An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images.

BACKGROUND: This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs. METHODS: Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong's test was used to compare the CPIs group with the VMIs group. RESULTS: When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong's test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05). CONCLUSION: The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Computed Tomography Findings for Predicting Invasiveness of Lung Adenocarcinomas Manifesting as Pure Ground-Glass Nodules.

Purpose: To comprehensively evaluate qualitative and quantitative features for predicting invasiveness of pure ground-glass nodules (pGGNs) using multiplanar computed tomography. Methods: Ninety-three resected pGGNs (16 atypical adenomatous hyperplasia [AAH], 18 adenocarcinoma in situ [AIS], 31 minimally invasive adenocarcinoma [MIA], and 28 invasive adenocarcinoma [IA]) were retrospectively included. Two radiologists analyzed qualitative and quantitative features on three standard planes. Univariable and multivariable logistic regression analyses were performed to identify features to distinguish the pre-invasive (AAH/AIS) from the invasive (MIA/IA) group. Results: Tumor size showed high area under the curve (AUC) for predicting invasiveness (.860, .863, .874, and .893, for axial long diameter [AXLD], multiplanar long diameter, mean diameter, and volume, respectively). The AUC for AXLD (cutoff, 11 mm) was comparable to that of the volume (P = .202). The invasive group had a significantly higher number of qualitative features than the pre-invasive group, regardless of tumor size. Six out of 59 invasive nodules (10.2%) were smaller than 11 mm, and all had at least one qualitative feature. pGGNs smaller than 11 mm without any qualitative features (n = 16) were all pre-invasive. In multivariable analysis, AXLD, vessel change, and the presence or number of qualitative features were independent predictors for invasiveness. The model with AXLD and the number of qualitative features achieved the highest AUC (.902, 95% confidence interval .833-.971). Conclusion: In adenocarcinomas manifesting as pGGNs on computed tomography, AXLD and the number of qualitative features are independent risk factors for invasiveness; small pGGNs (<11 mm) without qualitative features have low probability of invasiveness.

Adenocarcinoma in situ and minimally invasive adenocarcinoma in lungs of smokers: image feature differences from those in lungs of non-smokers.

PURPOSE: We aimed to examine the characteristics of imaging findings of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in the lungs of smokers compared with those of non-smokers. MATERIALS AND METHODS: We included seven cases of AIS and 20 cases of MIA in lungs of smokers (pack-years ≥ 20) and the same number of cases of AIS and MIA in lungs of non-smokers (pack-years = 0). We compared the diameter of the entire lesion and solid component measured on computed tomography (CT) images, pathological size and invasive component diameter measured from pathological specimens, and CT values of the entire lesion and ground-glass opacity (GGO) portions between the smoker and non-smoker groups. RESULTS: The diameters of AIS and MIA on CT images and pathological specimens of the smoker group were significantly larger than those of the non-smoker group (p = 0.036 and 0.008, respectively), whereas there was no significant difference in the diameter of the solid component on CT images or invasive component of pathological specimens between the two groups. Additionally, mean CT values of the entire lesion and GGO component of the lesions in the smoker group were significantly lower than those in the non-smoker group (p = 0.036 and 0.040, respectively). CONCLUSION: AIS and MIA in smoker's lung tended to have larger lesion diameter and lower internal CT values compared with lesions in non-smoker's lung. This study calls an attention on smoking status in CT-based diagnosis for early stage adenocarcinoma.

A rare case of ciliated muconodular papillary tumor accompanied with adenocarcinoma in situ.

