Rational and design of prophylactic cranial irradiation (PCI) and brain MRI surveillance versus brain MRI surveillance alone in patients with limited-stage small cell lung cancer achieving complete remission (CR) of tumor after chemoradiotherapy: a multicenter prospective randomized study.

BACKGROUND: Prophylactic cranial irradiation (PCI) is part of standard care in limited-stage small cell lung cancer (SCLC) at present. As evidence from retrospective studies increases, the benefits of PCI for limited-stage SCLC are being challenged. METHODS: A multicenter, prospective, randomized controlled study was designed. The key inclusion criteria were: histologically or cytologically confirmed small cell carcinoma, age ≥ 18 years, KPS ≥ 80, limited-stage is defined as tumor confined to one side of the chest including ipsilateral hilar, bilateral mediastinum and supraclavicular lymph nodes, patients have received definitive thoracic radiotherapy (regardless of the dose-fractionation of radiotherapy used) and chemotherapy, evaluated as complete remission (CR) of tumor 4-6 weeks after the completion of chemo-radiotherapy. Eligible patients will be randomly assigned to two arms: (1) PCI and brain MRI surveillance arm, receiving PCI (2.5 Gy qd to a total dose of 25 Gy in two weeks) followed by brain MRI surveillance once every three months for two years; (2) brain MRI surveillance alone arm, undergoing brain MRI surveillance once every three months for two years. The primary objective is to compare the 2-year brain metastasis-free survival (BMFS) rates between the two arms. Secondary objectives include 2-year overall survival (OS) rates, intra-cranial failure patterns, 2-year progression-free survival rates and neurotoxicity. In case of brain metastasis (BM) detect during follow-up, stereotactic radiosurgery (SRS) will be recommended if patients meet the eligibility criteria. DISCUSSION: Based on our post-hoc analysis of a prospective study, we hypothesize that in limited-stage SCLC patients with CR after definitive chemoradiotherapy, and ruling out of BM by MRI, it would be feasible to use brain MRI surveillance and omit PCI in these patients. If BM is detected during follow-up, treatment with SRS or whole brain radiotherapy does not appear to have a detrimental effect on OS. Additionally, this approach may reduce potential neurotoxicity associated with PCI.

Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study.

BACKGROUND AND PURPOSE: To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification. MATERIALS AND METHODS: This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature. RESULTS: Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk. CONCLUSIONS: The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.

FDG PET/CT Findings of a Small Cell Carcinoma of the Lung Presented as Diffuse Dermatomyositis.

ABSTRACT: Patients with dermatomyositis are prone to have occult malignancy. A previously healthy 68-year-old man with dermatomyositis underwent FDG PET/CT to detect possible malignancy of unknown origin. The images showed not only diffuse increased activity in the muscle, which was related to the known dermatomyositis, but also intense activity in the anterior chest with foci of abnormal activity throughout the body. Pathology examination confirmed small cell carcinoma of the lung with widespread metastases.

Metabolic Tumor Volume as Significant Predictor for Chemotherapy Containing PD-L1 Blocker in Extensive Stage Small Cell Lung Cancer.

BACKGROUND/AIM: Chemo-immunotherapy, including the programmed death ligand 1 (PD-L1) antibody, is an effective treatment for patients with extensive-stage small-cell lung cancer (ES-SCLC). However, no biomarker has been established for the prediction of chemo-immunotherapy. Therefore, we investigated the potential of 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) as a predictive marker. PATIENTS AND METHODS: Forty-six patients with ES-SCLC who received 18F-FDG-PET immediately before combined platinum-based chemotherapy with PD-L1 blockade as a first-line treatment were eligible, and the maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on 18F-FDG uptake were evaluated. RESULTS: PD-L1 and tumor infiltrative lymphocytes (TILs) were immunohistochemically analyzed in 36 of the 46 patients. A high MTV was significantly associated with poor performance status and low albumin levels, and there was a significant association between low albumin and high TLG. Univariate analysis identified sex, Brinkman index, and MTV as significant predictors of progression-free survival (PFS), and sex, SUVmax, MTV, and TLG as significant factors of overall survival (OS). Multivariate analysis revealed that sex, Brinkman index, and MTV were independent prognostic factors for PFS, and sex, SUVmax, MTV, and TLG were significant predictors of OS. SUVmax was significantly higher in patients with positive PD-L1 expression than in those with negative expression but was not significantly different between positive and negative TILs. Moreover, the levels of MTV and TLG were not closely associated with the levels of PD-L1 and TILs. CONCLUSION: MTV or TLG metabolic tumor activity is suitable for the prediction of chemo-immunotherapy outcomes in patients with ES-SCLC.

