Evaluation of Log Odds of Positive Lymph Nodes in Predicting the Survival of Neoadjuvant Therapy Patients with Non-Small Cell Lung Cancer After Surgery: A SEER Cohort-Based Study

Background log odds of positive lymph nodes (LODDS) is a novel lymph node (LN) descriptor, demonstrating promising prognostic value in many tumors. However, there was limited information on LODDS in non-small cell lung cancer (NSCLC) patients, especially those receiving neoadjuvant therapy followed by lung surgery. Methods A total of 2,059 NSCLC patients who received neoadjuvant therapy and surgery were identified in the Surveillance, Epidemiology, and End Results (SEER) database. We used the X-tile software to calculate the cut-off value of LODDS. Kaplan-Meier survival analysis and receiver operating characteristics (ROC) curve were used to compare the predictive value of the American Joint Committee on Cancer (AJCC) N staging descriptor and LODDS. Univariate and multivariate Cox regression and inverse probability of treatment weighting (IPTW) analyses were conducted to construct the model predicting the prognosis. Results LODDS showed better differentiating ability in survival analysis than N staging descriptor (Log-rank test, P<0.0001 vs. P=0.031). The ROC curve demonstrated that the AUC of LODDS was significantly higher than the N staging descriptor in 1-year, 3-year, and 5-year survival analyses (All P<0.05). Univariate and multivariate Cox regression analysis showed that the LODDS was an independent risk factor for NSCLC patients receiving neoadjuvant therapy followed by surgery, both before and after IPTW (all P<0.001). A clinicopathological model with LODDS, age, gender, T, and radiotherapy could better predict the prognosis. Conclusions Compared with the AJCC N staging descriptor, LODDS exhibits better predictive ability for NSCLC patients receiving neoadjuvant therapy followed by surgery. A multivariate clinicopathological model with LODDS included demonstrates sound performance in predicting the prognosis.

ROLE OF MAGNETIC RESONANCE IMAGING IN NODAL AND METASTATIC STAGING OF PROSTATE CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS

Objective: To evaluate the usage of MRI in prostate cancer staging, especially in nodal involvement (N-staging) and metastasis (M-staging) of prostate cancer. Methods: This is a systematic review and meta-analysis assessing role of MRI in nodal and metastasis staging of prostate cancer. Search of studies were done through search engine using Pubmed, Cochrane, and EBSCO Host and manual searching. Quality of eligible studies were assessed using a revised version of Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and analyzed in pooled analysis according to nodal involvement or metastasis staging, modality of diagnosis used as the index test and gold standard used using STATA version 13. Results: Total 26 studies corresponding with study’s eligibility criteria were found. Overall, usage of MRI has a sensitivity of 47% (95% CI 35% - 60%; I2 83.08%) and a specificity of 93% (95% CI 89% - 96%, I2 82.21%) in nodal involvement staging of prostate cancer, while using of MRI in M-staging of prostate cancer shows a sensitivity of 94% (95% CI 86% - 97%) and a specificity of 99% (95% CI 97% - 99%). Using lymphotrophic superparamagnetic nanoparticle (LSN) - enhanced MRI gives higher sensitivity than using MRI without LSN for N-staging of prostate cancer. Conclusion: The usage of MRI in prostate cancer staging has a moderate sensitivity and relatively high specificity in detecting lymph node. Moreover, it plays an important role and even can be used as a modality of choice in assisting bone metastatic prostate cancer detection.

[68Ga]Ga-DOTA-FAPI-04 PET/CT in The Staging of Gastric Cancer: Improved Diagnostic Efficacy Confirmed By Postoperative Histopathology

Purpose This study aimed to compare the diagnostic performance of [68Ga]Ga-DOTA-FAPI-04 and [18F]-FDG PET/CT in the primary and metastatic lesions of gastric cancer. Methods Fifty-six patients with histologically proven gastric carcinomas were enrolled in this study. Each patient underwent both [18F]-FDG and [68Ga]Ga-FAPI-04 PET/CT within one week. Activity of tracer accumulation in lesions were assessed by maximum standardized uptake value (SUVmax) and TBR (lesions SUVmax/ ascending aorta SUVmean). Histological work-up including immunohistochemical staining for FAP served as a standard of reference. Results [68Ga]Ga-FAPI PET/CT is superior in detecting primary tumors both in patient-based (100% [45/45] vs. 97.8% [44/45]) and lesion-based analyses (97.8% [45/46] vs. 95.7% [44/46]), showing higher SUVmax (10.25 vs. 8.13, P = 0.004) and TBR (11.63 vs. 5.83, P < 0.001), compared with [18F]-FDG PET/CT. The specificity and positive predictive value of [68Ga]Ga-FAPI were significantly higher than that of [18F]-FDG (100.0% vs. 97.7%, P < 0.001; 100.0% vs. 57.1%, P = 0.001) in determining the lymph node (LN) metastases. [68Ga]Ga-FAPI PET/CT was superior to [18F]-FDG PET/CT in N-staging (47.1% [8/17] vs. 23.5% [4/17]), and in evaluation for LN, peritoneum and bone metastases. [68Ga]Ga-FAPI PET/CT detected positive recurrent lesions in all patients and showed more positive lesions and clearer tumor delineation. Two patients underwent follow-up [68Ga]Ga-FAPI PET/CT scans after chemotherapy, which both showed remission. Conclusions [68Ga]Ga-FAPI PET/CT can better detect primary gastric cancer and metastatic lesions in peritoneum, abdominal LNs and bone, showing high usefulness in guiding N staging. Furthermore, [68Ga]Ga-FAPI PET/CT provides more information for patients with recurrence detection and also has great potential in monitoring response to treatment.