BACKGROUND: Ciliated muconodular papillary tumor (CMPT) is an incredibly rare pulmonary tumor. Currently, little is known about CMPT, and it has not yet been classified by the World Health Organization. The clinical manifestation of CMPT is nonspecific and the diagnosis is only based on pathology. CMPT has been documented in limited reports as a benign tumor, thus the treatment is typically with surgical excision if a solid tumor is identifiable. The prognosis of CMPT is very positive, as no recurrence has been reported in the limited literature available. However, CMPT accompanied with adenocarcinoma in situ has not been reported previously in the literature. CASE PRESENTATION: In this report, we presented a case of a 53-year-old male smoker with CMPT associated with adenocarcinoma in situ. This diagnosis was confirmed by pathological examination, including immunohistostaining. No solid resectable lesion was identified on CT scan; therefore, no surgery was performed. The patient's adenocarcinoma in situ was disseminated in both lungs, thus chemotherapeutic treatment with cisplatin and pemetrexed was given. The patient will be continually followed up closely on a wait-and-watch basis. CONCLUSIONS: In summary, our report reveals a unique case of CMPT in conjunction with adenocarcinoma in situ, potentially revealing an association between CMPT and malignancy which has not been previously reported. More similar case studies will be beneficial to determine the authentic relationship between CMPT and adenocarcinoma in situ.

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists. METHODS: First, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs. RESULTS: The new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6. CONCLUSIONS: The study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm. KEY POINTS: • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma.

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.

OBJECTIVES: Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). METHODS: This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. RESULTS: The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. CONCLUSIONS: Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. KEY POINTS: • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.

OBJECTIVES: Lung adenocarcinomas which manifest as ground-glass nodules (GGNs) have different degrees of pathological invasion and differentiating among them is critical for treatment. Our goal was to evaluate the addition of marginal features to a baseline radiomics model on computed tomography (CT) images to predict the degree of pathologic invasiveness. METHODS: We identified 236 patients from two cohorts (training, n = 189; validation, n = 47) who underwent surgery for GGNs. All GGNs were pathologically confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). The regions of interest were semi-automatically annotated and 40 radiomics features were computed. We selected features using L1-norm regularization to build the baseline radiomics model. Additional marginal features were developed using the cumulative distribution function (CDF) of intratumoral intensities. An improved model was built combining the baseline model with CDF features. Three classifiers were tested for both models. RESULTS: The baseline radiomics model included five features and resulted in an average area under the curve (AUC) of 0.8419 (training) and 0.9142 (validation) for the three classifiers. The second model, with the additional marginal features, resulted in AUCs of 0.8560 (training) and 0.9581 (validation). All three classifiers performed better with the added features. The support vector machine showed the most performance improvement (AUC improvement = 0.0790) and the best performance was achieved by the logistic classifier (validation AUC = 0.9825). CONCLUSION: Our novel marginal features, when combined with a baseline radiomics model, can help differentiate IA from AIS and MIA on preoperative CT scans. KEY POINTS: • Our novel marginal features could improve the existing radiomics model to predict the degree of pathologic invasiveness in lung adenocarcinoma.

CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules.

OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm2; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm2), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.

A simple prediction model using size measures for discrimination of invasive adenocarcinomas among incidental pulmonary subsolid nodules considered for resection.

OBJECTIVES: To develop and validate a concise prediction model using simple size measures for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among incidentally detected subsolid nodules (SSNs) considered for resection and to compare its diagnostic performance with the Brock model. METHODS: This retrospective institutional review board-approved study included 427 surgically resected SSNs (121 preinvasive lesions/minimally invasive adenocarcinomas [MIAs] and 306 IPAs) from 407 patients. After stratified random splitting of the study population into the training and validation sets (3:1), a simple logistic model was constructed using nodule size, solid proportion, and type for the differentiation of IPAs. Diagnostic performance of this model was compared with the original and modified Brock models using the DeLong method for area under the receiver-operating characteristic curve (AUC) and McNemar test for diagnostic sensitivity and specificity. RESULTS: Our proposed model had an AUC of 0.859 in the validation set, while the original Brock model showed an AUC of 0.775 (p = 0.035) and the modified Brock model exhibited an AUC of 0.787 (p = 0.006). At equally high specificity of 90%, our proposed model exhibited significantly higher sensitivity (65.8%) than the original and modified Brock models (38.2% and 50.0%; p < 0.001 and 0.008, respectively). CONCLUSIONS: Our study results demonstrated that the proposed concise model outperformed both Brock models, demonstrating its potential to be utilized as a specific tool to differentiate IPAs from preinvasive lesions and MIAs, which were considered for resection. External validation studies are warranted for the population with incidentally detected SSNs including small SSNs to confirm our observations. KEY POINTS: • Size measures provided sufficient information for the risk stratification of surgical candidate incidental subsolid nodules. • Our proposed concise model showed higher diagnostic performance than the Brock model for incidentally detected subsolid nodules. • Our proposed model can specifically differentiate invasive adenocarcinomas among incidentally detected subsolid nodules and reduce overtreatment for indolent subsolid nodules.