Evaluating CD133 as a Radiotheranostic Target in Small-Cell Lung Cancer.

Despite decades of work, small-cell lung cancer (SCLC) remains a frustratingly recalcitrant disease. Both diagnosis and treatment are challenges: low-dose computed tomography (the approved method used for lung cancer screening) is unable to reliably detect early SCLC, and the malignancy's 5 year survival rate stands at a paltry 7%. Clearly, the development of novel diagnostic and therapeutic tools for SCLC is an urgent, unmet need. CD133 is a transmembrane protein that is expressed at low levels in normal tissue but is overexpressed by a variety of tumors, including SCLC. We previously explored CD133 as a biomarker for a novel autoantibody-to-immunopositron emission tomography (PET) strategy for the diagnosis of SCLC, work that first suggested the promise of the antigen as a radiotheranostic target in the disease. Herein, we report the in vivo validation of a pair of CD133-targeted radioimmunoconjugates for the PET imaging and radioimmunotherapy of SCLC. To this end, [89Zr]Zr-DFO-αCD133 was first interrogated in a trio of advanced murine models of SCLC─i.e., orthotopic, metastatic, and patient-derived xenografts─with the PET probe consistently producing high activity concentrations (>%ID/g) in tumor lesions combined with low uptake in healthy tissues. Subsequently, a variant of αCD133 labeled with the ß-emitting radiometal 177Lu─[177Lu]Lu-DTPA-A″-CHX-αCD133─was synthesized and evaluated in a longitudinal therapy study in a subcutaneous xenograft model of SCLC, ultimately revealing that treatment with a dose of 9.6 MBq of the radioimmunoconjugate produced a significant increase in median survival compared to a control cohort. Taken together, these data establish CD133 as a viable target for the nuclear imaging and radiopharmaceutical therapy of SCLC.

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer.

BACKGROUND: The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). METHODS: Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis. RESULTS: A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001). CONCLUSION: Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer.

BACKGROUND: Brain metastases (BMs) are common in small cell lung cancer (SCLC), and the efficacy of immune checkpoint inhibitors (ICIs) in these patients is uncertain. In this study we aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) for intracranial efficacy prediction of ICIs in patients with BMs from SCLC. METHODS: The training and validation cohorts consisted of 101 patients from two centers. The interclass correlation coefficient (ICC), logistic univariate regression analysis, and random forest were applied to select the radiomic features, generating the radiomics score (Rad-score) through the formula. Using multivariable logistic regression analysis, a nomogram was created by the combined model. The discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. Kaplan-Meier curves were plotted based on the nomogram scores. RESULTS: Ten radiomic features were selected for calculating the Rad-score as they could differentiate the intracranial efficacy in the training (area under the curve [AUC], 0.759) and the validation cohort (AUC, 0.667). A nomogram was created by combining Rad-score, treatment lines, and neutrophil-to-lymphocyte ratio (NLR). The training cohort obtained an AUC of 0.878 for the combined model, verified in the validation cohort (AUC = 0.875). Kaplan-Meier analyses showed the nomogram was associated with progression-free survival (PFS) (p = 0.0152) and intracranial progression-free survival (iPFS) (p = 0.0052) but not overall survival (OS) (p = 0.4894). CONCLUSION: A radiomics nomogram model for predicting the intracranial efficacy of ICIs in SCLC patients with BMs can provide suggestions for exploring individual-based treatments for patients.

