A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study.

PURPOSE: To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists. METHODS: This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists. RESULTS: The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively). CONCLUSION: The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

The role of histological subtype and chemotherapy on prognosis of ureteral cancer.

OBJECTIVE: To date, there have been few studies examining the prognostic implications of histological subtypes in ureteral cancer. And chemotherapy plays a crucial role in the treatment of ureteral cancer, while many factors influence the efficacy of chemotherapy. This study aimed to utilize the Surveillance, Epidemiology and End Results database to assess the impact of histological type on ureteral cancer prognostic outcomes and discovered how histological type and T-stage influence the efficacy of chemotherapy. METHODS: Based on Surveillance, Epidemiology, and End Results Program, we reviewed 8915 records of patients with primary ureteral cancer from 18 centers between 2000 and 2018. We focused on the overall survival and cancer-specific survival of the records and used Kaplan‒Meier method to calculate survival curves. RESULTS: In the comparison of prognostic outcomes, atypical subtypes exhibited a less favorable prognosis compared to typical ureteral carcinoma. Notably, patients diagnosed with papillary urothelial carcinoma demonstrated the most favorable overall survival (p = 0.005). Statistically significant benefits were observed in the prognosis of patients with non-papillary urothelial carcinoma who received chemotherapy (HR = 0.860, 95% CI 0.764-0.966, p = 0.011), while chemotherapy did not yield a statistically significant effect on the prognosis of patients with papillary urothelial carcinoma (HR = 1.055, 95% CI 0.906-1.228, p = 0.493). Chemotherapy had an adverse impact on the prognosis of patients with T1 ureteral cancer (HR = 1.235, 95% CI 1.016-1.502, p = 0.034), whereas it exhibited a positive prognostic effect for T3/T4 cases (HR = 0.739, 95% CI 0.654-0.835, p < 0.001). CONCLUSIONS: Histological type affects the prognosis of ureteral cancer. And evaluation of cancer histological type and T stage in ureteral cancer patients prior to chemotherapy is mandatory.

Rare case report: a case of histological type transformation of lung cancer caused by neoadjuvant immunotherapy.

Lung cancer remains the leading cause of cancer-related mortality, with 1.8 million deaths per year. Small cell lung cancer and non-small cell lung cancer (NSCLC) are the main cancer types. Approximately 85% of cases are NSCLC, including adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In this reported treatment case, the tumor histological type changed after targeted therapy, which has not been previously well documented. The patient was a 67-year-old woman diagnosed with squamous cell carcinoma via bronchoscopy. She received five neoadjuvant immune monotherapies. The lesion shrank but then progressed, with a diagnosis of small cell carcinoma via bronchoscopy. This finding suggests that tumor acquisition of resistance as manifested by cancer-type changes needs consideration and study in the application of this particular type of immunotherapy.

The comparison of pure uterine serous carcinoma and mixed tumor with serous component: a single-institution review of 91 cases.

BACKGROUND: Pure uterine serous carcinoma (p-USC) and mixed tumors with serous component (m-USC) are aggressive subtypes of endometrial cancer associated with high mortality rates. This retrospective study aimed to compare clinicopathologic features and outcomes of p-USC and m-USC in a single center. METHODS: This study retrospectively reviewed patients diagnosed with USC at Peking University People's Hospital between 2008 and 2022. T-tests and chi-square tests were used to compare clinicopathological characteristics between p-USC and m-USC. Kaplan-Meier survival curve and Cox regression analysis were used to analyze the impact of clinical and pathological variables on OS and PFS. RESULTS: Among the 91 patients who underwent surgery, 65.9% (n = 60) were p-USC, and 34.1% (n = 31) were m-USC. Patients with p-USC had earlier menopause (P = 0.0217), a lower rate of progesterone receptor(PR) expression (P < 0.001), and were more likely to have positive peritoneal cytology (P = 0.0464). After a median follow-up time of 40 months, 28 (46.7%) p-USC and 9 (29%) m-USC patients had progression disease, 18 (30%) and 8 (25.8%) patients died of their disease. 5-year PFSR were 51.2% and 75.3%, respectively, and 5-year OS rates were 66% and 67.4%. Kaplan-Meier survival analysis showed that p-USC was more likely to relapse than m-USC (P = 0.034), but there was no significant difference in OS. Cox regression analysis showed that lymph node metastasis and surgical approach were risk factors for OS, and myoinvasion depth ≥ 1/2 was an independent risk factor for PFS. CONCLUSIONS: p-USC was more likely to relapse than m-USC, but there was no significant difference in OS between the two subtypes.