Lung Adenocarcinoma Invasiveness Risk in Pure Ground-Glass Opacity Lung Nodules Smaller than 2 cm.

BACKGROUND: We aimed to identify clinicopathologic characteristics and risk of invasiveness of lung adenocarcinoma in surgically resected pure ground-glass opacity lung nodules (GGNs) smaller than 2 cm. METHODS: Among 755 operations for lung cancer or tumors suspicious for lung cancer performed from 2012 to 2016, we retrospectively analyzed 44 surgically resected pure GGNs smaller than 2 cm in diameter on computed tomography (CT). RESULTS: The study group was composed of 36 patients including 11 men and 25 women with a median age of 59.5 years (range, 34-77). Median follow-up duration of pure GGNs was 6 months (range, 0-63). Median maximum diameter of pure GGNs was 8.5 mm (range, 4-19). Pure GGNs were resected by wedge resection, segmentectomy, or lobectomy in 27 (61.4%), 10 (22.7%), and 7 (15.9%) cases, respectively. Pathologic diagnosis was atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA) in 1 (2.3%), 18 (40.9%), 15 (34.1%), and 10 (22.7%) cases, respectively. The optimal cutoff value for CT-maximal diameter to predict MIA or IA was 9.1 mm. In multivariate analyses, maximal CT-maximal diameter of GGNs ≥10 mm (odds ratio, 24.050; 95% confidence interval, 2.6-221.908; p = 0.005) emerged as significant independent predictor for either MIA or IA. Estimated risks of MIA or IA were 37.2, 59.3, 78.2, and 89.8% at maximal GGN diameters of 5, 10, 15, and 20 mm, respectively. CONCLUSION: Pure GGNs were highly associated with lung adenocarcinoma in surgically resected cases, while estimated risk of GGNs invasiveness gradually increased as maximal diameter increased.

The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules.

OBJECTIVES: Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are assumed to be indolent lung adenocarcinoma with excellent prognosis. We aim to identify these lesions from invasive adenocarcinoma (IA) by a radiomics approach. METHODS: This retrospective study was approved by institutional review board with a waiver of informed consent. Pathologically confirmed lung adenocarcinomas manifested as lung nodules less than 3 cm were retrospectively identified. In-house software was used to quantitatively extract 60 CT-based radiomics features quantifying nodule's volume, intensity and texture property through manual segmentation. In order to differentiate AIS/MIA from IA, least absolute shrinkage and selection operator (LASSO) logistic regression was used for feature selection and developing radiomics signatures. The predictive performance of the signature was evaluated via receiver operating curve (ROC) and calibration curve, and validated using an independent cohort. RESULTS: 402 eligible patients were included and divided into the primary cohort (n = 207) and the validation cohort (n = 195). Using the primary cohort, we developed a radiomics signature based on five radiomics features. The signature showed good discrimination between MIA/AIS and IA in both the primary and validation cohort, with AUCs of 0.95 (95% CI, 0.91-0.98) and 0.89 (95% CI, 0.84-0.93), respectively. Multivariate logistic analysis revealed that the signature (OR, 13.3; 95% CI, 6.2-28.5; p < 0.001) and gender (OR, 3.5; 95% CI, 1.2-10.9; p = 0.03) were independent predictors of indolent lung adenocarcinoma. CONCLUSION: The signature based on radiomics features helps to differentiate indolent from invasive lung adenocarcinoma, which might be useful in guiding the intervention choice for patients with pulmonary nodules. KEY POINTS: • Based on radiomics features, a signature is established to differentiate adenocarcinoma in situ and minimally invasive adenocarcinoma from invasive lung adenocarcinoma.

