Evaluation of the Prognostic Value of FDG PET/CT Parameters for Patients With Surgically Treated Head and Neck Cancer: A Systematic Review

Detecting recurrent disease and surgical outcomes in patients with locally advanced tongue cancer after multimodal treatment using ¹⁸F-FDG-PET/CT

The purpose of this study was to evaluate the utility of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FPCT) parameters for detecting recurrent disease and the outcomes of salvage surgery in patients with locally advanced oral tongue squamous cell carcinoma (TSCC) after multimodal treatment. In total, 69 patients with locally advanced TSCC were treated with multimodal therapy. All patients underwent whole-body FPCT scans 4-10 months after the initial surgery. The analysis included FPCT parameters, such as maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Histological examination was used as the reference standard. Patients with recurrent TSCC underwent salvage surgery or surgery plus systemic treatment. This study included 69 patients: 36 in the recurrent TSCC group and 33 in the non-recurrent TSCC group. The SUVmax, MTV, and TLG in the recurrent TSCC group were 11.3 ± 3.6, 28.3 ± 15.6 cm3, and 113.2 ± 46.8 g, respectively; these values were 5.9 ± 3.6, 5.1 ± 2.2 cm3, and 13.4 ± 4.8 g, in the non-recurrent TSCC group respectively. The two groups had significant differences in terms of SUVmax, MTV, and TLG. In the recurrent TSCC group, 91.6 % of patients presented with local, locoregional, and regional disease and underwent salvage surgery plus systemic therapy, whereas 8.4 % had locoregional recurrence with distant metastases alone and underwent surgery plus systemic therapy. The patients were followed up for 12-60 months; 19 and 20 patients in the recurrent and non-recurrent TSCC groups showed no evidence of disease, whereas 11 and 8 were alive with the disease. Local recurrence or distant metastases led to the deaths of six patients in the recurrent TSCC group and five in the non-recurrent TSCC group. No significant differences in survival were observed between the two groups. FPCT parameters can detect the recurrence of locally advanced TSCC after multimodal treatment. Early salvage surgery can improve the treatment outcomes for recurrent TSCC.

Role of 18F-FDG PET/CT in Head and Neck Squamous Cell Carcinoma: Current Evidence and Innovative Applications

This article provides an overview of the use of 18F-FDG PET/CT in various clinical scenarios of head-neck squamous cell carcinoma, ranging from initial staging to treatment-response assessment, and post-therapy follow-up, with a focus on the current evidence, debated issues, and innovative applications. Methodological aspects and the most frequent pitfalls in head-neck imaging interpretation are described. In the initial work-up, 18F-FDG PET/CT is recommended in patients with metastatic cervical lymphadenectomy and occult primary tumor; moreover, it is a well-established imaging tool for detecting cervical nodal involvement, distant metastases, and synchronous primary tumors. Various 18F-FDG pre-treatment parameters show prognostic value in terms of disease progression and overall survival. In this scenario, an emerging role is played by radiomics and machine learning. For radiation-treatment planning, 18F-FDG PET/CT provides an accurate delineation of target volumes and treatment adaptation. Due to its high negative predictive value, 18F-FDG PET/CT, performed at least 12 weeks after the completion of chemoradiotherapy, can prevent unnecessary neck dissections. In addition to radiomics and machine learning, emerging applications include PET/MRI, which combines the high soft-tissue contrast of MRI with the metabolic information of PET, and the use of PET radiopharmaceuticals other than 18F-FDG, which can answer specific clinical needs.

Head-to-head comparison of [68Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT for the evaluation of tonsil cancer and lymph node metastases: a single-centre retrospective study

BACKGROUND

This study aimed to compare the diagnostic value of [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT imaging for primary lesions and metastatic lymph nodes in patients with tonsil cancer.

METHOD

Twenty-one tonsil cancer patients who underwent [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT scans within two weeks in our centre were retrospectively enrolled. The maximum standardized uptake value (SUVmax) and tumor-to-background ratio (TBR) of the two tracers were compared by using the Mann‒Whitney U test. In addition, the sensitivity, specificity, and accuracy of the two methods for diagnosing metastatic lymph nodes were analysed.

RESULTS

In detecting primary lesions, the efficiency was higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (20/22) than for [18F]FDG PET/CT (9/22). Although [68 Ga]Ga-DOTA-FAPI-04 uptake (SUVmax, 5.03 ± 4.06) was lower than [18F]FDG uptake (SUVmax, 7.90 ± 4.84, P = 0.006), [68 Ga]Ga-DOTA-FAPI-04 improved the distinction between the primary tumor and contralateral normal tonsillar tissue. The TBR was significantly higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (3.19 ± 2.06) than for [18F]FDG PET/CT (1.89 ± 1.80) (p < 0.001). In lymph node analysis, SUVmax and TBR were not significantly different between [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT (7.67 ± 5.88 vs. 8.36 ± 6.15, P = 0.498 and 5.56 ± 4.02 vs. 4.26 ± 3.16, P = 0.123, respectively). The specificity and accuracy of [68 Ga]Ga-DOTA-FAPI-04 PET/CT were higher than those of [18F]FDG PET/CT in diagnosing metastatic cervical lymph nodes (all P < 0.05).

