Malignant Nodules Research Articles

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models

Pulmonary nodules and nodule characteristics are important indicators of lung nodule malignancy. However, nodule information is often documented as free text in clinical narratives such as radiology reports in electronic health record systems. Natural language processing (NLP) is the key technology to extract and standardize patient information from radiology reports into structured data elements. This study aimed to develop an NLP system using state-of-the-art transformer models to extract pulmonary nodules and associated nodule characteristics from radiology reports. We identified a cohort of 3080 patients who underwent LDCT at the University of Florida health system and collected their radiology reports. We manually annotated 394 reports as the gold standard. We explored eight pretrained transformer models from three transformer architectures including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT), for clinical concept extraction, relation identification, and negation detection. We examined general transformer models pretrained using general English corpora, transformer models fine-tuned using a clinical corpus, and a large clinical transformer model, GatorTron, which was trained from scratch using 90 billion words of clinical text. We compared transformer models with two baseline models including a recurrent neural network implemented using bidirectional long short-term memory with a conditional random fields layer and support vector machines. RoBERTa-mimic achieved the best F1-score of 0.9279 for nodule concept and nodule characteristics extraction. ALBERT-base and GatorTron achieved the best F1-score of 0.9737 in linking nodule characteristics to pulmonary nodules. Seven out of eight transformers achieved the best F1-score of 1.0000 for negation detection. Our end-to-end system achieved an overall F1-score of 0.8869. This study demonstrated the advantage of state-of-the-art transformer models for pulmonary nodule information extraction from radiology reports.

A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology

IntroductionElectronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. MethodsThis phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2–0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. ResultsBased on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). ConclusionsIn the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a “multiomics” platform.

A comparison study of artificial intelligence performance against physicians in benign–malignant classification of pulmonary nodules

Abstract Objectives To compare and evaluate the performance of artificial intelligence (AI) against physicians in classifying benign and malignant pulmonary nodules from computerized tomography (CT) images. Methods A total of 506 CT images with pulmonary nodules were retrospectively collected. The AI was trained using in-house software. For comparing the diagnostic performance of artificial intelligence and different groups of physicians in pulmonary nodules, statistical methods of receiver operating characteristic (ROC) curve and area under the curve (AUC) were analyzed. The nodules in CT images were analyzed in a case-by-case manner. Results The diagnostic accuracy of AI surpassed that of all groups of physicians, exhibiting an AUC of 0.88 alongside a sensitivity of 0.80, specificity of 0.84, and accuracy of 0.83. The area under the curve (AUC) of seven groups of physicians varies between 0.63 and 0.84. The sensitivity of the physicians within these groups varies between 0.4 and 0.76. The specificity of different groups ranges from 0.8 to 0.85. Furthermore, the accuracy of the seven groups ranges from 0.7 to 0.82. The professional insights for enhancing deep learning models were obtained through an examination conducted on a per-case basis. Conclusions AI demonstrated great potential in the benign–malignant classification of pulmonary nodules with higher accuracy. More accurate information will be provided by AI when making clinical decisions.

Value of peripheral blood rare cell EGFR gene amplification detection in the evaluation of benign and malignant pulmonary nodules

Objective: To explore the value of detection of epidermal growth factor receptor (EGFR) gene amplification in peripheral blood rare cells in the assessment of benign and malignant pulmonary nodules. Methods: A total of 262 patients with pulmonary nodules were selected as the retrospectively study subjects from the Second Affiliated Hospital of Army Military Medical University and Peking Union Medical College Hospital from July 2022 to August 2023. There were 98 males and 164 females, with the age range from 16 to 79 (52.1±12.1) years. The EGFR gene amplification testing was performed on the rare cells enriched from patients' peripheral blood, and the clinical manifestations, CT imaging features, histopathological and/or pathological cytological confirmed results of patients were collected. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of the method of detection of EGFR gene amplification in peripheral blood rare cells, and its diagnostic efficacy was evaluated. Results: Among the 262 patients, 143 were malignant pulmonary nodules and 119 were benign pulmonary nodules. The differences between malignant pulmonary nodules and benign pulmonary nodules in nodule diameter and nodule density were statistically significant (both P<0.001), while the differences in age, gender and nodule number were not statistically significant (all P>0.05). The number [M (Q1, Q3)] of EGFR gene amplification positive rare cells in patients with malignant pulmonary nodule was 8 (6, 11), which was higher than that in patients with benign pulmonary nodule [2 (1, 4), P<0.001]. The ROC curve results showed that when the optimal cut-off value was 5 (that was, the number of EGFR gene amplification positive rare cells was>5), the area under the curve (AUC) of the detection of EGFR gene amplification in peripheral blood rare cells for discrimination of benign and malignant pulmonary lesions was 0.816 (95%CI: 0.761-0.870), with a sensitivity of 83.2%, a specificity of 80.7%, and an accuracy of 82.1%. Based on the analysis of the diameter of the nodules, the AUC for distinguishing between benign and malignant pulmonary nodules with diameter 5-9 mm and 10-30 mm was 0.797 (95%CI: 0.707-0.887) and 0.809 (95%CI: 0.669-0.949), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule density, the AUC for distinguishing between benign and malignant solid nodule and subsolid nodule was 0.845 (95%CI: 0.751-0.939) and 0.790 (95%CI: 0.701-0.880), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule number, the AUC for distinguishing between benign and malignant solitary pulmonary nodule and multiple pulmonary nodule was 0.830 (95%CI: 0.696-0.965) and 0.817 (95%CI: 0.758-0.877), respectively, with sensitivity, specificity and accuracy reached 80% or above. Conclusion: The detection of EGFR gene amplification in peripheral blood rare cells contributes to the evaluation of benign and malignant pulmonary nodules, and can be used in the auxiliary diagnosis of benign and malignant pulmonary nodules.

