Ratio of Mediastinal Lymph Node SUV to Primary Tumor SUV in 18F-FDG PET/CT for Nodal Staging in Non-Small-Cell Lung Cancer

High-Quality Samples for Next-Generation Sequencing and PD-L1 Assessment in Non-Small Cell Lung Cancer: The Role of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration

Background/Objectives: Recent advances in the treatment of non-small cell lung cancer (NSCLC) have shifted from conventional chemotherapy to targeted therapies aimed at specific genetic mutations, particularly in the adenocarcinoma subtype. These therapies have improved overall survival and quality of life. However, some patients still face barriers to accessing these treatments due to challenges in diagnosing advanced-stage NSCLC. Limited tumor cellularity in small biopsies and cytological samples hinders the ability to perform further molecular analyses. Additionally, the increasing number of genetic alterations requiring testing complicates the diagnostic process. To overcome this challenge, we propose combining endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) with next-generation sequencing (NGS) and immunohistochemistry for PD-L1. Methods: A total of 120 EBUS-TBNA samples were consecutively collected during the first year of integrating NGS at a reference hospital in Castilla y León, Spain. Depending on the histology and patient characteristics, a total of 67 NGS analyses and 116 PD-L1 determinations were performed. Results: The cytological sample obtained in these cases successfully achieved the triple objective proposed by the NCCN for lung cancer (diagnosis, staging, and molecular analysis in a single procedure) in 97% of instances. Conclusions: Our study highlights the effectiveness of EBUS-TBNA as a comprehensive, cost-effective, and safe diagnostic tool for NSCLC, successfully achieving the triple objective of diagnosis, staging, and molecular analysis in 97% of cases. The procedure consistently provided high-quality samples for NGS and PD-L1 testing, with minimal complications, reinforcing its value as a reliable approach for optimizing personalized treatment strategies.

MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer

Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgent for improving therapeutic planning and patients' risk stratification in clinical practice. This study collected clinicopathological variables from the pulmonary nodule and lung cancer database of the First Affiliated Hospital of Chongqing Medical University, where patients with early-stage (cT1) solitary lung cancer were evaluated from 2018.11 to 2022.10. The random forest model and Shapley Additive Explanations (SHAP) were used to investigate the importance of clinical features in the feature selection part. Random Forest, Gradient Boosting, and AdaBoost classifiers were applied to build the final model, and the predictive discrimination of each model was compared based on the receiver operating characteristics (ROC) curve and precision and recall curve. With the evaluation of feature importance, 9 features were used to construct the prediction model finally. The Random Forest model yielded an average precision of 0.93 with an area under the curve (AUC) of 0.92 (95% CI: 0.88-0.94) compared with the Gradient Boosting and AdaBoost classifiers in the internal validation dataset, yielding an average precision of 0.87 and 0.91 with AUCs of 0.87 (95% CI: 0.84-0.93) and 0.90 (95% CI: 0.86-0.92), respectively. In addition, the Random Forest classifier performed best in 5 other 5 diagnostic indices. Furthermore, we embedded this model in a web application called MoLPre (https://molpre.cqmu.edu.cn/), a user-friendly tool assisting in the metastasis prediction of cT1 solid lung cancer.

Mediastinal staging lymph node probability map in non-small cell lung cancer

BACKGROUND

Mediastinal lymph node (LN) staging is routinely performed using PET/CT and EBUS-TBNA. Promising predictive algorithms for lymph nodes have been reported for each technique, both individually and in combination. This study aims to develop a predictive algorithm that combines EBUS, PET/CT and clinical data to provide a probability of malignancy.

METHODS

A retrospective study was conducted on consecutive patients with non-small cell lung carcinoma staged using PET/CT and EBUS-TBNA. Lymph nodes were identified by level (N1, N2, and N3) and anatomical region (AR) (subcarinal, paratracheal, and hilar). A Standardized Uptake Value (SUV) was determined for each sampled LN. The ultrasound features collected included diameter in the short axis (DSA), morphology, border, echogenicity and the presence of the vascular hilum. A robust logistic regression model was used to construct an algorithm to estimate the probability of malignancy of the lymph node.