CONCLUSION

The availability of [68 Ga]Ga-DOTA-FAPI-04 complements the diagnostic results of [18F]FDG by improving the detection rate of primary lesions and the diagnostic accuracy of cervical metastatic lymph nodes in tonsil cancer compared to [18F]FDG.

Association of circulating tumor HPV16DNA levels and quantitative PET parameters in patients with HPV-positive head and neck squamous cell carcinoma

Circulating tumor DNA (ctDNA), which circulates in the blood after being shed from cancer cells in the body, has recently gained attention as an excellent tumor marker. The purpose of this study was to evaluate whether ct human papillomavirus (HPV) 16 DNA (ctHPV16DNA) levels were associated with quantitative PET parameters in patients with HPV-positive head and neck (HN) squamous cell carcinoma (SCC). Fifty patients with oropharyngeal SCC (OPSCC) and 5 with SCC of unknown primary (SCCUP) before treatment were included. They all underwent blood sampling to test ctHPV16DNA levels and FDG PET-CT examinations. Quantitative PET parameters included SUVmax, metabolic tumor volume (MTV), MTV of whole-body lesions (wbMTV), and 56 texture features. ctHPV16DNA levels were compared to texture features of primary tumors in OPSCC patients (Group A) or the largest primary or metastatic lymph node lesions in OPSCC and SCCUP patients (Group B) and to other PET parameters. Spearman rank correlation test and multiple regression analysis were used to confirm the associations between ctHPV16DNA levels and PET parameters. ctHPV16DNA levels moderately correlated with wbMTV, but not with SUVmax or MTV in Groups A and B. ctHPV16DNA levels exhibited a weak negative correlation with low gray-level zone emphasis in Groups A and B. Multiple regression analysis revealed that wbMTV and high gray-level zone emphasis were the significant factors for ctHPV16DNA levels in Group B. These results were not observed in Group A. This study demonstrated that ctHPV16DNA levels correlated with the whole-body tumor burden and tumor heterogeneity visualized on FDG PET-CT in patients with HPV-positive HNSCC.

Prognostic value of haematological parameters and [18F]FDG PET/CT metabolic parameters in head and neck cancer

AIM

To determine the usefulness of neutrophil/lymphocyte (N/L) and platelet/lymphocyte (P/L) ratios as well as quantitative [18F]FDG PET/CT parameters as prognostic factors for overall survival (OS), cancer-specific survival (CSS) and progression-free survival (PFS) in patients with head and neck squamous cell carcinoma (HyN).

MATERIAL AND METHODS

Sixty-six patients (56 men) diagnosed with HyN carcinoma were retrospectively assessed over an 8-year interval. Maximum SUV (SUVmax), metabolic tumour volume (MTV) and total lesion glycolysis (TLG) parameters were determined from the PET/CT study at diagnosis. After treatment with chemoradiotherapy, patient survival was assessed. The Cox regression model and the Kaplan-Meier method were used to analyse prognostic factors and survival curves.

RESULTS

Median follow-up was 50.4 months, with 39 recurrences-progressions and 39 deaths. In the univariate analysis, metabolic parameters, except SUVmax, were predictive factors for all three survivals and the two blood parameters were predictive for OS and EFS. TLG was the only predictive factor in the multivariate analysis. The three survival curves were significantly different for the metabolic parameters and the OS curve for the N/L ratio. Correlations were seen between N/L ratio, MTV and TLG. No correlations were demonstrated between P/L ratio and metabolic parameters.

CONCLUSION

The use of haematological and metabolic markers would allow to identify patients with a high risk of recurrences and por survival and to individualise treatment by applying more aggressive therapies.

The added values of 18F-FDG PET/CT in differentiating cancer recurrence and osteoradionecrosis of mandible in patients with treated oral squamous cell carcinoma

BACKGROUND

Osteoradionecrosis (ORN) of the jaw requires a differential diagnosis to exclude cancer recurrence. Here, we sought to develop a scoring system comprising ¹⁸F-FDG PET/CT parameters for distinguishing between the two conditions in patients with oral squamous cell carcinoma (OSCC).

METHODS

The study consisted of 103 OSCC patients with suspected ORN of the jaw. All participants underwent ¹⁸F-FDG PET/CT imaging within 6 months of diagnostic histopathology. Following extraction of PET parameters, we identified clinical and imaging predictors of mandibular recurrence-free survival (MRFS) using receiver operating characteristic curve analysis and multivariate Cox regression models.

