The 18F-FDG PET/CT response to radiotherapy for patients with spinal metastasis correlated with the clinical outcomes

State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

Positron Emission Tomography-Derived Metrics Predict the Probability of Local Relapse After Oligometastasis-Directed Ablative Radiation Therapy

PURPOSE

Early positron emission tomography-derived metrics post-oligometastasis radioablation may predict impending local relapses (LRs), providing a basis for a timely ablation.

METHODS AND MATERIALS

Positron emission tomography data of 623 lesions treated with either 24 Gy single-dose radiation therapy (SDRT) (n = 475) or 3 ×  9 Gy stereotactic body radiation therapy (SBRT) (n = 148) were analyzed in a training data set (n = 246) to obtain optimal cutoffs for pretreatment maximum standardized uptake value (SUVmax) and its 3-month posttreatment decline (ΔSUVmax) in predicting LR risk, validated in a data set unseen to testing (n = 377).

RESULTS

At a median of 21.7 months, 91 lesions developed LRs: 39 of 475 (8.2%) after SDRT and 52 of 148 (35.1%) after SBRT. The optimal cutoff values were 12 for SUVmax and -75% for ΔSUVmax. Bivariate SUVmax/ΔSUVmax permutations rendered a 3-tiered LR risk stratification of dual-favorable (low risk), 1 adverse (intermediate risk) and dual-adverse (high risk). Actuarial 5-year local relapse-free survival rates were 93.9% versus 89.6% versus 57.1% (P < .0001) and 76.1% versus 48.3% versus 8.2% (P < .0001) for SDRT and SBRT, respectively. The SBRT area under the ROC curve was 0.71 (95% CI, 0.61-0.79) and the high-risk subgroup yielded a 76.5% true positive LR prediction rate.

CONCLUSIONS

The SBRT dual-adverse SUVmax/ΔSUVmax category LR prediction power provides a basis for prospective studies testing whether a timely ablation of impending LRs affects oligometastasis outcomes.

Imaging response assessment following stereotactic body radiotherapy for solid tumour metastases of the spine: Current challenges and future directions

Patients with metastatic disease are routinely serially imaged to assess disease burden and response to systemic and local therapies, which places ever-expanding demands on our healthcare resources. Image interpretation following stereotactic body radiotherapy (SBRT) for spine metastases can be challenging; however, appropriate and accurate assessment is critical to ensure patients are managed correctly and resources are optimised. Here, we take a critical review of the merits and pitfalls of various imaging modalities, current response assessment guidelines, and explore novel imaging approaches and the potential for radiomics to add value in imaging assessment.

Does quantification have a role to play in the future of bone SPECT?

Routinely, there is a visual basis to nuclear medicine reporting: a reporter subjectively places a patient's condition into one of multiple discrete classes based on what they see. The addition of a quantitative result, such as a standardised uptake value (SUV), would provide a numerical insight into the nature of uptake, delivering greater objectivity, and perhaps improved patient management.For bone scintigraphy in particular quantification could increase the accuracy of diagnosis by helping to differentiate normal from abnormal uptake. Access to quantitative data might also enhance our ability to characterise lesions, stratify and monitor patients' conditions, and perform reliable dosimetry for radionuclide therapies. But is there enough evidence to suggest that we, as a community, should be making more effort to implement quantitative bone SPECT in routine clinical practice?We carried out multiple queries through the PubMed search engine to facilitate a cross-sectional review of the current status of bone SPECT quantification. Highly cited papers were assessed in more focus to scrutinise their conclusions.An increasing number of authors are reporting findings in terms of metrics such as SUVmax. Although interest in the field in general remains high, the rate of clinical implementation of quantitative bone SPECT remains slow and there is a significant amount of validation required before we get carried away.

Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits

We aimed to evaluate whether radiomic feature-based fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits.

Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict disease-free survival (DFS) and overall survival (OS) and were validated in the validation cohort. A radiomics nomogram with the radiomic signature incorporated was constructed to demonstrate the incremental value of the radiomic signature to the TNM staging system for individualized survival estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. The performance was assessed with concordance index (C-index) and integrated Brier scores.

Results: Significant differences were found between the high- and low-radiomic score (Rad-score) patients in 5-year DFS and OS in training and validation cohorts. Multivariate analysis revealed that the Rad-score was an independent prognostic factor. Incorporating the Rad-score into the radiomics-based nomogram resulted in better performance (C-index: DFS, 0.800; OS, 0.786; in the training cohort) than TNM staging system and clinicopathologic nomogram. Further analysis revealed that patients with higher Rad-scores were prone to benefit from chemotherapy.

Conclusion: The newly developed radiomic signature was a powerful predictor of OS and DFS. Moreover, the radiomic signature could predict which patients could benefit from chemotherapy.