Hybrid deep learning assisted multi classification: Grading of malignant thyroid nodules.

Thyroid nodules are commonly diagnosed with ultrasonography, which includes internal characteristics, varying looks, and hazy boundaries, making it challenging for a clinician to differentiate between malignant and benign forms based only on visual identification. The advancement of AI, particularly DL, provides significant breakthroughs in the domain of medical image identification. Yet, there are certain obstacles to achieving accuracy as well as efficacy in thyroid nodule detection. The thyroid nodules in this study are detected and classified using an inventive hybrid deep learning-assisted multi-classification method. The median blur method is applied in this work to eliminate the salt and pepper noise from the image. Then MPIU-Net-based segmentation is utilized to segment the image. The LGBPNP-based features are retrieved from the segmented image to obtain a single histogram sequence of the LGBP pattern in addition to other features like extraction of multi-texton and LTP-based features. After the feature extraction, the data augmentation process is applied and then the features are fed to the hybrid classification-based nodule classification model that comprises Deep Maxout and CNN, this hybrid classification trains the features and predicts the thyroid nodule. Additionally, the TIRADS score classification is used for the projected malignant thyroid nodule coupled with statistical features collected from the segmented. The DBNAAF with transfer learning model is employed to classify the grading of malignant thyroid nodules, where the weights of the model are learned with transfer learning. The MCC of the Hybrid Model is 0.9445, whereas the DCNN is 0.6858, YOLOV3-DMRF is 0.7229, CNN is 0.7780, DBN is 0.7601, Bi-GRU is 0.7038, Deep Maxout is 0.7528, and RNN is 0.8522, respectively.

A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named “Lung Cancer Prediction” (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.

Current Preoperative Management of Vulvar Squamous Cell Carcinoma: An Overview.

Vulvar carcinoma is a rare cancer affecting the genital tract, constituting 4% of gynecological tumors. Vulvar squamous cell carcinoma (VSCC) is the most common type. Diagnosis relies on biopsy during vulvoscopy, plus imaging such as ultrasonography (USG), magnetic resonance imaging (MRI) and positron emission tomography (PET). This review aims to lay out a thorough overview as to the current preoperative management of VSCC, both in case of vulvar and lymph node involvement. The data research was conducted using the following databases: MEDLINE, EMBASE, Web of Sciences, Scopus, ClinicalTrial.gov, OVID and Cochrane Library from 2010 to 2024. The selection criteria included only original articles. Seventeen studies were assessed for eligibility. A concordance rate of 62.3% for vHSIL and 65.2% for carcinoma at vulvoscopy, with a sensitivity of 98%, specificity of 40%, PPV (Positive Predictive Value) of 37% and NPV (Negative Predictive Value) of 98% in identifying malignant lesions was found. Regarding the reliability of PET for staging and assessing lymph node involvement, a mean SUV (Standardized Uptake Value) for malignant vulvar lesions of 8.4 (range 2.5-14.7) was reported. In the case of MRI, useful for the evaluation of loco-regional infiltration and lymph node involvement, the ratio of the short-to-long-axis diameter and the reader's diagnostic confidence for the presence of lymph node metastasis yielded accuracy of 84.8% and 86.9%, sensitivity of 86.7% and 87.5%, specificity of 81.3% and 86.2%, PPV of 89.7% and 87.5% and NPV of 76.5% and 86.2%, respectively. A long lymph node axis >10 mm and a short diameter >5.8 mm were found to be predictors of malignancy. At USG, instead, the two main characteristics of potentially malignant lymph nodes are cortical thickness and short axis length; the combination of these ultrasound parameters yielded the highest accuracy in distinguishing between negative and positive lymph nodes. Despite the heterogeneity of the included studies and the lack of randomized clinical trials, this review provides a broad overview of the three imaging tools used for the presurgical management of VSCC. Nowadays, although MRI and PET represent the gold standard, ultrasound evaluation is taking on a growing role, as long as it is carried out by expert sonographer. The management of this rare disease should be always performed by a multidisciplinary team in order to precisely stage the tumor and determine the most suitable treatment approach.