Change in Main Histological Type of Invasive Breast Cancer From Ductal to Lobular Carcinoma by Neoadjuvant Chemotherapy.

We present a case study of breast cancer initially diagnosed as invasive ductal carcinoma (IDC), which subsequently substituted into invasive lobular carcinoma (ILC) following neoadjuvant chemotherapy (NAC). A 61-year-old woman presented with a palpable breast lump, and histological examination through core needle biopsy (CNB) confirmed the presence of IDC. After a 6-month course of NAC, the patient achieved a clinically complete response (cCR) and underwent mastectomy. The surgical specimen showed no detectable tumor upon palpation, but microscopic analysis revealed a highly infiltrative growth of poorly-cohesive small atypical cells in the original tumor area. Immunohistochemical staining demonstrated that the tumor cells were negative for E-cadherin, leading to a diagnosis of ILC. To address the histological discrepancy before and after NAC, we re-evaluated the initial CNB using E-cadherin immunohistochemistry. While most tumor cells were E-cadherin positive, a small area displaying scirrhous subtype-like morphology exhibited E-cadherin negativity. Consequently, we revised the diagnosis to mixed IDC-ILC. The differential chemosensitivity between IDC and ILC may provide insight into this phenomenon.

Clinicopathological Features of Invasive Breast Cancer: A Five-Year Retrospective Study in Southern and South-Western Ethiopia.

Background: Breast cancer (BC) is the most common type of cancer in Ethiopia. The incidence of BC is also rising, but the exact figure is still poorly known. Therefore, this study was conducted to address the gap in epidemiological data on BC in southern and southwestern Ethiopia. Materials and Methods: This is a five-year (2015-2019) retrospective study. The demographic and clinicopathological data were collected from biopsy reports of different kinds of breast carcinomas in the pathology department of Jimma University Specialized Hospital and Hawassa University Specialized Referral Hospital. Histopathological grades and stages were conducted using Nottingham grading and TNM staging system, respectively. Collected data were entered and analyzed using SPSS Version-20 software. Results: The mean age of patients at diagnosis was 42.27 (SD = 13.57) years. The pathological stage of most BC patients was stage III, and most of them had tumor sizes greater than 5 cm. Most patients had moderately differentiated tumor grade, and mastectomy was the most common type of surgery at the time of diagnosis. Invasive ductal carcinoma was the most common histological type of BC, followed by invasive lobular carcinoma. Lymph node involvement was seen in 60.5% of cases. Lymph node involvement was associated with tumor size (χ2 = 8.55, p = 0.033) and type of surgery (χ2 = 39.69, p < 0.001). Conclusions: This study showed that BC patients in southern and southwestern Ethiopia displayed advanced pathological stages, relatively young age at diagnosis, and predominant invasive ductal carcinoma histological patterns.

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features.

PURPOSE: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. METHODS: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models. RESULTS: Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively. CONCLUSION: It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.

Efficacy analysis of multidisciplinary treatment for Wilms tumor in a single center.

OBJECTIVE: To analyze the efficacy of multidisciplinary treatment for Wilms tumor (WT) in Kunming Children's Hospital, and investigate the risk factors affecting the prognosis of WT. METHODS: The clinic-pathological data were collected and analyzed in patients with unilateral WT treated in Kunming Children's Hospital from January 2017 to July 2021. Research subjects were selected according to inclusion criteria and exclusion criteria. The risk factors and independent risk factors that affect the prognosis of patients with WT were determined by Kaplan-Meier survival analysis and Cox proportional hazards model, respectively. OUTCOME: A total of 68 children were included in this study, and the 5-year overall survival (OS) rate was 87.4%. Kaplan-Meier survival analysis results showed that ethnicity (P = 0.020), the tumor volume of resection (P = 0.001), histological type (P < 0.001), and postoperative recurrence (P < 0.001) were the risk factors affecting the prognosis of children with WT. The results of the Cox proportional hazards model showed that only the histological type (P = 0.018) was the independent risk factor for the prognosis of WT. CONCLUSION: The efficacy of multidisciplinary treatment for WT was satisfying. The histological type has important predictive value for the prognosis of WT, and the patient with unfavorable histology has a poor prognosis.

