The correlation of 18F-FDG PET/CT metabolic parameters, clinicopathological factors, and prognosis in breast cancer

Isocitrate dehydrogenases 2-mediated dysfunctional metabolic reprogramming promotes intestinal cancer progression via regulating HIF-1A signaling pathway

Changes in isocitrate dehydrogenases (IDH) lead to the production of the cancer-causing metabolite 2-hydroxyglutarate, making them a cause of cancer. However, the specific role of IDH in the progression of colon cancer is still not well understood. Our current study provides evidence that IDH2 is significantly increased in colorectal cancer (CRC) cells and actively promotes cell growth in vitro and the development of tumors in vivo. Inhibiting the activity of IDH2, either through genetic silencing or pharmacological inhibition, results in a significant increase in α-ketoglutarate (α-KG), indicating a decrease in the reductive citric acid cycle. The excessive accumulation of α-KG caused by the inactivation of IDH2 obstructs the generation of ATP in mitochondria and promotes the downregulation of HIF-1A, eventually inhibiting glycolysis. This dual metabolic impact results in a reduction in ATP levels and the suppression of tumor growth. Our study reveals a metabolic trait of colorectal cancer cells, which involves the active utilization of glutamine through reductive citric acid cycle metabolism. The data suggests that IDH2 plays a crucial role in this metabolic process and has the potential to be a valuable target for the advancement of treatments for colorectal cancer.

Correlation between Histopathological Prognostic Tumor Characteristics and [18F]FDG Uptake in Corresponding Metastases in Newly Diagnosed Metastatic Breast Cancer

BACKGROUND

In metastatic breast cancer (MBC), [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) can be used for staging. We evaluated the correlation between BC histopathological characteristics and [18F]FDG uptake in corresponding metastases.

PATIENTS AND METHODS

Patients with non-rapidly progressive MBC of all subtypes prospectively underwent a baseline histological metastasis biopsy and [18F]FDG-PET. Biopsies were assessed for estrogen, progesterone, and human epidermal growth factor receptor 2 (ER, PR, HER2); Ki-67; and histological subtype. [18F]FDG uptake was expressed as maximum standardized uptake value (SUVmax) and results were expressed as geometric means.

RESULTS

Of 200 patients, 188 had evaluable metastasis biopsies, and 182 of these contained tumor. HER2 positivity and Ki-67 ≥ 20% were correlated with higher [18F]FDG uptake (estimated geometric mean SUVmax 10.0 and 8.8, respectively; p = 0.0064 and p = 0.014). [18F]FDG uptake was lowest in ER-positive/HER2-negative BC and highest in HER2-positive BC (geometric mean SUVmax 6.8 and 10.0, respectively; p = 0.0058). Although [18F]FDG uptake was lower in invasive lobular carcinoma (n = 31) than invasive carcinoma NST (n = 146) (estimated geometric mean SUVmax 5.8 versus 7.8; p = 0.014), the metastasis detection rate was similar.

CONCLUSIONS

[18F]FDG-PET is a powerful tool to detect metastases, including invasive lobular carcinoma. Although BC histopathological characteristics are related to [18F]FDG uptake, [18F]FDG-PET and biopsy remain complementary in MBC staging (NCT01957332).

18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

This study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy.

RESULTS

This study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938).

CONCLUSION

The integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.

18F-FDG PET/CT-derived total lesion glycolysis predicts abscess formation in patients with surgically confirmed infective endocarditis: Results of a retrospective study at a tertiary center

BACKGROUND

Abnormal activity of 18F-FDG PET/CT is a major Duke criterion in the diagnostic work-up of infective prosthetic valve endocarditis (IE). We hypothesized that quantitative lesion assessment by 18F-FDG PET/CT-derived standard maximum uptake ratio (SURmax), metabolic volume (MV), and total lesion glycolysis (TLG) might be useful in distinct subgroups of IE patients (e.g. IE-related abscess formation).

METHODS

All patients (n = 27) hospitalized in our tertiary IE referral medical center from January 2014 to October 2018 with preoperatively performed 18F-FDG PET/CT and surgically confirmed IE were included into this retrospective analysis.

RESULTS

Patients with surgically confirmed abscess formation (n = 10) had significantly increased MV (by ~ fivefold) and TLG (by ~ sevenfold) as compared to patients without abscess (n = 17). Receiver operation characteristics (ROC) analyses demonstrated that TLG (calculated as MV × SURmean, i.e. TLG (SUR)) had the most favorable area under the ROC curve (0.841 [CI 0.659 to 1.000]) in predicting IE-related abscess formation. This resulted in a sensitivity of 80% and a specificity of 88% at a cut-off value of 14.14 mL for TLG (SUR).