RESULTS

A total of 116 patients with a mean age of 66, 93% of whom were men, were included. 358 lymph nodes were evaluated, 51% of which exhibited adenocarcinoma and 35% were squamous, while 14% were classified as non-small-cell lung carcinoma. The model estimated the probability of malignancy for each lymph node using age, DSA, SUVmax, and AR. The Area Under the ROC curve, was 0.89. A user-friendly application was also developed ( https://ubidi.shinyapps.io/lymma/ .) CONCLUSIONS: The integration of patient clinical characteristics, EBUS features, and PET/CT findings may generate a pre-sampling malignancy probability map for each lymph node. The model requires prospective and external validation.

Analysis of lymph node metastasis based on consolidation tumor ratio and maximum standardized uptake value in clinical stage IA non-small cell lung cancer

Background

Sublobar resection has been established as an acceptable treatment for early-stage non-small cell lung cancer (NSCLC). As a result, preoperative prediction of lymph node (LN) metastasis is becoming an important factor in determining surgical strategy. This study aimed to investigate the predictive accuracy of the consolidation tumor ratio (CTR) and the maximum standardized uptake value (maxSUV) of the primary tumor for LN metastasis in patients with clinical stage IA NSCLC.

Methods

We performed a retrospective analysis using data from 1,338 patients with clinical stage IA NSCLC who underwent surgery between 2011 and 2019. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to identify the optimal maxSUV and CTR for predicting LN metastasis. Multivariate logistic regression analysis was performed to identify independent predictors of LN metastasis. Survival analyses were performed using Cox proportional hazards models to identify prognostic factors for death and recurrence.

Results

Among the 896 patients who underwent lobectomy with systematic LN dissection, 9.8% (88 patients) were found to have LN metastasis. The ROC curve for CTR revealed an AUC of 0.689 [95% confidence interval (CI): 0.646-0.732, P<0.001], while the ROC curve for maxSUV yielded an AUC of 0.748 (95% CI: 0.705-0.791, P<0.001) for predicting LN metastasis. In pure solid mass (CTR =1) with maxSUV exceeding 5.0, LN metastasis was observed in 13.8% of tumor 0-2 cm and 25.7% of tumor 2.1-3 cm. Multivariate analysis identified CTR >0.5 (HR =1.741, 95% CI: 1.122-2.701, P=0.01) and maxSUV >5.0 (HR =2.004, 95% CI: 1.421-2.825, P<0.001) as independent prognostic factors for disease-free survival.

Conclusions

In clinical stage IA NSCLC, LN metastasis can be predicted using CTR and maxSUV of the primary mass. It is crucial not to underestimate the rate of LN metastasis when determining the surgical extent.

Diagnostic value of the standardised uptake value (SUV) ratio of mediastinal lymph node to primary tumour in lung cancer

Purpose

This study aimed to determine whether the mediastinal lymph node/tumour ratio (NTR) of the standardised uptake value (SUV) predicts N2 involvement more accurately than node SUV in patients with non-small cell lung cancer (NSCLC).

Material and methods

We retrospectively analysed consecutive patients with lung cancer at clinical stages I-IVA. All patients underwent positron emission tomography-computed tomography (PET-CT), followed by mediastinal staging using endobronchial ultrasound and endoscopic ultrasound imaging, and curative-intent lung resection with systematic lymph node dissection. Pathological examination of the surgical specimen was performed for confirmation.