Classification of Histological Types and Stages in Non-small Cell Lung Cancer Using Radiomic Features Based on CT Images.

Non-invasive diagnostic method based on radiomic features in patients with non-small cell lung cancer (NSCLC) has attracted attention. This study aimed to develop a CT image-based model for both histological typing and clinical staging of patients with NSCLC. A total of 309 NSCLC patients with 537 CT series from The Cancer Imaging Archive (TCIA) database were included in this study. All patients were randomly divided into the training set (247 patients, 425 CT series) and testing set (62 patients, 112 CT series). A total of 107 radiomic features were extracted. Four classifiers including random forest, XGBoost, support vector machine, and logistic regression were used to construct the classification model. The classification model had two output layers: histological type (adenocarcinoma, squamous cell carcinoma, and large cell) and clinical stage (I, II, and III) of NSCLC patients. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence interval (CI) were utilized to evaluate the performance of the model. Seven features were selected for inclusion in the classification model. The random forest model had the best classification ability compared with other classifiers. The AUC of the RF model for histological typing and clinical staging of NSCLC patients in the testing set was 0.700 (95% CI, 0.641-0.759) and 0.881 (95% CI, 0.842-0.920), respectively. The CT image-based radiomic feature model had good classification ability for both histological typing and clinical staging of patients with NSCLC.

Expert Consensus on Pathological Diagnosis of Intrahepatic Cholangiocarcinoma (2022 version).

Intrahepatic cholangiocarcinoma (iCCA) can originate from the large bile duct group (segment bile ducts and area bile ducts), small bile duct group (septal bile ducts and interlobular bile ducts), and terminal bile duct group (bile ductules and canals of Hering) of the intrahepatic biliary tree, which can be histopathological corresponding to large duct type iCCA, small duct type iCCA and iCCA with ductal plate malformation pattern, and cholangiolocarcinoma, respectively. The challenge in pathological diagnosis of above subtypes of iCCA falls in the distinction of cellular morphologies, tissue structures, growth patterns, invasive behaviors, immunophenotypes, molecular mutations, and surgical prognoses. For these reasons, this expert consensus provides nine recommendations as a reference for standardizing and refining the diagnosis of pathological subtypes of iCCA, mainly based on the 5th edition of the World Health Organization Classification of Tumours of the Digestive System.

Association of Programmed Death-Ligand 1 Expression with Aggressive Histological Types of Thyroid Carcinoma.

Introduction: Immunohistochemical expression of programmed death-ligand 1 (PD-L1) has become a biomarker to predict the usefulness of cancer immunotherapy using PD-1/PD-L1 blockade in a variety of advanced-stage tumours. This emerging biomarker may serve to generate novel therapies for aggressive thyroid carcinoma (TC), which has not shown optimal results with existing treatments. Methods: The present study investigated the relevance of PD-L1 expression in aggressive histological types of TC compared with that found in less aggressive types. Surgically resected specimens were investigated, including 52 cases of TC consisting of 26 cases of aggressive histological types and 26 cases of less aggressive histological types. Immunohistochemical examinations were carried out on paraffin blocks of both groups using a mouse monoclonal primary antibody against PD-L1 (clone 22C3). PD-L1 expression was evaluated by calculating the tumour proportion score (TPS) in both groups. Results: The results revealed a significant difference in the median TPS value of PD-L1 expression between the two groups. The TPS values were found to be higher in the group of aggressive histological types of TC compared with those in the group of less aggressive histological types. A significant difference in TPS value was also found for the extrathyroidal extension variable. Discussion: In conclusion, the present study found a significant association between PD-L1 expression and the aggressive histological type of TC. In addition, a potential association between PD-L1 expression and the presence of extrathyroidal extension of TC was observed. These findings provide novel approaches for immunotherapy as a potential new treatment modality in patients with aggressive histological types of TC.

Patterns of Extrathoracic Metastasis in Lung Cancer Patients.