CT features differentiating pre- and minimally invasive from invasive adenocarcinoma appearing as mixed ground-glass nodules: mass is a potential imaging biomarker.

AIM: To investigate the differential diagnosis value of preoperative computed tomography (CT) features between pre/minimally invasive and invasive adenocarcinoma in pulmonary mixed ground glass nodules (mGGNs). MATERIALS AND METHODS: The histopathological data and CT images of 146 mGGNs in 141 patients were reviewed retrospectively. Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to identify the CT features differentiating between pre/minimally invasive and invasive adenocarcinoma and to evaluate their accuracy. RESULTS: In univariate analysis, there were significant differences (p<0.05) in the nodule diameter, volume, density, mass, solid portion volume, shape, margin, air bronchogram, and pleural retraction between pre/minimally invasive and invasive adenocarcinoma. Multivariate logistic regression analyses revealed that nodule mass and volume were statistically significant independent differentiators. ROC curve analysis was performed to evaluate the differentiators. According to the corresponding ROC curve, the optimal cut-off mass to differentiate pre/minimally invasive adenocarcinoma from invasive adenocarcinoma was 254.87 mg, with a sensitivity of 84.52%, a specificity of 88.71%, and an accuracy of 86.30%. Compared with the area under the ROC curve (AUC) for mass, volume, and diameter, the differential diagnosis value of mass was higher than those of volume and diameter. CONCLUSION: Nodule mass and volume were significant differentiators of pre/minimally invasive adenocarcinoma from invasive adenocarcinoma in mGGN, and mass had a higher differential diagnosis value.

The morphological changes of bronchovascular bundles within subsolid nodules on HRCT correlate with the new IASLC classification of adenocarcinoma.

AIM: To observe the morphological changes of bronchovascular bundles within subsolid nodules on high-resolution (HR) computed tomography (CT) and analyse the correlation with the new adenocarcinoma classification. MATERIALS AND METHODS: Two hundred and sixteen lesions (absent consolidation on mediastinal window) were reviewed retrospectively. CT features including dimensions, contour, morphological changes of the blood vessels, and bronchi/bronchioles, vacuole signs, and their correlation with histopathology were evaluated. RESULTS: Excluding nine non-cancerous lesions, 34 pre-invasive lesions (PILs) including 15 atypical adenomatous hyperplasias (AAHs) and 19 adenocarcinomas in situ (AISs), 21 minimally invasive adenocarcinomas (MIAs), and 152 invasive adenocarcinomas (IACs) were analysed. Lepidic, acinar, and papillary patterns were identified in this cohort of adenocarcinomas. IACs were grouped into three types: type I (lepidic pattern ≥80%, n=47), type II (lepidic pattern ≥50%, <80%, n=67), and type III (lepidic pattern <50%, n=38). The contour of lesions, and morphological changes in vessels and bronchi/bronchioles significantly correlated with the classification of PIL, MIA, and IACs (p=0.000, p=0.000, and p=0.017, respectively). In IACs, the prevalence of vascular abnormalities on HRCT significantly correlated with (p=0.000) the proportion of non-lepidic pattern (23.40% in type I, 58.21% in type II, and 76.32% in type III); the prevalence of bronchial/bronchiolar abnormalities was higher (p=0.008) in type II/III (20.95%) compared with type I (6.38%). CONCLUSIONS: The morphological changes of vessels and bronchi/bronchioles within the subsolid nodules on HRCT help to differentiate IAC from PIL and MIA, and are more common in non-lepidic predominant adenocarcinomas.

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival.

OBJECTIVES: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. METHODS: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis. RESULTS: The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively. CONCLUSIONS: The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival. KEY POINTS: • Radiomic biomarker on CT was designed to identify phenotypes of lung adenocarcinoma • Built up radiomic signature for lung adenocarcinoma manifested as subsolid nodules • Retrospective study showed radiomic signature had greater diagnostic accuracy than volumetric analysis • Radiomics help to evaluate intratumour heterogeneity within lung adenocarcinoma • Medical decision can be given with more confidence.

Quantitative assessment of pancreatic cancer precursor lesions in IHC-stained tissue with a tissue image analysis platform.