Results

The data from 774 patients were analysed. There was a significant correlation between the risk of false-negative PET results for N2 disease and both the SUV of the mediastinal nodes (p = 0.012) and NTR (p = 0.030). The NTR outperformed node SUV in predictive ability; the Akaike information criterion was 307.268 for NTR compared to 308.498 for node SUV. Three factors were significantly associated with the positive predictive value of PET: patient age (p = 0.021), female sex (p = 0.012), and adenocarcinoma histology (p = 0.036). There were no significant correlations between PET sensitivity, specificity, and negative predictive value (NPV), and age, sex, body mass index (BMI), tumour grade, lobar location, or histological type.

Conclusions

The NTR may be a useful tool for excluding N2 disease in NSCLC. PET sensitivity and NPV for detecting N2 disease are not influenced by age, sex, BMI, tumour grade, lobar location, or histological type.

Comparison of the diagnostic value of [68 Ga]Ga-FAPI-04 PET/MR and [18F]FDG PET/CT in patients with T stage ≤ 2a2 uterine cervical cancer: a prospective study

PURPOSE

To compare the diagnostic value of [68 Ga]Ga-FAPI-04 PET/MR and [18F]FDG PET/CT in patients with T stage ≤ 2a2 uterine cervical cancer patients.

METHODS

Patients pathologically diagnosed with cervical cancer and with a T stage ≤ T2a2 were prospectively enrolled. All patients underwent whole-body [68 Ga]Ga-FAPI-04 PET/MR and [18F]FDG PET/CT within 2 weeks, and surgical treatment was performed within 10 days after PET.

RESULTS

Twenty-five patients were enrolled. Twenty patients underwent radical hysterectomy, among which all of them underwent pelvic lymphadenectomy, and 10 patients underwent para-aortic lymphadenectomy. Three patients received merely laparoscopic lymphadenectomy without hysterectomy. Two patients with both [18F]FDG and [68 Ga]Ga-FAPI-04 lymph node high metabolism were staged as FIGO IIIC1r, and concurrent chemoradiation therapy (CCRT) was performed. [18F]FDG and [68 Ga]Ga-FAPI-04 had equivalent detection ability on primary tumors, with a positive detection rate of 96.0%. The accuracy of T staging using [18F]FDG and [68 Ga]Ga-FAPI-04 was relatively 50% and 55.0%. Elevated and underrated staging was due to misdiagnosis of either vaginal infiltration or tumor size. In terms of lymph node metastasis detection, the specificity of [68 Ga]Ga-FAPI-04 was 100% (95% CI, 84.6% ~ 100.0%), which was significantly higher than [18F]FDG (59.1% (95% CI, 36.4% ~ 79.3%)) (p = 0.004).

CONCLUSION

[68 Ga]Ga-FAPI-04 PET/MR and [18F]FDG PET/CT demonstrated an equivalent detection ability on cervical cancer primary tumors. However, [68 Ga]Ga-FAPI-04 PET/MR's diagnostic value in lymph node metastasis was significantly higher than [18F]FDG PET/CT. [68 Ga]Ga-FAPI-04 PET/MR has the potential for more accurate treatment planning, thus clarifying fertility preservation indications for early-stage young patients.

Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer

The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 after surgery. Regional LNs were scored with a morphological Likert scale (NODE-SCORE) and divided into two groups: low likelihood (LLM: 0-2 points) and high likelihood (HLM: 3-7 points) of malignancy. The T-test and the Mann-Whitney test were used to compare 107 radiomic features extracted from the two groups. Radiomic features were also extracted from primary lesions (PLs), and the receiver operating characteristic (ROC) was used to test a LN/PL ratio when assessing the LN's status identified with radiomics and with the NODE-SCORE. An amount of 337 LNs were divided into 167 with LLM and 170 with HLM. Radiomics showed 15/107 features, with a significant difference (p < 0.02) between the two groups. The comparison of selected features between 81 PLs and the corresponding LNs showed all significant differences (p < 0.0001). According to the LN/PL ratio, the selected features recognized a higher number of LNs than the NODE-SCORE (p < 0.001). On validation of the cohort of 20 patients (10 pN0, 10 pN2), significant ROC curves were obtained for LN/PL busyness (AUC = 0.91; 0.69-0.99; 95% C.I.; and p < 0.001) and for LN/PL dependence entropy (AUC = 0.76; 0.52-0.92; 95% C.I.; and p = 0.03). The radiomics ratio between CC and LNs is more accurate for noninvasively discriminating benign LNs compared to CT morphological features.

Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature

Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([¹⁸F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [¹⁸F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.

Prospective Evaluation of Quantitative F-18-FDG-PET/CT for Pre-Operative Thoracic Lymph Node Staging in Patients with Lung Cancer as a Target for Computer-Aided Diagnosis

Purpose: Pre-operative assessment of thoracic lymphonodal (LN) involvement in patients with lung cancer (LC) is crucial when choosing the treatment modality. Visual assessment of F-18-FDG-PET/CT (PET/CT) is well established, however, there is still a need for prospective quantitative data to differentiate benign from malignant lesions which would simplify staging and guide the further implementation of computer-aided diagnosis (CAD). Methods: In this prospective study, 37 patients with confirmed lung cancer (m/f = 24/13; age: 70 [52–83] years) were analyzed. All patients underwent PET/CT and quantitative data (standardized uptake values) were obtained. Histological results were available for 101 thoracic lymph nodes. Quantitative data were matched to determine cut-off values for delineation between benign vs. malignant lymph nodes. Furthermore, a scoring system derived from these cut-off values was established. Statistical analyses were performed through ROC analysis. Results: Quantitative analysis revealed the optimal cut-off values (p < 0.01) for the differentiation between benign and malignant thoracic lymph nodes in patients suffering from lung cancer. The respective areas under the curve (AUC) ranged from 0.86 to 0.94. The highest AUC for a ratio of lymph node to healthy lung tissue was 0.94. The resulting accuracy ranged from 78.2% to 89.1%. A dedicated scoring system led to an AUC of 0.93 with a negative predictive value of 95.4%. Conclusion: Quantitative analysis of F-18-FDG-PET/CT data provides reliable results for delineation between benign and malignant thoracic lymph nodes. Thus, quantitative parameters can improve diagnostic accuracy and reliability and can also facilitate the handling of the steadily increasing number of clinical examinations.

A Comparative Evaluation of Mediastinal Nodal SUVmax and Derived Ratios from 18F-FDG PET/CT Imaging to Predict Nodal Metastases in Non-Small Cell Lung Cancer

18F-FDG positron emission tomography with computed tomography (PET/CT) is a standard imaging modality for the nodal staging of non-small cell lung cancer (NSCLC). To improve the accuracy of pre-operative staging, we compare the staging accuracy of mediastinal lymph node (LN) standard uptake values (SUV) with four derived SUV ratios based on the SUV values of primary tumours (TR), the mediastinal blood pool (MR), liver (LR), and nodal size (SR). In 2015-2017, 53 patients (29 women and 24 men, mean age 67.4 years, range 53-87) receiving surgical resection have pre-operative evidence of mediastinal nodal involvement (cN2). Among these, 114 mediastinal nodes are resected and available for correlative PET/CT analysis. cN2 status accuracy is low, with only 32.5% of the cN2 cases confirmed pathologically. Using receiver operating characteristic (ROC) curve analyses, a SUVmax of N2 LN performs well in predicting the presence of N2 disease (AUC, 0.822). Based on the respective selected thresholds for each ROC curve, normalisation of LN SUVmax to that for mediastinum, liver and tumour improved sensitivities of LN SUVmax from 68% to 81.1-89.2% while maintaining acceptable specificity (68-70.1%). In conclusion, normalised SUV ratios (particularly LR) improve current pre-operative staging performance in detecting mediastinal nodal involvement.