Metastasis is a major cause of death in lung cancer patients. Therefore, a deeper understanding of the metastatic mechanisms is important for developing better management strategies for lung cancer patients. This study evaluated the patterns of extrathoracic metastases in lung cancer. We retrieved data for 25,103 lung cancer patients from an institutional database and then evaluated the impacts of clinicopathologic factors on metastasis patterns. We found that 36.5% of patients had extrathoracic metastasis. Younger patients had a significantly higher extrathoracic metastasis rate in most histologic subtypes. Metastases to the bone (58.3%), central nervous system (CNS) (44.3%), liver (26.6%) and adrenal gland (18.3%) accounted for 85.5% of all extrathoracic metastases. Patients with nonmucinous adenocarcinoma had significantly higher bone metastasis rate. Patients with small cell carcinoma and large cell neuroendocrine carcinoma (LCNEC) had significantly higher liver metastasis rates. Further, patients with LCNEC also had a significantly lower bone metastasis rate, and patients with squamous cell carcinoma had a significantly lower CNS metastasis rate. Patients with multiple cancers had similar patterns of metastasis compared to patients with only lung cancer. In conclusion, different histologic subtypes of lung cancer have different metastatic patterns. Our study might help clinicians decide on follow-up strategies.

The value of narrow-band imaging bronchoscopy in diagnosing central lung cancer.

Aims: This research aimed to study the value of narrow-band imaging(NBI) in the diagnosis of central lung cancer. Materials and methods: This study included 916 patients with clinical suspected of central lung cancer or follow-up of patients after curative lung cancer surgery. All of the patients were examined by Olympus Evis Lucera electronic bronchoscope system, any sites that were abnormal when viewed by white-light bronchoscopy (WLB) or NBI were biopsied, four to six biopsies were taken at each site of the abnormal region visualized as lesions, we record the endoscopic features of NBI and compared with histopathology results, to evaluate the diagnostic value of NBI for central lung cancer and the relationship between vascular patterns of NBI and histological types of lung cancer, and try to establish a multinomial logistic regression model for predicting the histological types of lung cancer. The biopsy specimens were examined by CD34 antibody through immunohistochemistry (IHC) method, CD34 marked microvessel density(MVD), compared the number of microvessels between benign and malignant diseases and the number between different histological types of lung cancer, to verify the results of NBI. Results: NBI provided high sensitivity (91.7%), specificity (84.9%), positive predictive value (97.6%), negative predictive value (61.5%), and agreement rate (90.7%). The predominant vascular patterns in the well-defined histological types of lung cancer were dotted blood vessels (121 patients), tortuous blood vessels (248 patients), and abrupt-ending blood vessels (227 patients). Logistic regression analysis of the results showed that smoking status of the patient, combined with vascular patterns under NBI, and age partly affect the histological types of lung cancer. Conclusions: NBI is highly accurate for the diagnosis of central lung cancer.

The value of CT-based radiomics nomogram in differential diagnosis of different histological types of gastric cancer.

To establish and verify a nomogram based on computed tomography (CT) radiomics analysis to predict the histological types of gastric cancer preoperatively for patients with surgical indications. A sum of 171 patients with gastric cancer were included into this retrospective study. The least absolute shrinkage and selection operator (LASSO) was used for feature selection while the multivariate Logistic regression method was used for radiomics model and nomogram building. The area under curve (AUC) was used for performance evaluation in this study. The radiomics model got AUCs of 0.755 (95% CI 0.650-0.859), 0.71 (95% CI 0.543-0.875) and 0.712 (95% CI 0.500-0.923) for histological prediction in the training, the internal and external verification cohorts. The radiomics nomogram based on radiomics features and Carbohydrate antigen 125 (CA125) showed good discriminant performance in the training cohort (AUC: 0.777; 95% CI 0.679-0.875), the internal (AUC: 0.726; 95% CI 0.5591-0.8933) and external verification cohort (AUC: 0.720; 95% CI 0.5036-0.9358). The calibration curve of the radiomics nomogram also showed good results. The decision curve analysis (DCA) shows that the radiomics nomogram is clinically practical. The radiomics nomogram established and verified in this study showed good performance for the preoperative histological prediction of gastric cancer, which might contribute to the formulation of a better clinical treatment plan.