RESULTS

The results of histopathology revealed mandibular cancer recurrence in 24 patients (23.3%). Multivariate Cox regression analyses identified an age at diagnosis ≤ 52 years (P = 0.013), a location of the SUVmax voxel with soft tissue predominance (P = 0.019), and mandibular total lesion glycolysis (TLG) > 62.68 g (P < 0.001) as independent risk factors for MRFS. A scoring system was devised with scores from 0 (no risk factor) to 3 (presence of all three risk factors). High-risk patients with a score of 2-3 compared with score of 0-1 had a significantly higher likelihood of mandibular cancer recurrence (hazard ratio: 32.50, 95% confidence interval: 8.51-124.18, P < 0.001). The scoring system had a sensitivity of 87.50%, a specificity of 82.28%, and an accuracy of 83.50% for identifying mandibular cancer recurrence.

CONCLUSIONS

The scoring system of our study is clinically useful for identifying mandibular cancer recurrence in patients with suspected ORN of the jaw.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Combination of Circulating Cell-Free DNA and Positron Emission Tomography to Distinguish Non-Small Cell Lung Cancer from Tuberculosis

OBJECTIVE

Non-small cell lung cancer (NSCLC) holds high metabolic tumor burden and circulating cell-free DNA (cfDNA) levels, and the relationship between metabolic tumor burden and cfDNA in NSCLC and the underlying mechanism of their interaction therein remain poorly characterized. Our aim was to evaluate the clinical value of cfDNA and metabolic tumor burden by positron emission tomography-computed tomography (PET/CT) for NSCLC differential diagnosis from tuberculosis in patients with solitary pulmonary nodules.

METHODS

Metabolic tumor burden values in humans (subjects with NSCLC, subjects with tuberculosis, and healthy control subjects) and relevant mouse models were detected by preoperative 18F-fluorodeoxyglucose PET (18F-FDG PET/CT) and [3H]-2-deoxy-DG uptake, respectively. The cfDNA levels were detected by quantifying serum cfDNA fragments from the ALU (115 bp) gene using reverse transcription-polymerase chain reaction. RNA sequence was performed to determine the underlying target genes and knocked down or inhibited the target genes in vivo and in vitro to determine the mechanism therein.

RESULTS

Metabolic tumor burden correlated with serum cfDNA levels in NSCLC subjects but not in tuberculosis subjects or healthy controls. Mouse models showed a similar phenomenon. In addition, the RNA sequence showed that glucose transporter 1 (GLU1), factor-related apoptosis ligand (FasL), caspase 8, and caspase 3 were significantly increased in NSCLC mouse tumors compared with those in tuberculosis mouse masses. Inhibiting the metabolic tumor burden by blocking or knocking down GLU1 markedly reduced the expression of FasL, the phosphorylation of caspase 8/caspase 3, and serum cfDNA levels/apoptosis percentage in vivo and in vitro. Furthermore, the use of a combination of cfDNA and metabolic tumor burden allowed better ability to distinguish NSCLC subjects from those with tuberculosis or healthy controls than either method used alone.

CONCLUSION

Metabolic tumor burden promotes the formation of circulating cfDNA through GLU1-mediated apoptosis in NSCLC, and the combination of cfDNA and metabolic tumor burden could be valuable for distinguishing NSCLC from tuberculosis.

Relationship between Systemic Inflammatory Markers, GLUT1 Expression, and Maximum 18F-Fluorodeoxyglucose Uptake in Non-Small Cell Lung Carcinoma and Their Prognostic Significance

BACKGROUND

Factors involved in inflammation and cancer interact in various ways with each other, and biomarkers of systemic inflammation may have a prognostic value in cancer. Glucose transporter 1 (GLUT1) plays a pivotal role in glucose transport and metabolism and it is aberrantly expressed in various cancer types. We evaluated the differential expression of GLUT1, along with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in non-small-cell lung cancer (NSCLC), and then analyzed their prognostic significance.

METHODS

A total of 163 patients with resectable NSCLC were included in this study. Tumor sections were immunohistochemically stained for GLUT1 and GLUT3. Maximum standardized uptake value (SUV_max) was measured by preoperative FDG-PET, and neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR) were derived from pretreatment blood count.

RESULTS

GLUT1 and GLUT3 was positively expressed in 74.8% and 6.1% of the NSCLC tissues, respectively. GLUT1 expression was significantly correlated with squamous cell carcinoma histology, poor differentiation, high pathologic stage, old age, male, smoking, and high SUV_max (>7) (all p < 0.05). The squamous cell carcinoma and smoker group also showed significantly higher SUV_max (both p < 0.001). Systemic inflammation markers, including NLR, PLR, and LMR, were positively correlated with high SUV_max (all p < 0.05). High GLUT1 expression, high SUV_max, high NLR, and low LMR, were significantly associated with poor overall survival in patients with NSCLC. However, in the multivariate survival analysis, LMR was an independent prognostic factor overall (HR 1.86, 95% CI 1.05-3.3) and for the stage I/II cohort (HR 2.3, 95% CI 1.24-4.3) (all p < 0.05).