Synchronous double primary small cell lung cancer and invasive ductal breast carcinoma: a case report.

BACKGROUND: Although lung and breast cancers are common malignancies, the occurrence of primary synchronous neoplasms involving these organs has been rarely reported in literature. CASE PRESENTATION: A 75-year-old female patient presented at a local hospital with a ten-day history of dizziness and slurred speech. A CT contrast-enhanced scan revealed a 4.2 cm mass in the lower lobe of the right lung and a 3.8 cm space-occupying lesion in the right breast. Subsequent breast ultrasound identified a hypoechoic lesion measuring5.41 × 4.75 × 3.06 cm in the right breast, and an ultrasound-guided biopsy confirmed the presence of infiltrating ductal carcinoma of the right breast. The immunohistochemistry analysis of the breast mass revealed positive staining for ER, PR, HER-2, AR and Ki67 in the tumor cells, while negative staining was observed for P63, Calponin, CK5/6 and CK14. MR imaging of the head detected abnormal signals in the right frontal lobe (3.6 cm×2.9 cm in size), left cerebellar hemisphere, and punctate enhancement in the left temporal lobe, indicating potential metastasis. Pathological examination of a lung biopsy specimen confirmed the presence of small cell lung cancer (SCLC). Furthermore, immunohistochemistry analysis of the lung lesions demonstrated positive staining for TTF-1, CK-Pan, Syn, CgA, CD56, P53 (90%) and Ki67 (70%), and negative staining for NapsinA and P40 in the tumor cells. The patient's diagnosis of SCLC with stage cT2bN0M1c IVB and brain metastases (BM), as well as invasive ductal breast carcinoma (IDC), was confirmed based on the aforementioned results. Whereupon we proposed a treatment plan consisting of whole-brain radiation (40 Gy/20fractions), focal radiotherapy (60 Gy/20fractions), and adjuvant concurrent chemotherapy with oral etoposide (50 mg on days 1 to 20). CONCLUSIONS: To the best of our knowledge, the present case is the first of its kind to describe the synchronous double cancer, consisting of primary SCLC and IDC.

Delta Radiomics Model for the Prediction of Overall Survival and Local Recurrence in Small Cell Lung Cancer Patients After Chemotherapy.

RATIONALE AND OBJECTIVES: To evaluate the validity of CT-based delta radiomics signatures in predicting overall survival (OS) and local recurrence (LR) in small cell lung cancer (SCLC) patients after chemotherapy. MATERIALS AND METHODS: Retrospectively enrolled 136 SCLC patients were split into training and testing cohorts. Radiomics features were extracted from CT images before, after the second, and the fourth cycle of chemotherapy. Delta radiomics features were obtained by calculating the net changes of features. Three radiomics signatures (R1, R2, and R3) and three delta radiomics signatures (R21, R31, and R32) were developed. The best signature was defined as the radiomics risk signature (RRS). The significant clinicoradiological factors and RRS of OS or LR were applied to build the combined model. RRS was also investigated in the subgroups based on stage and treatment regimens, respectively. RESULTS: Delta radiomics models presented improved performance. R32 signature demonstrated the highest C-indices in the training and testing cohorts, with C-indices of 0.850 and 0.834 in the OS arm, and 0.723 and 0.737 in the LR arm, respectively. The incremental performance was observed after the clinicoradiological characteristics integrated into the RRSOS, with C-indexes of 0.857 and 0.836, respectively. Furthermore, the stratified analysis also confirmed the ability of RRS based on the stage and treatment regimen subgroups in the OS and LR arms, respectively. CONCLUSION: Delta radiomics signatures could improve the personalized prediction of OS and LR at the early stage of chemotherapy in SCLC patients. R32 signature performed the highest performance.