Squamous cell carcinoma predicts worse prognosis than adenocarcinoma in stage IA lung cancer patients: A population-based propensity score matching analysis.

Background: Although numerous studies have reported the association between histological types and the prognosis of IA non-small-cell lung cancer (NSCLC) patients, few studies have deeply investigated the impact of pathology on the outcome of NSCLC patients. In this study, we comprehensively explored whether the type of histology influenced the outcome of IA-stage NSCLC patients. Methods: The study population was obtained from the Surveillance, Epidemiology, and End Results (SEER) program, which is supported by the National Cancer Institute of the United States. To avoid potential bias, the method of propensity score matching (PSM) was used to obtain a balanced cohort for further analysis. Results: The results from univariate and multivariate regression models showed that lung squamous cell carcinoma (LSQCC) patients were at a significantly greater risk of undergoing shorter overall survival (OS) and lung cancer-specific survival (LCSS). After PSM analysis, LSQCC was still closely associated with a reduction in OS and LCSS. All of these suggested that the histological type was an independent prognostic factor for OS and LCSS. Conclusion: Our study demonstrated that squamous cell carcinoma predicted worse OS and LCSS in IA-stage NSCLC patients compared with lung adenocarcinoma (LUAD). We suggest that the outcomes of LSQCC and LUAD are very different and that the two histological types should be differently analyzed.

The significance of interferon gamma inducible protein 16 (IFI16) expression in drug resistant ovarian cancer cell lines.

BACKGROUND: Inherent or developed during treatment drug resistance is the main reason for the low effectiveness of chemotherapy in ovarian cancer. IFI16 is a cytoplasmic/nuclear protein involved in response to virus's infection and cell cycle arrest associated with the cellular senescence. METHODS: Here we performed a detailed IFI16 expression analysis in ovarian cancer cell lines sensitive (A2780) and resistant to doxorubicin (DOX) (A2780DR1 and A2780DR2) and paclitaxel (PAC) (A2780PR1). IFI16 mRNA level, protein level in the nuclear and cytoplasmic fraction (Western blot analysis), the protein expression in cancer cells and nuclei (immunofluorescence analysis) and cancer patient lesions (immunohistochemistry) were performed in this study. RESULTS: We observed upregulation of IFI16 expression in drug resistant cell lines with dominant cytoplasmic localization in DOX-resistant cell lines and nuclear one in the PAC-resistant cell line. The most abundantly overexpressed isoforms of IFI16 were IFI16A and IFI16C. Finally, an analysis of a histological type of ovarian cancer (immunohistochemistry) showed expression in serous ovarian cancer. CONCLUSIONS: Expression of IFI16 in drug-resistant cell lines suggests its role in drug resistance development in ovarian cancer. Expression in serous ovarian cancer suggests its role in the pathogenesis of this histological type.

Prognostic Significance of C-reactive Protein in Patients With Non-metastatic Papillary Renal Cell Carcinoma: Results from the INternational Marker Consortium for Renal Cancer (INMARC) Cohort.

INTRODUCTION: C-reactive protein is a useful biomarker for screening renal cell carcinoma (RCC); however, its significance in papillary RCC is unclear. We assessed the prognostic effect of serum C-reactive protein levels in patients with surgically treated non-metastatic papillary RCC. PATIENTS AND METHODS: We established an international multi-institutional database (the INternational Marker Consortium for Renal Cancer) of 3799 patients with surgically treated RCC. Among these, data of 400 patients with non-metastatic papillary RCC were analyzed. An elevated pretreatment serum C-reactive protein level was defined as > 10 mg/L. Associations of clinical covariates with recurrence-free survival were investigated. RESULTS: Among the patients, 174 were African Americans, 155 were European-Americans, 50 were Asians, and 21 were of other races. Pathological T stages were 1, 2, 3, and 4 in 313, 46, 32, and 3 patients, respectively. The median pretreatment C-reactive protein level was 1.0 mg/L; 48 patients exhibited an elevated C-reactive protein level. During follow-up (median 18 months), 30 patients presented recurrence. The 1-, 3-, and 5-year recurrence-free rates were 95%, 91%, and 87%, respectively. Multivariate analysis revealed a significant association of the elevated pretreatment C-reactive protein level with poor recurrence-free survival (hazard ratio 2.47, 95% confidence interval 1.03-5.48; P = .043). The 5-year recurrence-free survival was significantly worse for patients with elevated C-reactive protein levels (67% vs. 90%; P = .001). CONCLUSIONS: C-reactive protein is a significant prognostic factor for patients with non-metastatic papillary RCC and can serve as a useful adjunct biomarker for screening patients with a high risk of recurrence.