CONCLUSIONS

Systemic inflammatory markers-NLR, PLR, and LMR are strongly correlated with the SUV_max and are indicators of aggressive tumor behavior. Specifically, LMR is a promising prognostic biomarker in NSCLC patients.

Metabolic parameters of [18F]FDG PET-CT before and after radiotherapy may predict survival and recurrence in cervical cancer

BACKGROUND

Cervical cancer is the fourth most common female malignancy. [¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is routinely performed in patients with locally advanced cervical cancer for staging and treatment response evaluation. With this retrospective, observational cohort study, we wanted to investigate the prognostic value of the maximum standardised uptake value (SUVmax) and the volumetric parameters of metabolic tumour volume (MTV) and total lesion glycolysis (TLG) before and after treatment in women with cervical cancer, with overall survival (OS) and recurrence as outcome measures.

METHODS

Women with cervical cancer referred for curative radiotherapy and who underwent two PET-CT scans (before treatment and approximately 7 months post-treatment) were included. SUVmax, MTV and TLG were measured at baseline and post-treatment on the primary tumour, pelvic and distant lymph node metastases, distant organ metastases, and on total tumour burden. The PET parameters were associated with OS by Cox regression and recurrence by multivariable logistic regression. Kaplan-Meier curves and C-index were used to visualise the prognostic potential of the different measures.

RESULTS

A total of 133 patients were included. At the primary tumour level and on total tumour burden, age- and clinical-stage adjusted analyses showed a significant association between PET parameters and OS and recurrence when measured post-treatment. At baseline (pre-treatment), MTV and TLG were associated with OS and recurrence, whereas SUVmax was not. C-index from adjusted Cox models on total tumour burden showed higher values for the post-treatment PET compared to baseline. Kaplan-Meier curves demonstrated a greater prognostic potential for MTV and TLG compared to SUVmax, both at baseline and post-treatment.

CONCLUSIONS

The FDG PET-CT-derived parameters SUVmax, MTV, and TLG measured post-treatment can predict OS and recurrence in cervical cancer. Parameters measured before treatment had overall lower prognostic potential, and only MTV and TLG showed significant association to OS and recurrence.

18F-FDG PET/CT-Based Prognostic Survival Model After Surgery for Head and Neck Cancer

The aims of this multicenter study were to identify clinical and preoperative PET/CT parameters predicting overall survival (OS) and distant metastasis-free survival (DMFS) in a cohort of head and neck squamous cell carcinoma patients treated with surgery, to generate a prognostic model of OS and DMFS, and to validate this prognostic model with an independent cohort. Methods: A total of 382 consecutive patients with head and neck squamous cell carcinoma, divided into training (n = 318) and validation (n = 64) cohorts, were retrospectively included. The following PET/CT parameters were analyzed: clinical parameters, SUV_max, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis, and distance parameters for the primary tumor and lymph nodes defined by 2 segmentation methods (relative SUV_max threshold and absolute SUV threshold). Cox analyses were performed for OS and DMFS in the training cohort. The concordance index (c-index) was used to identify highly prognostic parameters. These prognostic parameters were externally tested in the validation cohort. Results: In multivariable analysis, the significant parameters for OS were T stage and nodal MTV, with a c-index of 0.64 (P < 0.001). For DMFS, the significant parameters were T stage, nodal MTV, and maximal tumor-node distance, with a c-index of 0.76 (P < 0.001). These combinations of parameters were externally validated, with c-indices of 0.63 (P < 0.001) and 0.71 (P < 0.001) for OS and DMFS, respectively. Conclusion: The nodal MTV associated with the maximal tumor-node distance was significantly correlated with the risk of DMFS. Moreover, this parameter, in addition to clinical parameters, was associated with a higher risk of death. These prognostic factors may be used to tailor individualized treatment.

Utility and limitations of metabolic parameters in head and neck cancer: finding a practical segmentation method

PURPOSE

Although metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have shown good prognostic value in head and neck cancer (HNC), there are still many issues to resolve before their potential application in standard clinical practice. The purpose of this study was to compare the discrimination ability of two relevant segmentation methods in HNC and to evaluate the potential benefit of adding lymph nodes' metabolism (LNM) to the measurements.

METHODS

We retrospectively analyzed a recently published database of 62 patients with HNC treated with chemoradiotherapy. MTV and TLG were measured using an absolute threshold of SUV2.5. Comparison analysis with previously published background-level threshold (BLT) results was done through Concordance index (C-index) in eight prognostic models.