Deep Learning-Based Magnetic Resonance Imaging in the Evaluation of Tumor, Node, and Metastasis Staging of Renal Cancer

This study was aimed to investigate the diagnostic accuracy of magnetic resonance imaging (MRI) based on deep dictionary learning in TNM (tumor, node, and metastasis) staging of renal cell carcinoma. In this study, 82 patients with renal cancer were selected as the research object. The results were diagnosed by deep dictionary learning MRI, and TNM staging was performed by professional imaging personnel. MRI image will be reconstructed after deep dictionary learning to improve its image recognition ability. The pathological diagnosis will be handed over to the physiological pathology laboratory of the hospital for diagnosis. The staging results were compared with the pathological diagnostic staging results, and the results were analyzed by consistency statistics to evaluate the diagnostic value. The results showed that T staging was significantly consistent with the pathological diagnosis. 2 cases were misdiagnosed, and the accuracy rate was 97.56%. Compared with the pathological diagnosis, N staging had less obvious consistency. 10 cases were misdiagnosed, and the accuracy rate was 87.80%. M staging was significantly consistent with the pathological diagnosis. 4 cases were misdiagnosed. The accuracy rate was 95.12%. After laparotomy, it was found that 37 patients had emboli and 45 patients had no emboli, while 40 patients had emboli and 42 patients had no emboli by MRI. The accuracy rate was 96.34%. The results showed that in the evaluation of TNM staging by MRI imaging based on deep dictionary learning in patients with renal cell carcinoma, the diagnostic results of N staging and M staging were highly consistent with the pathological diagnosis, while the diagnostic results of T staging were slightly less accurate, and the diagnostic consistency was good. The results can provide effective support for the clinical application of MRI imaging based on deep dictionary learning as the clinical diagnosis of TNM staging of renal cell carcinoma.

Automated Extraction of Tumor Staging and Diagnosis Information From Surgical Pathology Reports

PURPOSE Typically stored as unstructured notes, surgical pathology reports contain data elements valuable to cancer research that require labor-intensive manual extraction. Although studies have described natural language processing (NLP) of surgical pathology reports to automate information extraction, efforts have focused on specific cancer subtypes rather than across multiple oncologic domains. To address this gap, we developed and evaluated an NLP method to extract tumor staging and diagnosis information across multiple cancer subtypes. METHODS The NLP pipeline was implemented on an open-source framework called Leo. We used a total of 555,681 surgical pathology reports of 329,076 patients to develop the pipeline and evaluated our approach on subsets of reports from patients with breast, prostate, colorectal, and randomly selected cancer subtypes. RESULTS Averaged across all four cancer subtypes, the NLP pipeline achieved an accuracy of 1.00 for International Classification of Diseases, Tenth Revision codes, 0.89 for T staging, 0.90 for N staging, and 0.97 for M staging. It achieved an F1 score of 1.00 for International Classification of Diseases, Tenth Revision codes, 0.88 for T staging, 0.90 for N staging, and 0.24 for M staging. CONCLUSION The NLP pipeline was developed to extract tumor staging and diagnosis information across multiple cancer subtypes to support the research enterprise in our institution. Although it was not possible to demonstrate generalizability of our NLP pipeline to other institutions, other institutions may find value in adopting a similar NLP approach—and reusing code available at GitHub—to support the oncology research enterprise with elements extracted from surgical pathology reports.

Loco-regional N staging in Rectal Cancer with Magnetic Resonance Imaging: A Study of Inter- and Intra-Observer Variability