CT-Guided Percutaneous Core Needle Biopsy in Typing and Subtyping Lung Cancer: A Comparison to Surgery.

Background: Lung cancer histologic types and subtypes are closely associated with treatment selection and prognosis prediction. In this study, we aim to evaluate the suitability of computed tomography-guided percutaneous core needle biopsy (CT-guided PCNB) in typing and subtyping lung cancer. Methods: From August 2007 to December 2015, the patients who underwent CT-guided PCNB and lung lesion resection were retrospectively collected and analyzed. All pathological sections were reassessed in consensus by 2 junior pathologists (group A) and 2 senior pathologists (group B), respectively. All cases were diagnosed on 3 levels: first, malignant and benign diagnosis; second, histologic types diagnosis; and third, histologic subtypes diagnosis and compared with surgery results. Pearson chi-square test was used to compare the differences of diagnostic accuracy between pathologists in group A and group B. Results: A cohort of 160 patients was included in this study. On the first level, the diagnostic accuracy was 90.63% (group A) and 94.38% (group B), (P = .20). On the second level, the diagnostic accuracy for malignant lesions, adenocarcinoma (ADC), and squamous cell carcinoma (SQC) were, respectively, 72.66%, 84.72%, and 69.05% (group A) and 76.98%, 90.28%, and 71.43% (group B) (P > .05). On the third level, the diagnostic accuracy for ADC subtypes were 26.39% (group A) and 55.56% (group B) (P < 0.01); for SQC subtypes were 28.57% (group A) and 38.10% (group B) (P = 0.36). Conclusion: Small specimens obtained by CT-guided PCNB were suitable for the diagnosis of lung cancer histologic types, which may contribute to the selection of a suitable treatment strategy for the unresectable lung cancers. While for the diagnosis of subtypes, discussion with experienced pathologists was recommended.

Significance of histology and nodal status on the survival of women with early-stage cervical cancer: validation of the 2018 FIGO cervical cancer staging system.

OBJECTIVE: To assess the efficacy of the FIGO 2018 classification system for nodal-specific classifications for early-stage cervical cancer; specifically, to examine the impact of nodal metastasis on survival and the effect of postoperative treatments, according to histological subtypes. METHODS: This society-based retrospective observational study in Japan examined 16,539 women with the 2009 FIGO stage IB1 cervical cancer who underwent primary surgical treatment from 2004 to 2015. Associations of cause-specific survival (CSS) with nodal metastasis and postoperative adjuvant therapy were examined according to histology type (squamous cell carcinoma [SCC], n=10,315; and non-SCC, n=6,224). RESULTS: The nodal metastasis rate for SCC was higher than that for non-SCC (10.7% vs. 8.3%, p<0.001). In multivariable analysis, the impact of nodal metastasis on CSS was greater for non-SCC tumors (adjusted-hazard ratio [HR], 3.11; 95% confidence interval [CI], 2.40-4.02) than for SCC tumors (adjusted-HR, 2.20; 95% CI, 1.70-2.84; p<0.001). Propensity score matching analysis showed significantly lower CSS rates for women with pelvic nodal metastasis from non-SCC tumors than from SCC tumors (5-year CSS rate, 75.4% vs. 90.3%, p<0.001). The CSS rates for women with nodal metastasis in SCC histology were similar between the postoperative concurrent chemoradiotherapy/radiotherapy and chemotherapy groups (89.2% vs. 86.1%, p=0.42), whereas those in non-SCC histology who received postoperative chemotherapy improved the CSS (74.1% vs. 67.7%, p=0.043). CONCLUSION: The node-specific staging system in the 2018 FIGO cervical cancer classification is applicable to both non-SCC tumors and SCC tumors; however, the prognostic significance of nodal metastases and efficacy of postoperative therapies vary according to histology.