RESULTS

BLT obtained better C-index values in five out of the eight models. The addition of LNM improved C-index values in six of the prognostic models.

CONCLUSION

We found a potential benefit in adding LNM to the main tumor measurements, as well as in using a BLT for MTV segmentation compared to the most commonly used SUV2.5 threshold. Despite its limitations, this study suggests a practical and simple manner to use these parameters in standard clinical practice, aiming to help elaborate a general consensus.

18F-FDG-PET/CT can be used to predict distant metastasis in hypopharyngeal squamous cell carcinoma

Background

Hypopharyngeal squamous cell carcinoma (HPSCC) has a high rate of distant metastasis, resulting in poor prognosis. The role of the maximum standardized uptake value (SUVmax), which was assessed via pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG-PET), and computed tomography (CT) was examined, for predicting distant metastasis and survival.

Methods

This study included 121 patients who underwent pretreatment FDG-PET/CT scanning and subsequent treatment for HPSCC. The SUVmax was measured via FDG-PET/CT. A receiver operating characteristic (ROC) curve analysis was used to determine whether the SUVmax was a predictor of distant metastasis and to select the best cutoff value. Univariate and multivariate Cox hazard regression analyses were used in identifying associations between the SUVmax and other clinicopathological factors with distant metastasis-free survival.

Results

Distant metastases were identified in 33 patients during the median follow-up of 24 months after treatment. The ROC curve analysis determined that SUVmax was predictive of distant metastasis and identified a SUVmax of 13.9 as the best potential cutoff value. The univariate analysis showed that T and N classification, clinical stage, and SUVmax were significantly related to distant metastasis. However, in multivariate analysis, an SUVmax ≥ 13.9 was the only independent predictor of distant metastasis. Patients with high SUVmax values displayed significantly shorter distant metastasis-free survival and overall survival.

Conclusions

SUVmax determined via pretreatment FDG-PET/CT is useful for predicting distant metastasis, distant metastasis-free survival, and overall survival in patients with HPSCC.

Graphical Abstract

Prognostic value of 11C-methionine volume-based PET parameters in IDH wild type glioblastoma

Purpose

¹¹C-Methionine (¹¹C-MET) PET prognostication of isocitrate dehydrogenase (IDH) wild type glioblastomas is inadequate as conventional parameters such as standardized uptake value (SUV) do not adequately reflect tumor heterogeneity. We retrospectively evaluated whether volume-based parameters such as metabolic tumor volume (MTV) and total lesion methionine metabolism (TLMM) outperformed SUV for survival correlation in patients with IDH wild type glioblastomas.

Methods

Thirteen IDH wild type glioblastoma patients underwent preoperative ¹¹C-MET PET. Both SUV-based parameters and volume-based parameters were calculated for each lesion. Kaplan-Meier curves with log-rank testing and Cox regression analysis were used for correlation between PET parameters and overall survival.

Results

Median overall survival for the entire cohort was 393 days. MTV (HR 1.136, p = 0.007) and TLMM (HR 1.022, p = 0.030) were inversely correlated with overall survival. SUV-based ¹¹C-MET PET parameters did not show a correlation with survival. In a paired analysis with other clinical parameters including age and radiotherapy dose, MTV and TLMM were found to be independent factors.

Conclusions

MTV and TLMM, and not SUV, significantly correlate with overall survival in patients with IDH wild type glioblastomas. The incorporation of volume-based ¹¹C-MET PET parameters may lead to a better outcome prediction for this heterogeneous patient population.

Metabolic Tumour Volume as a Predictor of Survival for Sinonasal Tract Squamous Cell Carcinoma

Background: High uptake of F18-fluorodeoxyglucose parameters for glucose metabolism is related to shorter survival in sinonasal tract cancer with various histological classifications. We investigated whether F18-fluorodeoxyglucose uptake parameters are associated with survival outcomes for patients with only squamous cell carcinoma (SCC) in the sinonasal tract that are treated either with surgery or nonsurgery. Methods: We retrospectively observed F18-fluorodeoxyglucose uptake parameters on positron emission tomography with computed tomography for the primary tumour of SCC in 39 patients. Log-rank test or a Cox regression model with 95% confidence interval (95%CI) and hazard ratio (HR) were used for monovariable or multivariable analysis, respectively. We determined cut-off values of the F18-fluorodeoxyglucose uptake parameters using the lowest p value for monovariable sinonasal tract cancer-specific survival analysis. Results: Monovariable analysis showed that patients with metabolic tumour volume (MTV) ≥ 21.8 had a shorter cancer-specific, disease-free and local recurrence-free survival than those with MTV < 21.8. After adjusting for age, gender, clinical stage and treatment group in the multivariable analysis, MTV (≥21.8/<21.8) was related to shorter cancer-specific (HR: 3.69, 95%CI: 1.17–12.0), disease-free (HR: 3.38, 95%CI: 1.19–9.71) and local recurrence-free (HR: 5.42, 95%CI: 1.59–20.3) survivals. Conclusions: MTV as advances in diagnostics of sinonasal tract SCC is a predictor.

