A review of different methods used for quantification and assessment of FDG-PET/CT in multiple myeloma

The role of conventional and novel PET radiotracers in assessment of myeloma bone disease

Over 80 % of patients with multiple myeloma (MM) experience osteolytic bone lesions, primarily due to an imbalanced interaction between osteoclasts and osteoblasts. This imbalance can lead to several adverse outcomes such as pain, fractures, limited mobility, and neurological impairments. Myeloma bone disease (MBD) raises the expense of management in addition to being a major source of disability and morbidity in myeloma patients. Whole-body x-ray radiography was the gold standard imaging modality for detecting lytic lesions. Osteolytic lesions are difficult to identify at an earlier stage on X-ray since the lesions do not manifest themselves on conventional radiographs until at least 30 % to 50 % of the bone mass has been destroyed. Hence, early diagnosis of osteolytic lesions necessitates the utilization of more complex and advanced imaging modalities, such as PET. One of the PET radiotracers that has been frequently investigated in MM is 18F-FDG, which has demonstrated a high level of sensitivity and specificity in detecting myeloma lesions. However, 18F-FDG PET/CT has several restrictions, and therefore the novel PET tracers that can overcome the limitations of 18F-FDG PET/CT should be further examined in assessment of MBD. The objective of this review article is to thoroughly examine the significance of both conventional and novel PET radiotracers in the assessment of MBD. The intention is to present the information in a manner that would be easily understood by healthcare professionals from diverse backgrounds, while minimizing the use of complex nuclear medicine terminology.

PET/CT in assessment of colorectal liver metastases: a comprehensive review with emphasis on 18F-FDG

Approximately 25% of those who are diagnosed with colorectal cancer will develop colorectal liver metastases (CRLM) as their illness advances. Despite major improvements in both diagnostic and treatment methods, the prognosis for patients with CRLM is still poor, with low survival rates. Accurate employment of imaging methods is critical in identifying the most effective treatment approach for CRLM. Different imaging modalities are used to evaluate CRLM, including positron emission tomography (PET)/computed tomography (CT). Among the PET radiotracers, fluoro-18-deoxyglucose (18F-FDG), a glucose analog, is commonly used as the primary radiotracer in assessment of CRLM. As the importance of 18F-FDG-PET/CT continues to grow in assessment of CRLM, developing a comprehensive understanding of this subject becomes imperative for healthcare professionals from diverse disciplines. The primary aim of this article is to offer a simplified and comprehensive explanation of PET/CT in the evaluation of CRLM, with a deliberate effort to minimize the use of technical nuclear medicine terminology. This approach intends to provide various healthcare professionals and researchers with a thorough understanding of the subject matter.

Clinical Application of 18F-FDG-PET Quantification in Hematological Malignancies: Emphasizing Multiple Myeloma, Lymphoma and Chronic Lymphocytic Leukemia

Most hematological malignancies display heightened glycolytic activity, leading to their detectability through 18F-FDG-PET imaging. PET quantification enables the extraction of metabolic information from tumors. Among various PET measurements, maximum standardized uptake value (SUVmax), which indicates the highest value of 18F-FDG uptake within the tumor, has emerged as the commonly used parameter in clinical oncology. This is because of SUVmax ease of calculation using most available commercial workstations, as well as its simplicity and independence from observer interpretation. Nonetheless, SUVmax represents the increase in activity within a specific small area, which may not fully capture the overall tumor uptake. Volumetric PET parameters have been identified as a potential solution to overcome certain limitations associated with SUVmax. However, these parameters are influenced by the low spatial resolution of PET when assessing small lesions. Another challenge is the high number of lesions observed in some patients, leading to a time-consuming process for evaluating all focal lesions. Some institutions recently have started advocating for CT-based segmentation as a method for measuring radiotracer uptake in the bone marrow and overall bone of the patients. This review article aims to provide insights into clinical application of PET quantification specifically focusing on 3 major hematologic malignancies: multiple myeloma, lymphoma, and chronic lymphocytic leukemia.

Gradient-based Volumetric PET Parameters on Immediate Pre-ablation FDG-PET Predict Local Tumor Progression in Patients with Colorectal Liver Metastasis Treated by Microwave Ablation

PURPOSE

This study aimed to evaluate the optimal method of segmentation of colorectal liver metastasis (CLM) on immediate pre-ablation PET scans and assess the prognostic value of quantitative pre-ablation PET parameters with regards to local tumor control. A secondary objective was to correlate the target tumor size estimation by PET methods with the tumor measurements on anatomical imaging.