CONCLUSION

We suggest that 18F-FDG PET/CT-derived quantitative assessment of TLG (SUR) may provide a novel diagnostic tool in predicting endocarditis-associated abscess formation.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

 Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

 The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

 AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

Role of multiple dual-phase 18F-FDG PET/CT metabolic parameters in differentiating adenocarcinomas from squamous cell carcinomas of the lung

Purpose

To evaluate the ability of multiple dual-phase 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters to distinguish the histological subtypes of non-small cell lung cancer (NSCLC).

Methods

Data from 127 patients with non-small cell lung cancer who underwent preoperative dual-phase 18F-FDG PET/CT scanning at the PET-CT center of our hospital from December 2020 to October 2021 were collected, and the metabolic parameters of their primary lesions were measured and analyzed retrospectively. Intraclass correlation coefficients (ICC) were calculated for consistency between readers. Metabolic parameters in the early (SUVpeak, SUVmean, SUVmin, SUVmax, MTV, and TLG) and delayed phases (dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, dpMTV, and dpTLG) were calculated. We drew receiver operating characteristic (ROC) curves to compare the differences in different metabolic parameters between the adenocarcinoma (AC) and squamous cell carcinoma (SCC) groups and evaluated the ability of different metabolic parameters to distinguish AC from SCC.

Results

Inter-reader agreement, as assessed by the intraclass correlation coefficient (ICC), was good (ICC = 0.71, 95% CI:0.60-0.79). The mean MTV, SUVmax, TLG, SUVpeak, SUVmean, dpSUVmax, dpTLG, dpSUVpeak, dpSUVmean, and dpSUVmin of the tumors were significantly higher in SCC lesions than in AC lesions (P = 0.049, < 0.001, 0.016, < 0.001, 0.001, < 0.001, 0.018, < 0.001, 0.001, and 0.001, respectively). The diagnostic efficacy of the metabolic parameters in 18F-FDG PET/CT for differentiating adenocarcinoma from squamous cell carcinoma ranged from high to low as follows: SUVpeak (AUC = 0.727), SUVmax (AUC = 0.708), dpSUVmax (AUC = 0.699), dpSUVpeak (AUC = 0.698), TLG (AUC = 0.695), and dpTLG (AUC = 0.692), SUVmean (AUC = 0.690), dpSUVmean (AUC = 0.687), dpSUVmin (AUC = 0.680), SUVmin (AUC = 0.676), and MTV (AUC = 0.657).

Conclusions

Squamous cell carcinoma of the lung had higher mean MTV, SUVmax, TLG, SUVpeak, SUVmean, SUVmin, dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, and dpTLG than AC, which can be helpful tools in differentiating between the two. The metabolic parameters of the delayed phase (2 h after injection) 18F-FDG PET/CT did not improve the diagnostic efficacy in distinguishing lung AC from SCC. Conventional dual-phase 18F-FDG PET/CT is not recommended.

The potential application of MR-derived ADCmin values from 68Ga-DOTATATE and 18F-FDG dual tracer PET/MR as replacements for FDG PET in assessment of grade and stage of pancreatic neuroendocrine tumors

BACKGROUND

To evaluate the utility of ⁶⁸Ga-DOTATATE and ¹⁸F-FDG PET/MR for prediction of grade and stage of pancreatic neuroendocrine tumors (PNETs), and to examine the correlation between parameters obtained from FDG PET and diffusion-weighted imaging (DWI) MR parameters.

METHODS

A retrospective study using ⁶⁸Ga-DOTATATE and ¹⁸F-FDG PET/MR imaging was performed between April 2020 and May 2022 on 46 individuals with histologically confirmed PNETs. Metabolic tumor volume (MTV), maximum standardized uptake value (FSUVmax), and tumor lesion glycolysis (TLG) for FDG; somatostatin receptor density (SRD), maximum standardized uptake value (GSUVmax), and total lesion somatostatin receptor density (TLSRD) for DOTATATE; and minimum and mean apparent diffusion coefficient (ADCmin and ADCmean) values for MRI, respectively. We performed Spearman's correlation analysis to examine the links between these variables and primary tumor stage and grading.

RESULTS

Higher PNET grading was associated with higher FSUVmax, MTV, and TLG values (P < 0.05). TLG, SRD, ADCmin, and ADCmean values were correlated with N staging, while SRD, MTV, ADCmin, TLG, and ADCmean were associated with M staging. Notably, ADCmin was a negative correlation between FSUVmax (r =  - 0.52; P < 0.001), MTV (r =  - 0.50; P < 0.001), and TLG (r =  - 0.56; P < 0.001).

