum‐based chemotherapy.

Methods

The treatment response according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria was evaluated in 212 patients with advanced NSCLC who received platinum‐based chemotherapy. Patients with partial response (PR) or stable disease (SD) were classified into “PR homo,” “PR hetero,” “SD homo,” and “SD hetero” by the presence of a heterogeneous radiological response, and survival was compared between groups. We also compared survival based on the presence of metabolic responses in lesions with heterogeneous radiological responses.

Results

Fifty‐two patients (24.5%) were classified as PR, 112 patients (52.8%) as SD, and 48 patients (22.7%) as progressive disease (PD). There was no significant difference in progression‐free survival (PFS) and overall survival (OS) between the PR homo and PR hetero groups. The SD homo group had a longer PFS and OS than the SD hetero group. In the SD hetero group, patients with increased maximum standardized uptake value (SUVmax) in lesions with heterogeneous radiological responses had a shorter PFS than those with a stable SUVmax.

Conclusions

The presence of lesions with radiological heterogeneity was associated with disease progression and poor prognosis in the SD group. Patients with heterogeneous radiological responses require careful monitoring.

Response to Immune Checkpoint Inhibitors in Recurrent or Metastatic Esophageal Squamous Cell Carcinoma May Be Affected by Tumor Sites

INTRODUCTION

Heterogeneous tumor response has been reported in cancer patients treated with immune checkpoint inhibitors (ICIs). This study investigated whether the tumor site is associated with the response to ICIs in patients with recurrent or metastatic esophageal squamous cell carcinoma (ESCC).

METHODS

Patients with ESCC who had measurable tumors in the liver, lung, or lymph node (LN) according to the response evaluation criteria in solid tumors (RECIST) 1.1 and received ICIs at 2 medical centers in Taiwan were enrolled. In addition to RECIST 1.1, tumor responses were determined per individual organ basis according to organ-specific criteria modified from RECIST 1.1. Fisher test or χ2 test was used for statistical analysis.

RESULTS

In total, 37 patients were enrolled. The overall response rate per RECIST 1.1 was 13.5%. Measurable tumors in the LN, lung, and liver were observed in 26, 17, and 13 patients, respectively. The organ-specific response rates were 26.9%, 29.4%, and 15.4% for the LN, lung, and liver tumors, respectively (p = 0.05). The organ-specific disease control rates were 69.2%, 52.9%, and 21.1% for the LN, lung, and liver tumors, respectively (p = 0.024). Five (27.8%) among 18 patients harboring at least 2 involved organs had heterogeneous tumor response.

CONCLUSION

The response and disease control to ICIs may differ in ESCC tumors located at different metastatic sites, with a lesser likelihood of response and disease control in metastatic liver tumors than in tumors located at the LNs and lung.