Tissue image analysis (tIA) is emerging as a powerful tool for quantifying biomarker expression and distribution in complex diseases and tissues. Pancreatic ductal adenocarcinoma (PDAC) develops in a highly complex and heterogeneous tissue environment and, generally, has a very poor prognosis. Early detection of PDAC is confounded by limited knowledge of the pre-neoplastic disease stages and limited methods to quantitatively assess disease heterogeneity. We sought to develop a tIA approach to assess the most common PDAC precursor lesions, pancreatic intraepithelial neoplasia (PanIN), in tissues from KrasLSL-G12D/+; Trp53LSL-R172H/+; Pdx-Cre (KPC) mice, a validated model of PDAC development. tIA profiling of training regions of PanIN and tumor microenvironment (TME) cells was utilized to guide identification of PanIN/TME tissue compartment stratification criteria. A custom CellMap algorithm implementing these criteria was applied to whole-slide images of KPC mice pancreata sections to quantify p53 and Ki-67 biomarker staining in each tissue compartment as a proof-of-concept for the algorithm platform. The algorithm robustly identified a higher percentage of p53-positive cells in PanIN lesions relative to the TME, whereas no difference was observed for Ki-67. Ki-67 expression was also quantified in a human pancreatic tissue sample available to demonstrate the translatability of the CellMap algorithm to human samples. Together, our data demonstrated the utility of CellMap to enable objective and quantitative assessments, across entire tissue sections, of PDAC precursor lesions in preclinical and clinical models of this disease to support efforts leading to novel insights into disease progression, diagnostic markers, and potential therapeutic targets.

Persistent pulmonary subsolid nodules with solid portions of 5 mm or smaller: Their natural course and predictors of interval growth.

OBJECTIVE: To investigate the natural course of persistent pulmonary subsolid nodules (SSNs) with solid portions ≤5 mm and the clinico-radiological features that influence interval growth over follow-ups. METHODS: From 2005 to 2013, the natural courses of 213 persistent SSNs in 213 patients were evaluated. To identify significant predictors of interval growth, Kaplan-Meier analysis and Cox proportional hazard regression analysis were performed. RESULTS: Among the 213 nodules, 136 were pure ground-glass nodules (GGNs; growth, 18; stable, 118) and 77 were part-solid GGNs with solid portions ≤5 mm (growth, 24; stable, 53). For all SSNs, lung cancer history (p = 0.001), part-solid GGNs (p < 0.001), and nodule diameter (p < 0.001) were significant predictors for interval growth. On subgroup analysis, nodule diameter was an independent predictor for the interval growth of both pure GGNs (p < 0.001), and part-solid GGNs (p = 0.037). For part-solid GGNs, lung cancer history (p = 0.002) was another significant predictor of the interval growth. Interval growth of pure GGNs ≥10 mm and part-solid GGNs ≥8 mm were significantly more frequent than in pure GGNs <10 mm (p < 0.001) and part-solid GGNs <8 mm (p = 0.003), respectively. CONCLUSION: The natural course of SSNs with solid portions ≤5 mm differed significantly according to their nodule type and nodule diameters, with which their management can be subdivided. KEY POINTS: • Pure GGNs ≥10 mm have significantly more frequent interval growth than those <10 mm. • Part-solid GGNs ≥8 mm have significantly more frequent interval growth than those <8 mm. • Management of SSNs with solid portions ≤5 mm can be subdivided by diameter.

Clinical significance of the lymphoscintigraphy in the evaluation of non-axillary sentinel lymph node localization in breast cancer.