Application of machine learning in oral and maxillofacial surgery

Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.

Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge's task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (H&N) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in H&N cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs.

Prognostic Value of 18F-FDG PET/CT Volume-Based Metabolic Parameters in Patients with Node-Negative Stage II Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is a major cancer prevalent in Asian males. Pretreatment tumor burden can be prognostic for ESCC. We studied the prognostic value of metabolic parameters of 2-deoxy-2-[¹⁸F] fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and the serum squamous cell carcinoma antigen (SCC-Ag) level in node-negative stage II ESCC patients. Eighteen males underwent staging evaluation were included. The volume-based metabolic parameters derived from ¹⁸F-FDG PET/CT, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were obtained using the PET Volume Computer Assisted Reading application. The Spearman correlation coefficients were calculated to assess the relationship between metabolic parameters and pretreatment serum SCC-Ag levels. Based on the 5-year follow-up, patients were sub-divided into the demised and the stable groups. Potential prognostic value was assessed by independent t-test and the Mann–Whitney U test. The association of overall survival was assessed using univariate and multivariate Cox regression analyses. The demised group showed significant higher values in serum SCC-Ag, as well as in MTV and TLG, but not SUVmax and SUVmean. The SUVmax, MTV, TLG, and serum SCC-Ag showed significant association with overall survival. Our findings suggest potential usage of pretreatment volume-based metabolic parameters of ¹⁸F-FDG PET/CT and serum SCC-Ag as prognostic factors for node-negative stage II ESCC patients.

Total lesion glycolysis as a predictor of clinical T3-4a laryngeal cancer with laryngectomy or nonlaryngectomy

The purpose of the present study is to investigate whether the 18F-fluorodeoxyglucose (18F-FDG) uptake parameter is related to survival outcomes for patients with clinical T3-T4a laryngeal cancer with various definitive treatments including total laryngectomy (TL). Parameters of 18F-FDG uptake in the primary tumors of 46 cases which were assessed by positron emission tomography with computed tomography were enrolled in the present observation study. Monovariate or multivariate survival analyses were performed with log-rank test or Cox regression model, with the hazard ratio (HR) and 95% confidence interval (CI), respectively. Cutoff values of the 18F-FDG uptake parameters were determined by the lowest P-value for monovariate overall survival. In the monovariate analysis, both metabolic tumor volume ≥13.1 and total lesion glycolysis (TLG) ≥46.5 were significantly associated with shorter overall survival, and TLG ≥46.5 was also related to a reduction in distant metastasis-free survival. In the multivariate analysis adjusting for clinical T classification (cT4/cT3) and treatment group (TL/non-TL), TLG (≥46.5/<46.5) was associated with both poorer overall (HR: 3.16, 95% CI: 1.10-9.49) and distant metastasis-free (HR: 8.91, 95% CI: 1.93-62.6) survival. In conclusion, TLG is a predictor for survival in laryngeal cancer.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

PET Imaging for Head and Neck Cancers

Head and neck cancers are commonly encountered cancers in clinical practice in the United States. Fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) PET/CT has been clinically applied in staging, occult primary tumor detection, treatment planning, response assessment, follow-up, recurrent disease detection, and prognosis prediction in these patients. Alternative PET tracers remain investigational and can provide additional valuable information such as radioresistant tumor hypoxia. The recent introduction of ¹⁸F-FDG PET/MR imaging has provided the advantage of combining the superior soft tissue resolution of MR imaging with the functional information provided by ¹⁸F-FDG PET. This article is a concise review of recent advances in PET imaging in head and neck cancer.

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

PURPOSE

This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC).

METHODS

Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation.

RESULTS

The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment.

CONCLUSION

QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma

AIM

To investigate the value of machine learning-based multiparametric analysis using 2-[¹⁸F]-fluoro-2-deoxy-d-glucose positron-emission tomography (FDG-PET) images to predict treatment outcome in patients with oral cavity squamous cell carcinoma (OCSCC).

MATERIALS AND METHODS

Ninety-nine patients with OCSCC who received pretreatment integrated FDG-PET/computed tomography (CT) were included. They were divided into the training (66 patients) and validation (33 patients) cohorts. The diagnosis of local control or local failure was obtained from patient's medical records. Conventional FDG-PET parameters, including the maximum and mean standardised uptake values (SUVmax and SUVmean), metabolic tumour volume (MTV), and total lesion glycolysis (TLG), quantitative tumour morphological parameters, intratumoural histogram, and texture parameters, as well as T-stage and clinical stage, were evaluated by a machine learning analysis. The diagnostic ability of T-stage, clinical stage, and conventional FDG-PET parameters (SUVmax, SUVmean, MTV, and TLG) was also assessed separately.