When chronic obstructive pulmonary disease meets small cell lung cancer: an unusual case report of rapid progression.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease and a risk factor for lung cancer. Small cell lung cancer is a neuroendocrine tumor with a high degree of malignancy and an overall five-year survival rate of less than 7%. CASES PRESENTATION: Herein, we report the case of an 68-year-old male presented to the respiratory department with cough, sputum, and dyspnea. He was diagnosed as community acquired pneumonia and treated with intravenous anti-infection. Previous pulmonary function was definitively diagnosed as COPD. About 7 months after discharge, the patient returned to the hospital for cough and dyspnea. After diagnosis of the tumor, cisplatin, etoposide and durvalumab were administered. Finally the patient died of respiratory failure approximately 9 months after his diagnosis. CONCLUSIONS: For COPD patients with immunocompromised manifestations, it is necessary to be alert to complications and shorten the follow-up interval of chest CT. COPD may accelerate the formation and progression of SCLC.

A Rare-Earth Near-Infrared Nanoprobe for the Identification of Small Cell Lung Cancer.

Background: Small cell lung cancer (SCLC) is a common subtype of lung cancer, and there is currently no established method for the early identification of SCLC. We prepared a novel rare-earth near-infrared (NIR) downconversion nanoprobe to identify SCLC cells. Methods: The shell precursors Gd-OA and Na-TFA-OA were prepared, and the NaYF4:Nd@NaGdF4-ProGRP antibody probe was obtained after synthesizing downconversion fluorescent nanocrystals. The probe was used for NIR identification of cancer cells and subcutaneous tumors in nude mice. The biotoxicity of the probe to SCLC cells and nude mice was studied. Results: The NaYF4:Nd@NaGdF4-ProGRP antibody probe was successfully prepared, with a size of 44 nm, an NIR emission peak at approximately 1060 nm, and a concentration of 40 µmol/mL. The probe could achieve accurate NIR identification of SCLC cells and subcutaneous tumors in nude mice. Optimal images of the subcutaneous tumor model were obtained approximately 10 minutes after probe injection. There was no significant change in the hematology indices, respiratory rate, or heart rate of nude mice after the probe was injected (all P > 0.05). Conclusion: We have successfully prepared a low-toxicity probe that can identify SCLC cells, which may be useful for the early detection of SCLC. And conduct theoretical exploration for non-invasive identification and identification of some early metastatic lesions without pathological sampling in the future.

Assessing treatment outcomes of chemoimmunotherapy in extensive-stage small cell lung cancer: an integrated clinical and radiomics approach.

BACKGROUND: Small cell lung cancer (SCLC) is a highly malignant cancer characterized by metastasis and an extremely poor prognosis. Although combined chemoimmunotherapy improves the prognosis of extensive-stage (ES)-SCLC, the survival benefits remain limited. Furthermore, no reliable biomarker is available so far to predict the treatment outcomes for chemoimmunotherapy. METHODS: This retrospective study included patients with ES-SCLC treated with first-line combined atezolizumab or durvalumab with standard chemotherapy between Janauray 1, 2019 and October 1, 2022 at five medical centers in China as the chemoimmunotherapy group. The patients were divided into one training cohort and two independent external validation cohorts. Additionally, we created a control group of ES-SCLC who was treated with first-line standard chemotherapy alone. The Radiomics Score was derived using machine learning algorithms based on the radiomics features extracted in the regions of interest delineated on the chest CT obtained before treatment. Cox proportional hazards regression analysis was performed to identify clinical features associated with therapeutic efficacy. The log-rank test, time-dependent receiver operating characteristic curve, and Concordance Index (C-index) were used to assess the effectiveness of the models. RESULTS: A total of 341 patients (mean age, 62±8.7 years) were included in our study. After a median follow-up time of 12.1 months, the median progression-free survival (mPFS) was 7.1 (95% CI 6.6 to 7.7) months, whereas the median overall survival (mOS) was not reached. The TNM stage, Eastern Cooperative Oncology Group performance status, and Lung Immune Prognostic Index showed significant correlations with PFS. We proposed a predictive model based on eight radiomics features to determine the risk of chemoimmunotherapy resistance among patients with SCLC (validation set 1: mPFS, 12.0 m vs 5.0 m, C-index=0.634; validation set 2: mPFS, 10.8 m vs 6.1 m, C-index=0.617). By incorporating the clinical features associated with PFS into the radiomics model, the predictive efficacy was substantially improved. Consequently, the low-progression-risk group exhibited a significantly longer mPFS than the high-progression-risk group in both validation set 1 (mPFS, 12.8 m vs 4.5 m, HR=0.40, p=0.028) and validation set 2 (mPFS, 9.2 m vs 4.6 m, HR=0.30, p=0.012). External validation set 1 and set 2 yielded the highest 6-month area under the curve and C-index of 0.852 and 0.820, respectively. Importantly, the integrated prediction model also exhibited considerable differentiation power for survival outcomes. The HR for OS derived from the low-progression-risk and high-progression-risk groups was 0.28 (95% CI 0.17 to 0.48) in all patients and 0.20 (95% CI 0.08 to 0.54) in validation set. By contrast, no significant differences were observed in PFS and OS, between high-progression-risk patients receiving chemoimmunotherapy and the chemotherapy cohort (mPFS, 5.5 m vs 5.9 m, HR=0.90, p=0.547; mOS, 14.5 m vs 13.7 m, HR=0.97, p=0.910). CONCLUSIONS: The integrated clinical and radiomics model can predict the treatment outcomes in patients with ES-SCLC receiving chemoimmunotherapy, rendering a convenient and low-cost prognostic model for decision-making regarding patient management.