BACKGROUND: Identification and biopsy of the sentinel lymph node (SLN) in early-stage breast cancer (T1-T2N0) has become the standard method in the surgical treatment of the axilla, due to its accuracy in the evaluation of axillary lymph node status,thus avoiding extensive axillary lymph node dissection inpatients with negative SLN. The aim of our study is to highlight the role of 99mTc-nanocolloid lymphoscintigraphy in the preoperative lymphatic mapping, especially for SLN localizations outside the axilla, as well as the benefits of this technique in the accurate staging of breast cancer. MATERIALS AND METHOD: 430 patients (age 31-81 years) with breast cancer (T1-T2N0 stage) were included in the study group, who underwent lymphoscintigraphy in order to identify the sentinel lymph node in the Nuclear Medicine Department of "€œProf. Dr. Al. Trestioreanu"€ Institute of Oncology, Bucharest,between October 2008 - July 2014. Radiocolloid (99mTc-nanocolloid)was injected using peritumoral or periareolar intradermal technique, doses between 20-37 MBq (0.3-0.5 ml volume), followed by static and dynamic post-injection acquisitions.Intraoperative identification of the SLN was performed using a gamma-probe, guided by the skin marker performed preoperatively after completion of lymphoscintigraphy. RESULTS: 697 sentinel lymph nodes were identified through imaging techniques in 427 patients (99%). Of them, 364 patients had axillary localization of the SLN, while 48 patients(11%) had non-axillary (pectoral, internal mammary, supraclavicular,intra-mammary) localization and 15 patients (3%)had multiple localization (axillary and non-axillary).Intraoperative histopathological exam revealed lymphatic invasion in 74 SLN (12% macrometastases and 88% micrometastases). CONCLUSIONS: The identification and biopsy of the sentinel lymph node in stages I and IIA is a useful routine for accurate breast cancer staging, suited for axillary lymphatic drainage, as well as for unusual non-axillary SLN localization, guiding the clinician for further postoperative management of these patients.

Clinical and Sonographic Features of Noninvasive Follicular Thyroid Neoplasm With Papillary-Like Nuclear Features: A Retrospective Study.

ABSTRACT: This study was designed to investigate the clinical and sonographic features of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTPs) as compared with classical papillary thyroid carcinoma (cPTC), follicular adenoma (FA), and follicular thyroid carcinoma (FTC). A total of 178 patients were enrolled in this study. The clinical characteristics and sonographic features of thyroid nodules were compared between NIFTP and cPTC or FA/FTC. All nodules were reclassified according to the Thyroid Ultrasound Imaging Reporting and Data System and American Thyroid Association guidelines classification. The mean size of NIFTP was 29.91 ± 14.71 mm, which was larger than that of cPTC ( P = 0.000). Significant difference was found in lymph node metastases between NIFTP and cPTC ( P = 0.000). Most NIFTPs showed solid composition, hypoechoic echogenicity, smooth margin, wider than tall shape, none echogenic foci, absence of halo, and perinodular vascularity, which were similar with FA and FTC. Compared with NIFTP, hypoechoic and very hypoechoic, taller than wide, irregular margin, punctate echogenic foci, absence of halo, and low vascularity were more commonly observed in cPTC. There were statistical differences both in American College of Radiology Thyroid Ultrasound Imaging Reporting and Data System and in American Thyroid Association classification between NIFTP and cPTC ( P < 0.05), but there were no significant differences between NIFTP and FTC/FA ( P > 0.05). The ultrasonographic characteristics of NIFTP were obviously different from cPTC but overlapped with FTC and FA. Ultrasound could help increase preoperative attention of NIFTP in an appropriate clinical setting, which may lead to a more conservative treatment approach.

CT-Assisted Improvements in the Accuracy of the Intraoperative Frozen Section Examination of Ground-Glass Density Nodules.