The value of the SUV ratio between lymph node and bone marrow in predicting pelvic lymphatic metastasis of patients with locally advanced cervical cancer: an integrated PET/CT study

PURPOSE

This study aimed to evaluate the value of the standardized uptake value (SUV) ratio between lymph nodes and bone marrow (BM) measured by Fluorine-18-fluorodeoxyglucose PET and computed tomography ( 18 F-FDG PET/CT) for predicting pelvic lymph node (PLN) metastasis in patients with locally advanced cervical cancer (LACC).

MATERIALS AND METHODS

A total of 62 patients with pathological stage Ib-IVa cervical cancer who underwent 18 F-FDG PET/CT before treatment were reviewed retrospectively. We measured the metabolic and morphological parameters of lymph nodes and primary tumors, bone marrow SUV (SUVBM) and calculated the ratio of lymph nodes maximum SUV (SUVmax) to bone marrow SUV (SUVLN/BM) and the ratio of short-axis diameter to long-axis diameter (Ds/l) of lymph nodes. A receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic efficacy of each parameter.

RESULTS

There were 180 lymph nodes with pathological evidence included in the study. Our results indicated that Ds/l, SUVmax of lymph nodes (SUVLN) and SUVLN/BM were independent risk factors for PLN metastasis in LACC ( P  < 0.05), and SUVLN/BM showed the best diagnostic performance by ROC curve analysis. The SUVBM in the anemia group was significantly higher than that in the nonanemia group (3.05 vs. 2.40, P  < 0.05); furthermore, false-positive cases decreased when the SUVLN/BM was used as the diagnostic criterion instead of SUVLN, especially in the anemia group. ROC curve analysis showed that the area under the curve value of the combination of SUVLN/BM and Ds/l was 0.884 ( P  < 0.05), which was higher than Ds/l or SUVLN/BM alone.

CONCLUSIONS

SUVLN/BM could improve the ability to predicting PLN metastasis in patients with LACC, and the diagnostic efficacy of the combination of SUVLN/BM and Ds/l might be better than that of a single parameter.

The differential diagnostic value of dual-phase 18F-DCFPyL PET/CT in prostate carcinoma

OBJECTIVE

Binding of ¹⁸F-DCFPyL at prostate cancer (PC) cells increases over time. The dual-phase protocol may be helpful in separating benign lesions from malignant ones associated with prostate cancer. The purpose of this study was to retrospectively analyze the incremental diagnostic value of ¹⁸F-DCFPyL dual-time imaging in patients with prostate cancer.

METHOD

114 prostate-related malignant lesions and 43 benign lesions in 38 patients with prostate cancer were retrospectively analyzed. Maximum standardized uptake value (SUVmax) for benign and prostate-related malignant lesions were calculated at min 60 and min 120 of PET/CT imaging. In order to calculate SUV ratio, the SUVmax of left gluteus maximus was measured at the same time. The difference of SUVmax metrics and SUV ratio between malignant and benign lesions was statistically analyzed, the cut-off value of ROC curve was calculated, and the diagnostic efficacy of SUVmax index and SUV ratio at two time points was compared.

RESULTS

SUVmax metrics and SUV ratio of early and delayed imaging of PC-related malignant lesions were significantly higher than those of benign lesions (p < 0.05). In terms of individual indicators, the highest accuracy and sensitivity was in the delayed SUV ratio (89.2% and 94.7%), the best specificity was in the early SUVmax (93.0%). When the individual and combined indicators were compared together, the SUV ratio in the delay period still showed the best diagnostic sensitivity and accuracy, and the best specificity were SUVmax early and ▵SUVmax, SUVmax early and RI.

CONCLUSIONS

Uptake of ¹⁸F-DCFPyL increased over time in prostate-associated malignant lesions compared with benign tissue. For single-phase imaging, 2-hour (delayed) imaging has better diagnostic performance. However, the dual-phase imaging (1 and 2 h) are helpful in the differential diagnosis of prostate-associated malignant lesions and benign lesions.