RESULTS

In support-vector machine analysis of the training dataset, the final selected parameters were T-stage, SUVmax, TLG, morphological irregularity, entropy, and run-length non-uniformity. In the validation dataset, the diagnostic performance of the created algorithm was as follows: sensitivity 0.82, specificity 0.7, positive predictive value 0.86, negative predictive value 0.64, and accuracy 0.79. In a univariate analysis using conventional FDG-PET parameters, T-stage and clinical stage, diagnostic accuracy of each variable was revealed as follows: 0.61 in T-stage, 0.61 in clinical stage, 0.64 in SUVmax, 0.61 in SUVmean, 0.64 in MTV, and 0.7 in TLG.

CONCLUSION

A machine-learning-based approach to analysing FDG-PET images by multiparametric analysis might help predict local control or failure in patients with OCSCC.

Correlation of [18F] FDG-PET/CT with dosimetry data: recurrence pattern after radiotherapy for head and neck carcinoma

OBJECTIVE

To analyze the pattern of failure in relation to pre-treatment [18F] FDG-PET/CT uptake in head and neck squamous cell carcinoma (HNSCC) patients treated with definitive radio-chemotherapy (RT-CHT).

METHODS AND MATERIALS

From 2012 to 2016, 87 HNSCC patients treated with definitive RT-CHT, with intensity modulated radiation therapy with simultaneous integrated boost, underwent pre-treatment [18F] FDG-PET/CT (PETpre), and MRI/CT for radiotherapy (RT) planning purposes. Patients with local recurrence, received [18F] FDG-PET/CT, (PETrec) at the time of the discovery of recurrence. In these patients, the metabolic target volume (MTV), MTVpre and MTVrec were segmented on PET images by means of an adaptive thresholding algorithm. The overlapping volume between MTVpre and MTVrec (MTVpre&rec) was generated and the dose coverage of MTVrec and MTVpre&rec was checked on the planning CT using the D99 and D95 dose metrics. The recurrent volume was defined as: ''In-Field (IF)'', "Marginal recurrence" or ''Out-of-Field (OF)'' if D95 was respectively equal or higher than 95%, D95 was between 95 and 20% or the D95 was less than 20% of prescribed dose.

RESULTS

We found 10/87 patients (11.5%) who had recurrence at primary site. Mean MTVpre was 12.2 cc (4.6-28.9 cc), while the mean MTVrec was 4.3 cc (1.1-12.7 cc). Two recurrences resulted 100% inside MTVpre, 4 recurrences were mostly inside (61-91%) and 4 recurrences were marginal to MTVpre (1-33%). At dosimetric analysis, five recurrences (50%) were IF, 4 (40%) marginal and one (10%) OF. The mean D99 of the overlapping volumes MTVpre&rec was 68.1 Gy (66.5-69.2 Gy), considering a prescription dose of 70 Gy to the planning target volume (PTV).

CONCLUSION

Our study shows that the recurrence may originate from the volume with the highest FDG-signal. Tumor relapse in the high-dose volume support the hypothesis that an intensification of the dose on these volumes could be further assessed to prevent local relapse.

c-MYC-directed NRF2 drives malignant progression of head and neck cancer via glucose-6-phosphate dehydrogenase and transketolase activation

Rationale: NRF2, a redox sensitive transcription factor, is up-regulated in head and neck squamous cell carcinoma (HNSCC), however, the associated impact and regulatory mechanisms remain unclear.

Methods: The protein expression of NRF2 in HNSCC specimens was examined by IHC. The regulatory effect of c-MYC on NRF2 was validated by ChIP-qPCR, RT-qPCR and western blot. The impacts of NRF2 on malignant progression of HNSCC were determined through genetic manipulation and pharmacological inhibition in vitro and in vivo. The gene-set enrichment analysis (GSEA) on expression data of cDNA microarray combined with ChIP-qPCR, RT-qPCR, western blot, transwell migration/ invasion, cell proliferation and soft agar colony formation assays were used to investigate the regulatory mechanisms of NRF2.

Results: NRF2 expression is positively correlated with malignant features of HNSCC. In addition, carcinogens, such as nicotine and arecoline, trigger c-MYC-directed NRF2 activation in HNSCC cells. NRF2 reprograms a wide range of cancer metabolic pathways and the most notable is the pentose phosphate pathway (PPP). Furthermore, glucose-6-phosphate dehydrogenase (G6PD) and transketolase (TKT) are critical downstream effectors of NRF2 that drive malignant progression of HNSCC; the coherently expressed signature NRF2/G6PD/TKT gene set is a potential prognostic biomarker for prediction of patient overall survival. Notably, G6PD- and TKT-regulated nucleotide biosynthesis is more important than redox regulation in determining malignant progression of HNSCC.