CONCLUSIONS

This study highlights significant correlative relationships between FDG PET-derived parameters and ADCmin. ADCmin may offer utility as a tool for PNET staging and grading in lieu of FDG PET. ⁶⁸Ga-DOTATATE PET/MR alone may be a sufficient alternative to dual tracer PET/MR when conducting grading and staging of primary PNETs.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

· Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

CITATION FORMAT

· Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.

The influence of receptor expression and clinical subtypes on baseline [18F]FDG uptake in breast cancer: systematic review and meta-analysis

BACKGROUND

To quantify the relationship between [18F]FDG uptake of the primary tumour measured by PET-imaging with immunohistochemical (IHC) expression of ER, PR, HER2, Ki-67, and clinical subtypes based on these markers in breast cancer patients.

METHODS

PubMed and Embase were searched for studies that compared SUVmax between breast cancer patients negative and positive for IHC expression of ER, PR, HER2, Ki-67, and clinical subtypes based on these markers. Two reviewers independently screened the studies and extracted the data. Standardized mean differences (SMD) and 95% confidence intervals (CIs) were estimated by using DerSimonian-Laird random-effects models. P values less than or equal to 5% indicated statistically significant results.

RESULTS

Fifty studies were included in the final analysis. SUVmax is significantly higher in ER-negative (31 studies, SMD 0.66, 0.56-0.77, P < 0.0001), PR-negative (30 studies, SMD 0.56; 0.40-0.71, P < 0.0001), HER2-positive (32 studies, SMD - 0.29, - 0.49 to - 0.10, P = 0.0043) or Ki-67-positive (19 studies, SMD - 0.77; - 0.93 to - 0.61, P < 0.0001) primary tumours compared to their counterparts. The majority of clinical subtypes were either luminal A (LA), luminal B (LB), HER2-positive or triple negative breast cancer (TNBC). LA is associated with significantly lower SUVmax compared to LB (11 studies, SMD - 0.49, - 0.68 to - 0.31, P = 0.0001), HER2-positive (15 studies, SMD - 0.91, - 1.21 to - 0.61, P < 0.0001) and TNBC (17 studies, SMD - 1.21, - 1.57 to - 0.85, P < 0.0001); and LB showed significantly lower uptake compared to TNBC (10 studies, SMD - 0.77, - 1.05 to - 0.49, P = 0.0002). Differences in SUVmax between LB and HER2-positive (9 studies, SMD - 0.32, - 0.88 to 0.24, P = 0.2244), and HER2-positive and TNBC (17 studies, SMD - 0.29, - 0.61 to 0.02, P = 0.0667) are not significant.

CONCLUSION

Primary tumour SUVmax is significantly higher in ER-negative, PR-negative, HER2-positive and Ki-67-positive breast cancer patients. Luminal tumours have the lowest and TNBC tumours the highest SUVmax. HER2 overexpression has an intermediate effect.

Prognostic impact of 18F-FDG PET/CT in pathologic stage II invasive ductal carcinoma of the breast: re-illuminating the value of PET/CT in intermediate-risk breast cancer

BACKGROUND

The aim of this study is to investigate the impact of ¹⁸F-FDG PET/CT on prognosis of stage II invasive ductal carcinoma (IDC) of the breast primarily treated with surgery.

METHODS

The clinical records of 297 consecutive IDC with preoperative PET/CT and pathologically staged II in surgery from 2013 to 2017 were retrospectively reviewed. The maximum standardized uptake value (SUVmax), peak standardized uptake value (SUVpeak), tumor-to-liver ratio (TLR), and metabolic tumor volume (MTV) were measured. Association of clinicopathologic factors (age, T stage, N stage, AJCC pathologic stage of IIA or IIB, pathologic prognostic stage, grade, hormonal receptor status, HER2 status, Ki-67, and adjuvant therapy) and PET parameters with DFS was assessed using the Cox proportional hazards model.

RESULTS

There were 35 recurrences and 10 deaths at a median follow-up of 49 months (range 0.8 ~ 87.3). All PET parameters were significantly associated with DFS in univariate analysis but in multivariate analysis, SUVpeak was the only factor significantly associated with DFS (hazard ratio 2.58, 95% confidence interval 1.29-5.15, P = 0.007). In cohorts with higher values of SUVpeak or TLR, patients who received adjuvant chemotherapy had significantly superior DFS.

CONCLUSION

Metabolic parameters derived from preoperative PET/CT was significantly associated with recurrence in stage II IDC primarily treated with surgery. PET/CT can be a powerful prognostic tool in conjunction with novel staging systems and current biomarkers for patients undergoing contemporary therapy. Our results urge to reconsider the currently underestimated value of PET/CT confined to diagnostic aspect and to newly recognize its prognostic impact in these intermediate-risk breast cancer.