The diagnostic value of metabolic, morphological and heterogeneous parameters of 18F-FDG PET/CT in mediastinal lymph node metastasis of non-small cell lung cancer

OBJECTIVES

To investigate the value of PET/CT metabolic, morphological and heterogeneous parameters in the diagnosis of 18F-FDG positive mediastinal lymph node metastasis in non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

A total of 156 patients with pathologically diagnosed NSCLC and underwent 18F-FDG PET/CT scans were enrolled in this study. Mediastinal lymph nodes with 18F-FDG uptake greater than the mediastinum were analyzed. The metabolic parameters of maximum and mean standardized uptake value (SUVmax, SUVmean), SUVratio (node SUVmax/mediastinum SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), morphological parameters of maximum short diameter (Dmin), CT values and metabolic heterogeneity parameter of coefficient of variation (COV) were measured. The performance of each parameter and their combinations for diagnosis of lymph node metastasis was evaluated through receiver operating characteristic (ROC) curves and binary logistic regression analysis.

RESULTS

There were 206 lymph nodes with pathological evidence included in the study, including 103 metastatic and 103 nonmetastatic nodes. The SUVmax, SUVmean, SUVratio, TLG, COV and Dmin of metastatic lymph nodes were significantly higher/greater than those in nonmetastatic ones (P < 0.05). ROC curve analysis revealed that the combination of SUVratio, Dmin and COV showed the highest diagnostic efficacy among all single and combined parameters, the area under the curve (AUC) was 0.907 (P = 0.000), these three parameters all increased the risk of lymph node metastasis, with odds ratios of 1.848, 1.293 and 1.258, respectively (all P < 0.05).

CONCLUSION

Heterogeneity parameter was helpful for the accurate distinction of mediastinal lymph node metastasis in NSCLC. The combination of the SUVratio, Dmin and COV could improve the diagnostic accuracy. Multiple-parameters analysis plays an important complementary role in the diagnosis of lymph node metastasis.

Identification of specific SUVmax ratios enhances diagnostic accuracy for staging of intrathoracic nodes in lung cancer

INTRODUCTION

Studies demonstrating limited accuracy of 'positive' and 'negative' lymph nodes on fluorodeoxyglucose (FDG) PET-CT in staging for lung cancer have led to guidelines stating mediastinal nodes enlarged on computed tomography, irrespective of FDG uptake, require endobronchial ultrasound (EBUS)-transbronchial needle aspiration (TBNA). However FDG uptake occurs on a continuous spectrum and the use of standardised uptake value (SUV)max ratios, rather than a binary classification, may have improved diagnostic accuracy.

METHODS

This was a retrospective analysis of patients with lung cancer who had PET-CT and EBUS-TBNA in 2015-2018. Results from EBUS and the SUVmax ratio of sampled lymph nodes to mediastinal blood pool (SUVmax LN/MBP) were analysed.

RESULTS

From 99 patients 102 malignant and 54 benign nodes were identified. The SUVmax range was 2.5-52 for malignant and 1.6-5.4 for benign nodes. The SUVmax LN/MBP was 1.3-23 for malignant and 0.7-2.3 for benign nodes. All nodes with SUVmax LN/MBP <1.3 were benign with 100% negative predictive value (NPV). All nodes with SUVmax LN/MBP >2.3 were malignant with 100% positive predictive value (PPV).

CONCLUSION

In this relatively small sample, SUVmax LN/MBP <1.3 had a NPV of 100% for excluding malignant nodes and SUVmax LN/MBP >2.3 had a PPV of 100% for diagnosing malignant nodes. Using SUVmax ratios could obviate the need for staging EBUS in selected patients with resultant time and cost savings. Selecting different SUVmax ratios, chosen to provide high accuracies for the parameter of interest to change management, is a potentially powerful diagnostic tool that is overlooked when FDG uptake is only classified as 'positive' or 'negative'.

Development and validation of tumor-to-blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer

Background

To develop and validate tumor‐to‐blood based nomograms for preoperative prediction of lymph node (LN) metastasis in patients with lung cancer (LC).