Conclusions: Carcinogens trigger c-MYC-directed NRF2 activation. Over-activation of NRF2 promotes malignant progression of HNSCC through reprogramming G6PD- and TKT-mediated nucleotide biosynthesis. Targeting NRF2-directed cellular metabolism is an effective strategy for development of novel treatments for head and neck cancer.

Quantitative FDG PET Assessment for Oncology Therapy

Simple Summary

PET enables quantitative assessment of tumour biology in vivo. Accumulation of F-18 fluorodeoxyglucose (FDG) may reflect tumour metabolic activity. Quantitative assessment of FDG uptake can be applied for treatment monitoring. Numerous studies indicated biochemical change assessed by FDG-PET as a more sensitive marker than morphological change. Those with complete metabolic response after therapy may show better prognosis. Assessment of metabolic change may be performed using absolute FDG uptake or metabolic tumour volume. More recently, radiomics approaches have been applied to FDG PET. Texture analysis quantifies intratumoral heterogeneity in a voxel-by-voxel basis. Combined with various machine learning techniques, these new quantitative parameters hold a promise for assessing tissue characterization and predicting treatment effect, and could also be used for future prognosis of various tumours.

Abstract

Positron emission tomography (PET) has unique characteristics for quantitative assessment of tumour biology in vivo. Accumulation of F-18 fluorodeoxyglucose (FDG) may reflect tumour characteristics based on its metabolic activity. Quantitative assessment of FDG uptake can often be applied for treatment monitoring after chemotherapy or chemoradiotherapy. Numerous studies indicated biochemical change assessed by FDG PET as a more sensitive marker than morphological change estimated by CT or MRI. In addition, those with complete metabolic response after therapy may show better disease-free survival and overall survival than those with other responses. Assessment of metabolic change may be performed using absolute FDG uptake in the tumour (standardized uptake value: SUV). In addition, volumetric parameters such as metabolic tumour volume (MTV) have been introduced for quantitative assessment of FDG uptake in tumour. More recently, radiomics approaches that focus on image-based precision medicine have been applied to FDG PET, as well as other radiological imaging. Among these, texture analysis extracts intratumoral heterogeneity on a voxel-by-voxel basis. Combined with various machine learning techniques, these new quantitative parameters hold a promise for assessing tissue characterization and predicting treatment effect, and could also be used for future prognosis of various tumours, although multicentre clinical trials are needed before application in clinical settings.

Prediction of Response in Head and Neck Tumor: Focus on Main Hot Topics in Research

Radiation therapy is a cornerstone in the treatment of head and neck cancer patients; actually, their management is based on clinical and radiological staging with all patients at the same stage treated in the same way. Recently the increasing knowledge in molecular characterization of head and neck cancer opens the way for a more tailored treatment. Patient outcomes could be improved by a personalized radiotherapy beyond technological and anatomical precision. Several tumor markers are under evaluation to understand their possible prognostic or predictive value. In this paper we discuss those markers specific for evaluate response to radiation therapy in head and neck cancer for a shift toward a biological personalization of radiotherapy.

Total lesion glycolysis in oral squamous cell carcinoma as a biomarker derived from pre-operative FDG PET/CT outperforms established prognostic factors in a newly developed multivariate prediction model

PURPOSE

Retrospective study to investigate the impact of image derived biomarkers from [¹⁸F]FDG PET/CT prior to surgical resection in patients with initial diagnosis of oral squamous cell carcinoma (OSCC), namely SUV_max, SUV_mean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary tumor to predict overall survival (OS).

MATERIALS AND METHODS

127 subsequent patients with biopsy-proven OSCC were included who underwent [¹⁸F]FDG PET/CT before surgery. SUV_max, SUV_mean, MTV and TLG of the primary tumor were measured. OS was estimated according to Kaplan-Meier and compared between median-splitted groups by the log-rank test. Prognostic parameters were analyzed by uni-/multivariate Cox-regression.

RESULTS

During follow-up 52 (41%) of the patients died. Median OS was longer for patients with lower MTV or lower TLG. SUV_max and SUV_mean failed to be significant predictors for OS. Univariate Cox-regression identified MTV, TLG, lymph node status and UICC stage as prognostic factors. By multivariate Cox-regression MTV and TLG turned out to be independent prognostic factors for OS.

CONCLUSIONS

The pre-therapeutic [¹⁸F]FDG PET/CT parameters MTV and TLG in the primary tumor are prognostic for OS of patients with an initial diagnosis of OSCC. TLG is the strongest independent prognostic factor for OS and outperforms established prognostic parameters in OSCC.