METHODOLOGY

A prospectively accrued cohort of 55 CLMs (46 patients) treated with real-time ¹⁸F-FDG-PET/CT-guided percutaneous microwave ablation was followed-up for a median of 10.8 months (interquartile: 5.5-20.2). Total lesion glycolysis (TLG) and metabolic tumor volume (MTV) values of each CLM were derived from pre-ablation ¹⁸F-FDG-PET with gradient and threshold PET segmentation methodologies. The event was defined as local tumor progression (LTP). Time-dependent receiver operating characteristic (ROC) curve analyses were used to assess area under the curves (AUCs). Intraclass correlation (ICC) and 95.0% confidence interval (CI) were performed to measure the linear relationships between the continuous variables.

RESULTS

AUCs for prediction of LTP obtained from time-dependent ROC analysis for the gradient technique were higher in comparison to the threshold methodologies (AUCs for TLG and volume were: 0.790 and 0.807, respectively). ICC between PET gradient-based and anatomical measurements were higher in comparison to threshold methodologies (ICC for the longest diameter: 733 (95.0% CI 0.538-0.846), ICC for the shortest diameter: .747 (95.0% CI 0.546-0.859), p-values < 0.001).

CONCLUSIONS

The gradient-based technique had a higher AUC for prediction of LTP after microwave ablation of CLM and showed the highest correlation with anatomical imaging tumor measurements.

Segmentation and counting of multiple myeloma cells using IEMD based deep neural network

In biomedical image analysis, segmentation of cell nuclei from microscopic images is a highly challenging research problem. In the computer-assisted health care system, the segmented microscopic cells have been used by many biological researchers for the early prediction of various diseases. Multiple myeloma is one type of disease which is also term as a plasma cell cancer. The segmentation of the nucleus and cell is a very critical step for multiple myeloma detection. Here, In this work, we have designed two modules. One is for recognizing the nucleus of myeloma cells with a deep IEMD neural network, and the other is for differentiating the cell i.e cytoplasm. The different IMFs provides detailed frequency component of an image which are used for feature extraction. This will significantly improves the performance. We proposed a new counting algorithm for counting the myeloma-affected plasma cells in this paper. An algorithm for counting overgrowth plasma cells within the myeloid tissue has been developed using the Python TensorFlow framework. Experimental outcomes on SegPC datasets substantiate that, the proposed deep learning approach outperforms other competitive methods in myeloma recognition and detection. The result of this research indicates that, the proposed image segmentation mechanism can recognize multiple myeloma with superiority. Early detection of multiple myeloma at the initial stage increases the chances to cure patients.

Prognostic significance of conventional and volumetric PET parameters with and without partial volume correction in the assessment of head and neck squamous cell carcinoma

BACKGROUND

The optimal quantification of PET in assessment of head and neck squamous cell carcinoma (HNSCC) is still under development. The effect of partial volume correction (PVC) on the evaluation of survival in the HNSCC patients has not been investigated yet.

METHODOLOGY

Pretreatment 18F-FDG-PET/CT scans of a selected group of 57 patients with advanced stage HNSCC were collected. Conventional (SUVmean and SUVmax) and volumetric [total lesion glycolysis (TLG) and metabolic tumor volume (MTV)] PET metrics were calculated. The ROVER software (ABX GmbH, Radeberg, Germany) automatically applied PVC to the PET metrics. Cox proportional hazards regression model calculated hazard ratio (HR) for assessment of predictive parameters of progression-free survival (PFS).

RESULTS

In multivariate Cox regression analysis, including age, gender, race, human papillomavirus status, and stage, the only significant predictors of PFS were the volumetric PET parameters (TLG: HR, 1.003; 95% CI, 1.001-1.005; P = 0.02), pvcTLG (HR, 1.002; 95% CI, 1.001-1.004; P = 0.01) and MTV (HR, 1.050; 95% CI, 1.024-1.077; P < 0.01). The partial volume-corrected values were significantly higher than the noncorrected values (Wilcoxon sign test; P < 0.05). However, there was not a statistically significant difference between the nonpartial volume corrected and partial volume-corrected PET metrics for assessment of PFS.

CONCLUSION

Volumetric PET metrics were predictors of PFS in Cox regression analysis. Applying PVC could not significantly improve the accuracy of PET metrics for assessment of PFS.