Methods

A prediction model was developed in a primary cohort comprising 330 LN stations from patients with pathologically confirmed LC, these data having been gathered from January 2016 to June 2019. Tumor‐to‐blood variables of LNs were calculated from positron emission tomography‐computed tomography (PET‐CT) images of LC and the short axis diameters of LNs were measured on CT images. Tumor‐to‐blood variables, number of stations suspected of harboring LN metastasis according to PET, and independent clinicopathological risk factors were included in the final nomograms. After being internally validated, the nomograms were used to assess an independent validation cohort containing 101 consecutive LN stations accumulated from July 2019 to March 2020.

Results

Four tumor‐to‐blood variables (left atrium, inferior vena cava, liver, and aortic arch) and the maximum standardized uptake value (SUVmax) for LNs were found to be significantly associated with LN status (p < 0.001 for both primary and validation cohorts). Five predictive nomograms were built. Of these, one with LN SUVmax/left atrium SUVmax was found to be optimal for predicting LN status with AUC 0.830 (95% confidence interval [CI]: 0.774–0.886) in the primary cohort and AUC 0.865 (95% CI: 0.782–0.948) in the validation cohort. All models showed good discrimination, with a modest C‐index, and good calibration in both primary and validation cohorts.

Conclusions

We have developed tumor‐to‐blood based nomograms that incorporate identified clinicopathological risk factors and facilitate preoperative prediction of LN metastasis in LC patients.

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.

Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the ¹⁸F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.

Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).

Conclusions: The RM based on ¹⁸F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions.

Machine learning-based diagnostic method of pre-therapeutic 18F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer

OBJECTIVES

We aimed to find the best machine learning (ML) model using ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for evaluating metastatic mediastinal lymph nodes (MedLNs) in non-small cell lung cancer, and compare the diagnostic results with those of nuclear medicine physicians.

METHODS

A total of 1329 MedLNs were reviewed. Boosted decision tree, logistic regression, support vector machine, neural network, and decision forest models were compared. The diagnostic performance of the best ML model was compared with that of physicians. The ML method was divided into ML with quantitative variables only (MLq) and adding clinical information (MLc). We performed an analysis based on the ¹⁸F-FDG-avidity of the MedLNs.

RESULTS

The boosted decision tree model obtained higher sensitivity and negative predictive values but lower specificity and positive predictive values than the physicians. There was no significant difference between the accuracy of the physicians and MLq (79.8% vs. 76.8%, p = 0.067). The accuracy of MLc was significantly higher than that of the physicians (81.0% vs. 76.8%, p = 0.009). In MedLNs with low ¹⁸F-FDG-avidity, ML had significantly higher accuracy than the physicians (70.0% vs. 63.3%, p = 0.018).

CONCLUSION

Although there was no significant difference in accuracy between the MLq and physicians, the diagnostic performance of MLc was better than that of MLq or of the physicians. The ML method appeared to be useful for evaluating low metabolic MedLNs. Therefore, adding clinical information to the quantitative variables from ¹⁸F-FDG PET/CT can improve the diagnostic results of ML.

KEY POINTS

• Machine learning using two-class boosted decision tree model revealed the highest value of area under curve, and it showed higher sensitivity and negative predictive values but lower specificity and positive predictive values than nuclear medicine physicians. • The diagnostic results from machine learning method after adding clinical information to the quantitative variables improved accuracy significantly than nuclear medicine physicians. • Machine learning could improve the diagnostic significance of metastatic mediastinal lymph nodes, especially in mediastinal lymph nodes with low 18F-FDG-avidity.

Prognostic value of lymph node to primary tumor standardized uptake value ratio in unresectable esophageal cancer

Background

Unresectable esophageal cancer harbors high mortality despite chemoradiotherapy. Better patient selection for more personalized management may result in better treatment outcomes. We presume the ratio of maximum standardized uptake value (SUV) of metastatic lymph nodes to primary tumor (NTR) in 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (FDG PET/CT) may provide prognostic information and further stratification of these patients.