A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study.

PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation. METHODS: 558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram. CONCLUSION: A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.

Significance of marginal vessels in differentiating peripheral small-cell lung cancer and benign lung tumor.

BACKGROUND: Some peripheral small cell lung cancers (pSCLCs) and benign lung tumors (pBLTs) have similar morphological features but different treatment and prognosis. PURPOSE: To determine the significance of marginal vessels in differentiating pSCLCs and pBLTs. MATERIAL AND METHODS: A total of 57 and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC and pBLT with similar morphological features were enrolled in this study retrospectively. The patients' clinical characteristics and computed tomography (CT) features of tumors and marginal vessels (vessels connecting with tumors) were analyzed and compared. RESULTS: Compared with pBLTs, pSCLCs had a larger diameter (P = 0.001) but lower enhancement (P = 0.015) and fewer had calcification (P = 0.013). Compared with pBLTs, more lesions had proximal (70.2% vs. 22.1%) and distal (59.6% vs. 4.2%) marginal vessels in pSCLCs (each P < 0.0001). In addition, in pSCLCs, the numbers of proximal (1.3 ± 1.4 vs. 0.3 ± 0.6), distal (2.4 ± 3.1 vs. 0.1 ± 0.5), and total (3.6 ± 3.5 vs. 0.4 ± 1.0) marginal vessels were all more than those in pBLTs (each P < 0.001). Receiver operating characteristic curve analysis revealed the positive distal marginal vessel sign had the highest specificity (95.8%), and the number of total marginal vessels had the best performance in discriminating pSCLC from pBLT (cutoff value = 1.5, AUC = 0.80, 95% CI = 0.72-0.89, sensitivity = 70.2%, and specificity = 91.6%). CONCLUSION: For peripheral solid nodules similar to pBLTs but without any calcification, the possibility of pSCLC should be considered if they have multiple marginal vessels (≥2), especially the distal ones.

Accuracy of nodal staging by 18F-FDG-PET/CT in limited disease small-cell lung cancer.