Geometrical Comparison and Quantitative Evaluation of 18F-FDG PET/CT- and DW-MRI-Based Target Delineation Before and During Radiotherapy for Esophageal Squamous Carcinoma

Background and Purpose

This study aimed to evaluate the geometrical differences in and metabolic parameters of ¹⁸F-fluorodeoxyglucose positron emission tomography–computed tomography (¹⁸F-FDG PET-CT) and diffusion-weighted magnetic resonance imaging (DW-MRI) performed before and during radiotherapy (RT) for patients with esophageal cancer based on the three-dimensional CT (3DCT) medium and explore whether the high signal area derived from DW-MRI can be used as a tool for an individualized definition of the volume in need of dose escalation for esophageal squamous cancer.

Materials and Methods

Thirty-two patients with esophageal squamous cancer sequentially underwent repeated 3DCT, ¹⁸F-FDG PET-CT, and enhanced MRI before the initiation of RT and after the 15th fraction. All images were fused with 3DCT images through deformable registration. The gross tumor volume (GTV) was delineated based on PET Edge on the first and second PET-CT images and defined as GTV_PETpre and GTV_PETdur, respectively. GTV_DWIpre and GTV_DWIdur were delineated on the first and second DWI and corresponding T₂-weighted MRI (T₂W-MRI)-fused images. The maximum, mean, and peak standardized uptake values (SUVs; SUV_max, SUV_mean, and SUV_peak, respectively); metabolic tumor volume (MTV); and total lesion glycolysis(TLG) and its relative changes were calculated automatically on PET. Similarly, the minimum and mean apparent diffusion coefficient (ADC; ADC_min and ADC_mean) and its relative changes were measured manually using ADC maps.

Results

The volume of GTV_CT exhibited a significant positive correlation with that of GTV_PET and GTV_DWI (both p < 0.001). Significant differences were observed in both ADCs and ¹⁸F-FDG PET metabolic parameters before and during RT (both p < 0.001). No significant correlation was observed between SUVs and ADCs before and during RT (p = 0.072–0.944) and between ∆ADCs and ∆SUVs (p = 0.238–0.854). The conformity index and degree of inclusion of GTV_PETpre to GTV_DWIpre were significantly higher than those of GTV_PETdur to GTV_DWIdur (both p < 0.001). The maximum diameter shrinkage rate (∆LD_DWI) (24%) and the tumor volume shrinkage rate (VRR_DWI) (60%) based on DW-MRI during RT were significantly greater than the corresponding PET-based ∆LD_PET (14%) and VRR_PET (41%) rates (p = 0.017 and 0.000, respectively).

Conclusion

Based on the medium of CT images, there are significant differences in spatial position, biometabolic characteristics, and the tumor shrinkage rate for GTVs derived from ¹⁸F-FDG PET-CT and DW-MRI before and during RT for esophageal squamous cancer. Further studies are needed to determine if DW-MRI will be used as tool for an individualized definition of the volume in need of dose escalation.

Relationship between Prognostic Stage in Breast Cancer and Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography

This retrospective study examined the relationship between the standardized uptake value max (SUVmax) of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and the prognostic stage of breast cancer. We examined 358 breast cancers in 334 patients who underwent ¹⁸F-FDG PET/CT for initial staging between January 2016 and December 2019. We extracted data including SUVmax of ¹⁸F-FDG PET and pathological biomarkers, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and nuclear grade. Anatomical and prognostic stages were determined per the American Joint Committee on Cancer (eighth edition). We examined whether there were statistical differences in SUVmax between each prognostic stage. The mean SUVmax values for clinical prognostic stages were as follow: stage 0, 2.2 ± 1.4; stage IA, 2.6 ± 2.1; stage IB, 4.2 ± 3.5; stage IIA, 5.2 ± 2.8; stage IIB, 7.7 ± 6.7; and stage III + IV, 7.0 ± 4.5. The SUVmax values for pathological prognostic stages were as follows: stage 0, 2.2 ± 1.4; stage IA, 2.8 ± 2.2; stage IB, 5.4 ± 3.6; stage IIA, 6.3 ± 3.1; stage IIB, 9.2 ± 7.5, and stage III + IV, 6.2 ± 5.2. There were significant differences in mean SUVmax between clinical prognostic stage 0 and ≥II (p < 0.001) and I and ≥II (p < 0.001). There were also significant differences in mean SUVmax between pathological prognostic stage 0 and ≥II (p < 0.001) and I and ≥II (p < 0.001). In conclusion, mean SUVmax increased with all stages up to prognostic stage IIB, and there were significant differences between several stages. The SUVmax of ¹⁸F-FDG PET/CT may contribute to prognostic stage stratification, particularly in early cases of breast cancers.