Methods

The patients with non-metastatic and unresectable esophageal squamous cell carcinoma (SCC) receiving FDG PET/CT staging and treated by chemoradiotherapy were retrospectively reviewed. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off value for NTR. Kaplan-Meier method and Cox regression model were used for survival analyses and multivariable analyses, respectively.

Results

From 2010 to 2016, 96 eligible patients were analyzed. The median follow-up time was 10.2 months (range 1.6 to 83.6 months). Using ROC analysis, the best NTR cut-off value was 0.46 for prediction of distant metastasis. The median distant metastasis-free survival (DMFS) was significantly lower in the high-NTR group (9.5 vs. 22.2 months, p = 0.002) and median overall survival (OS) (9.5 vs. 11.6 months, p = 0.013) was also significantly worse. Multivariable analysis revealed that NTR was an independent prognostic factor for DMFS (hazard ratio [HR] 1.81, p = 0.023) and OS (HR 1.77, p = 0.014).

Conclusions

High pretreatment NTR predicts worse treatment outcomes and could be an easy-to-use and helpful prognostic factor to provide more personalized treatment for patients with non-metastatic and unresectable esophageal SCC.

Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images

OBJECTIVE. The purpose of this study was to assess, by analyzing features of the primary tumor with ¹⁸F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung cancer (NSCLC) to metastasize to lymph nodes or distant sites. MATERIALS AND METHODS. Consecutively registered patients with newly diagnosed, untreated NSCLC were retrospectively included in a single-center study. PET images were segmented with local image features extraction software, and data were used for CNN training and validation after data augmentation strategies were used. The standard of reference for designation of N category was invasive lymph node sampling or 6-month follow-up imaging. Distant metastases developing during the study follow-up period were assessed by imaging (CT or PET/CT), in tissue obtained from new suspected sites of disease, and according to the treating oncologist's designation. RESULTS. A total of 264 patients with NSCLC participated in follow-up for a median of 25.2 months (range, 6-43 months). N category designations were available for 223 of 264 (84.5%) patients, and M category for all 264. The sensitivity, specificity, and accuracy of CNN for predicting node positivity were 0.74 ± 0.32, 0.84 ± 0.16, and 0.80 ± 0.17. The corresponding values for predicting distant metastases were 0.45 ± 0.08, 0.79 ± 0.06, and 0.63 ± 0.05. CONCLUSION. This study showed that using a CNN to analyze segmented PET images of patients with previously untreated NSCLC can yield moderately high accuracy for designation of N category, although this may be insufficient to preclude invasive lymph node sampling. The sensitivity of the CNN in predicting distant metastases is fairly poor, although specificity is moderately high.

FDG PET Hybrid Imaging

Molecular imaging with positron emission tomography (PET) using tumour-seeking radiopharmaceuticals has gained wide acceptance in oncology with many clinical applications. The hybrid imaging modality PET/CT (computed tomography) allows assessing molecular as well as morphologic information at the same time. Therefore, PET/CT represents an efficient tool for whole-body staging and re-staging within one imaging modality. In oncology, the glucose analogue 18-F-fluorodeoxyglucose (FDG) is the most widely used PET/CT radiopharmaceutical in clinical routine. FDG PET and FDG PET/CT have been used for staging and re-staging of tumour patients in numerous studies. This chapter will discuss the use and the main indications of FDG PET/CT in oncology with special emphasis on lung cancer, lymphoma, head and neck cancer, melanoma and breast cancer (among other tumour entities). A review of the current literature is given with respect to primary diagnosis, staging and diagnosis of recurrent disease. Besides its integral role in diagnosis, staging and re-staging of disease in oncology, there is increasing evidence that FDG PET/CT can be used for therapy response assessment (possibly influencing therapeutic management and treatment planning) by evaluating tumour control, which will also be discussed in this chapter.