Background: Colorectal cancer is one of the most common tumors for both men and women: in the United States, it represents the third leading cause of new cancer cases and cancer-related deaths. The prognosis is directly related to tumor infiltration in the mesorectum and lymph node metastases. In particular, it’s important to define the distance between lymphadenopathy and mesorectal fascia, as this has repercussions on surgical planning. This study aimed to evaluate the agreement among observers with different abdominal MRI expertise and intra-observer reliability in lymph nodes size and feature definition. Methods: In this retrospective study, MRI examinations were performed in 88 patients with rectal adenocarcinoma treated with primary surgery. Four observers, two senior physicians, and two junior physicians, analyzed MRI scans in two sessions 30 days apart and determined the size and morphological pattern of regional lymph nodes. Statistical analysis included the determination of Fleiss kappa (k) coefficient, Cohen's Kappa coefficient, and confidence intervals (CI). Results: The inter-observer reproducibility for MRI N-staging was good among the four physicians (kappa = 0.65; CI 0.45–0.77). Reproducibility between the two senior physicians had a kappa of 0.68 (CI 0.62–1.00), while between the two junior physicians had a kappa of 0.61 (CI 0.33–0.89). Inter-observer reproducibility was excellent for mesorectal, inferior mesenteric, and internal iliac lymph nodes (kappa values of 0.89, 0.82, and 0.80 respectively). For the other two nodal stations (superior and middle rectal lymph nodes, sacral lymph nodes), there was a good interobserver reproducibility (kappa between 0.70 and 0.77).The intra-observer reproducibility of interpretations of the MRI overall N staging progressively decreased among observer B (kappa= 0.85), observer C (kappa= 0.59), and the other two physicians. There was a significant difference in lymph nodes measurements between the first and second sessions in observer A (p ≥ 0.05). Excellent intraobserver reproducibility was found for mesorectal lymph nodes; the lowest intraobserver reproducibility values were found for presacral and lateral sacral lymph nodes.Conclusions: Although the low accuracy of MRI in assessing the involvement of metastatic lymph nodes in rectal cancer, this study demonstrates good interobserver reliability among physicians with different abdominal MRI experiences.

Prognostic value of different lymph node staging methods for node-positive cardia gastric cancer: a register-based retrospective cohort study

ObjectiveTo investigate the prognostic efficacy of lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) in node-positive cardia gastric adenocarcinoma (CGA).DesignA registry-based retrospective cohort study.SettingPatients diagnosed with node-positive CGA in the Surveillance, Epidemiology, and End Results database from 2010 to 2015.ParticipantsA total of 1038 patients were enrolled and randomly assigned (7:3) to the training set (n=723) or validating set (n=315).Primary outcome measureCancer-specific survival (CSS).ResultsThe baseline characteristics of the training and validation sets were similar. Based on the optimal cut-off values, LNR was classified into low (<0.09), medium (0.09~0.33) and high (>0.33) groups; LODDS was also classified into low (<−2.09), medium (−2.09~−0.65) and high (>−0.65) groups. CSS was significantly different across LNR and LODDS subgroups. The Harrell concordance index of the N stage was lower than that of the LNR or LODDS. The Akaike information criterion of the N stage was higher than that of the LNR or LODDS. Independent predictors included race, T stage, M stage and LNR (or LODDS), and they were incorporated into nomograms for 1-year, 2-year and 5-year CSS prediction. Calibration plots showed satisfactory results for internal and external validity of the nomogram.ConclusionsLNR and LODDS staging methods have better prognostic efficacy than the traditional N staging method in CGA with node metastasis. Moreover, the two values are promising substitutes for N staging in nomogram development when other independent prognostic factors are incorporated.

N-staging in large cell neuroendocrine carcinoma of the lung: diagnostic value of [18F]FDG PET/CT compared to the histopathology reference standard

Background Large cell neuroendocrine carcinoma of the lung (LCNEC) is a rare entity occurring in less than 4% of all lung cancers. Due to its low differentiation and high glucose transporter 1 (GLUT1) expression, LCNEC demonstrates an increased glucose turnover. Thus, PET/CT with 2-[18F]-fluoro-deoxyglucose ([18F]FDG) is suitable for LCNEC staging. Surgery with curative intent is the treatment of choice in early stage LCNEC. Prerequisite for this is correct lymph node staging. This study aimed at evaluating the diagnostic performance of [18F]FDG PET/CT validated by histopathology following surgical resection or mediastinoscopy. N-staging interrater-reliability was assessed to test for robustness of the [18F]FDG PET/CT findings. Methods Between 03/2014 and 12/2020, 46 patients with LCNEC were included in this single center retrospective analysis. All underwent [18F]FDG PET/CT for pre-operative staging and subsequently either surgery (n = 38) or mediastinoscopy (n = 8). Regarding the lymph node involvement, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were calculated for [18F]FDG PET/CT using the final histopathological N-staging (pN0 to pN3) as reference. Results Per patient 14 ± 7 (range 4–32) lymph nodes were resected and histologically processed. 31/46 patients had no LCNEC spread into the lymph nodes. In 8/46 patients, the final stage was pN1, in 5/46 pN2 and in 2/46 pN3. [18F]FDG PET/CT diagnosed lymph node metastasis of LCNEC with a sensitivity of 93%, a specificity of 87%, an accuracy of 89%, a PPV of 78% and a NPV of 96%. In the four false positive cases, the [18F]FDG uptake of the lymph nodes was 33 to 67% less in comparison with that of the respective LCNEC primary. Interrater-reliability was high with a strong level of agreement (κ = 0.82). Conclusions In LCNEC N-staging with [18F]FDG PET/CT demonstrates both high sensitivity and specificity, an excellent NPV but a slightly reduced PPV. Accordingly, preoperative invasive mediastinal staging may be omitted in cases with cN0 disease by [18F]FDG PET/CT. In [18F]FDG PET/CT cN1-cN3 stages histological confirmation is warranted, particularly in case of only moderate [18F]FDG uptake as compared to the LCNEC primary.