BACKGROUND: Small-cell lung cancer (SCLC) is highly aggressive with a nearly incurable disease in most cases. The most important prognostic factor is the status of the mediastinal lymph nodes. Only a small proportion of patients can be diagnosed at early stages and directed to curative multimodal treatment. Therefore, accuracy of nodal staging by (18F)-Fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) computed tomography (18F-FDG-PET/CT) in (very) limited disease SCLC, although not well investigated, is highly important. METHODS: Treatment naive, non-bulky patients treated or diagnosed with SCLC between June 2012 and April 2020 with complete data including FDG-PET/CT and invasive mediastinal staging were retrospectively analyzed (n = 19). Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and accuracy of mediastinal lymph node staging of 18F-FDG-PET/CT was calculated. RESULTS: The FDG-PET/CT showed a sensitivity of 91%, and the specificity was calculated as 87.5%. In this cohort, the disease prevalence in lymph nodes was 58% (n = 11). Positive predictive value was 91%, NPV 88% and accuracy calculated at 89%. One patient was upstaged from single-level N2 to multilevel N2. In one patient, upstaging in invasive staging was performed from N2 to N3, and one patient was downstaged from N1 to N0. CONCLUSIONS: FDG-PET/CT is a valuable tool for the detection of distant metastases, but in mediastinal staging of SCLC some limitations might remain. Invasive methods remain the gold standard. Therefore, the mediastinal lymph nodal status of patients with SCLC screened for multimodal treatment should be further evaluated by additional invasive techniques to verify the exact N-staging and to optimize treatment stratification.

68Ga-SSO-120 PET for Initial Staging of Small Cell Lung Cancer Patients: A Single-Center Retrospective Study.

PET imaging using the somatostatin receptor 2 (SSTR2) antagonist satoreotide trizoxetan (SSO-120, previously OPS-202) could offer accurate tumor detection and screening for SSTR2-antagonist radionuclide therapy in patients with SSTR2-expressing small cell lung cancer (SCLC). The aim of this single-center study was to investigate tumor uptake and detection rates of 68Ga-SSO-120 in comparison to 18F-FDG PET in the initial staging of SCLC patients. Methods: Patients with newly diagnosed SCLC who underwent additional whole-body 68Ga-SSO-120 PET/CT during the initial diagnostic workup were retrospectively included. The mean administered activity was 139 MBq, and the mean uptake time was 60 min. Gold-standard staging 18F-FDG PET/CT was evaluated if available within 2 wk before or after 68Ga-SSO-120 PET if morphologic differences in CT images were absent. 68Ga-SSO-120- or 18F-FDG-positive lesions were reported in 7 anatomic regions (primary tumor, thoracic lymph node metastases, and distant metastases including pleural, contralateral pulmonary, liver, bone, and other) according to the TNM classification for lung cancer (eighth edition). Consensus TNM staging (derived from CT, endobronchial ultrasound-guided transbronchial needle aspiration, PET, and brain MRI) by a clinical tumor board served as the reference standard. Results: Thirty-one patients were included, 12 with limited and 19 with extensive disease according to the Veterans Administration Lung Study Group classification. 68Ga-SSO-120-positive tumor was detected in all patients (100%) and in 90 of the 217 evaluated regions (41.5%). Thirteen patients (42.0%) had intense average 68Ga-SSO-120 uptake (region-based mean SUVmax ≥ 10); 28 patients (90.3%) had average 68Ga-SSO-120 uptake greater than liver uptake (region-based mean peak tumor-to-liver ratio > 1). In 25 patients with evaluable 18F-FDG PET, primary tumor, thoracic lymph node metastases, and distant metastases were detected in 100%, 92%, and 64%, respectively, of all investigated patients by 68Ga-SSO-120 and in 100%, 92%, and 56%, respectively, by 18F-FDG PET. 68Ga-SSO-120 PET detected additional contralateral lymph node, liver, and brain metastases in 1, 1, and 2 patients, respectively (no histopathology available), and 18F-FDG PET detected additional contralateral lymph node metastases in 3 patients (1 confirmed, 1 systematic endobronchial ultrasound-guided transbronchial needle aspiration-negative, and 1 without available histopathology). None of these differences altered Veterans Administration Lung Study Group staging. The region-based monotonic correlation between 68Ga-SSO-120 and 18F-FDG uptake was low (Spearman ρ = 0.26-0.33). Conclusion: 68Ga-SSO-120 PET offers high diagnostic precision with comparable detection rates and additional complementary information to the gold standard, 18F-FDG PET. Consistent uptake in most patients warrants exploration of SSTR2-directed radionuclide therapy.