OBJECTIVES: The intraoperative frozen section examination (IFSE) of pulmonary ground-glass density nodules (GGNs) is a great challenge. In the present study, through comparing the correlation between the computed tomography (CT) findings and pathological diagnosis of GGNs, the CT features as independent risk factors affecting the examination were defined, and their value in the rapid intraoperative examination of GGNs was explored. METHODS: The relevant clinical data of 90 patients with GGNs on CT were collected, and all CT findings of GGNs, including the maximum transverse diameter, average CT value, spiculation, solid component, vascular sign, air sign, bronchus sign, lobulation, and pleural indentation, were recorded. All the cases received thoracoscopic surgery, and final pathological results were obtained. The cases were divided into three groups on the basis of pathological diagnosis: benign/atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS)/microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). The CT findings were analyzed statistically, the independent risk factors were identified through the intergroup bivariate logistic regression analysis on variables with statistically significant differences, and a receiver operating curve (ROC) was plotted to establish a logistic regression model for diagnosing GGNs. A retrospective analysis was conducted on the coincidence rate of the rapid intraoperative and routine postoperative pathological examinations of the 90 cases with GGNs. The relevant clinical data of 49 cases with GGNs were collected. Conventional rapid intraoperative examination and CT-assisted rapid intraoperative examination were performed, and their coincidence rates with routine postoperative pathological examinations were compared. RESULTS: No statistical differences in the onset age, gender, smoking history, and family history of malignant tumors were found among cases with GGNs in the identification of benign/AAH, AIS/MIA, and IAC (P = 0.158, P = 0.947, P = 0.746, P = 0.566). No statistically significant difference was found among the three groups in terms of CT findings, such as lobulation, bronchus sign, pleural indentation, spiculation, vascular sign, and solid component (P > 0.05). The air sign, the maximum transverse diameter of GGNs, and average CT value showed statistically significant differences among the groups (P < 0.001, P < 0.05, P < 0.001). Bivariate logistic regression analysis was performed on three risk factors, and the predicted probability value was obtained. A ROC curve was plotted by using the maximum transverse diameter as a predictor for analysis between the groups with benign/AAH and AIS/MIA, and the results demonstrated that the area under the curve (AUC) was 0.692. A ROC curve was plotted by using the predicted probability value, maximum transverse diameter, and average CT value as predictors for distinguishing between the groups with AIS/MIA and IAC, and the results showed that the AUC values of the predicted probability value, maximum transverse diameter, and CT value were 0.920, 0.816, and 0.772, respectively. A regression model [Logit (P) = 2.304 - 2.689X1 + 0.302X2 + 0.011X3] was established to identify GGNs as IAC, obtaining AUC values of up to 0.920 for the groups with AIS/MIA and IAC, the sensitivity of 0.821, and the specificity of 0.894. The coincidence rate of rapid intraoperative and routine postoperative pathological examinations taken for modeling was 79.3%, that of conventional IFSE and postoperative pathological examination in prospective studies was 83.7%, and that of CT-assisted rapid intraoperative and postoperative pathological examinations was 98.0%. The former two were statistically different from the last one (P = 0.003 and P = 0.031, respectively). CONCLUSION: The air sign, maximum transverse diameter, and average CT value of the CT findings of GGNs had superior capabilities to enhance the pathologic classification of GGNs. The auxiliary function of the comprehensive multifactor analysis of GGNs was better than that of single-factor analysis. CT-assisted diagnosis can improve the accuracy of rapid intraoperative examination, thereby increasing the accuracy of the selection of operative approaches in clinical practice.

Can computed tomography differentiate adenocarcinoma in situ from minimally invasive adenocarcinoma?

BACKGROUND: Given the subtle pathological signs of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), effective differentiation between the two entities is crucial. However, it is difficult to predict these conditions using preoperative computed tomography (CT) imaging. In this study, we investigated whether histological diagnosis of AIS and MIA using quantitative three-dimensional CT imaging analysis could be predicted. METHODS: We retrospectively analyzed the images and histopathological findings of patients with lung cancer who were diagnosed with AIS or MIA between January 2017 and June 2018. We used Synapse Vincent (v. 4.3) (Fujifilm) software to analyze the CT attenuation values and performed a histogram analysis. RESULTS: There were 22 patients with AIS and 22 with MIA. The ground-glass nodule (GGN) rate was significantly higher in patients with AIS (p < 0.001), whereas the solid volume (p < 0.001) and solid rate (p = 0.001) were significantly higher in those with MIA. The mean (p = 0.002) and maximum (p = 0.025) CT values were significantly higher in patients with MIA. The 25th, 50th, 75th, and 97.5th percentiles (all p < 0.05) for the CT values were significantly higher in patients with MIA. CONCLUSIONS: We demonstrated that quantitative analysis of 3D-CT imaging data using software can help distinguish AIS from MIA. These analyses are useful for guiding decision-making in the surgical management of early lung cancer, as well as subsequent follow-up.