Differential diagnosis of thyroid nodules by DCE-MRI based on compressed sensing volumetric interpolated breath-hold examination: A feasibility study

ObjectivesTo explore the potential and performance of quantitative and semi-quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on compressed sensing volumetric interpolated breath-hold (CS-VIBE) examination in the differential diagnosis of thyroid nodules. Materials and methodsA total of 208 patients with 259 thyroid nodules scheduled for surgery operation were prospectively recruited. All participants underwent routine and DCE-MRI. DCE-MRI quantitative parameters [Ktrans, Kep, Ve], semi-quantitative parameters [wash-in, wash-out, time to peak (TTP), arrival time (AT), peak enhancement intensity (PEI), and initial area under curve in 60 s (iAUC)] and time-intensity curve (TIC) types were analyzed. Differential diagnostic performances were assessed using area under the receiver operating characteristic curve (AUC) and compared with the Delong test. ResultsKtrans, Kep, Ve, wash-in, wash-out, PEI and iAUC were statistically significantly different between malignant and benign nodules (P < 0.001). Among these parameters, ROC analysis revealed that Ktrans showed the highest diagnostic performance in the differentiation of benign and malignant nodules, followed by wash-in. ROC analysis also revealed that Ktrans achieved the best diagnostic performance for distinguishing papillary thyroid carcinoma (PTC) from non-PTC, follicular adenoma (FA) from non-FA, nodular goiter (NG) from non-NG, with AUC values of 0.854, 0.895 and 0.609, respectively. Type III curve is frequently observed in benign thyroid nodules, accounting for 77.4% (82/106). While malignant nodules are more common in type II, accounting for 57.5% (88/153). ConclusionThyroid examination using CS-VIBE based DCE-MRI is a feasible, non-invasive method to identify benign and malignant thyroid nodules and pathological types.

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm)

Objectives. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by devising and externally validating a Multimodal Integrated Feature Neural Network (MIFNN). We hypothesize that the fusion of deep learning algorithms with morphological nodule features will significantly enhance diagnostic accuracy. Materials and Methods. Data were retrospectively collected from the Lung Nodule Analysis 2016 (LUNA16) dataset and four local centers in Beijing, China. The study includes patients with small pulmonary nodules (≤10 mm). We developed a neural network, termed MIFNN, that synergistically combines computed tomography (CT) images and morphological characteristics of pulmonary nodules. The network is designed to acquire clinically relevant deep learning features, thereby elevating the diagnostic accuracy of existing models. Importantly, the network’s simple architecture and use of standard screening variables enable seamless integration into standard lung cancer screening protocols. Results. In summary, the study analyzed a total of 382 small pulmonary nodules (85 malignant) from the LUNA16 dataset and 101 small pulmonary nodules (33 malignant) obtained from four specialized centers in Beijing, China, for model training and external validation. Both internal and external validation metrics indicate that the MIFNN significantly surpasses extant state-of-the-art models, achieving an internal area under the curve (AUC) of 0.890 (95% CI: 0.848–0.932) and an external AUC of 0.843 (95% CI: 0.784–0.891). Conclusion. The MIFNN model significantly enhances the diagnostic accuracy of small pulmonary nodules, outperforming existing benchmarks by Zhang et al with a 6.34% improvement for nodules less than 10 mm. Leveraging advanced integration techniques for imaging and clinical data, MIFNN increases the efficiency of lung cancer screenings and optimizes nodule management, potentially reducing false positives and unnecessary biopsies. Clinical relevance statement. The MIFNN enhances lung cancer screening efficiency and patient management for small pulmonary nodules, while seamlessly integrating into existing workflows due to its reliance on standard screening variables.

Comparative analysis of the image quality and diagnostic performance of the zooming technique with diffusion-weighted imaging using different b-values for thyroid papillary carcinomas and benign nodules.

To compare image quality and diagnostic performance using different b-values for the zooming technique with diffusion-weighted imaging (ZOOMit-DWI) in thyroid nodules. A total of 51 benign thyroid nodules and 50 thyroid papillary carcinomas were included. ZOOMit-DWI was performed with b-values of 0, 500, 1000, 1500 and 2000 s/mm2. The sharpness was evaluated as subjective index. The signal intensity ratio (SIR), signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) were measured as objective indices. Pairwise comparisons were performed among the different b-value groups using the Friedman test. A receiver operating characteristic curve of the ADC value was used to evaluate diagnostic performance. The DeLong test was used to compare diagnostic effectiveness among the different b-value groups. In both the papillary carcinoma group (P = 0.670) and the benign nodule group (P = 0.185), the sharpness of nodules was similar between b-values of 1000 s/mm2and 1500 s/mm2. In the papillary carcinoma group, the SIRnodule was statistically higher in DWI images with a b-value of 1500 s/mm2than in DWI images with b-values of 500 s/mm2(P = 0.004), 1000 s/mm2(P = 0.002), and 2000 s/mm2(P = 0.003). When the b-values were 1500 s/mm2(P = 0.008) and 2000 s/mm2(P = 0.009), the SIRnodule significantly differed between the papillary carcinoma group and the benign nodule group. When b = 500 s/mm2, the ADC had an AUC of 0.888. When b = 1000 s/mm2, the ADC had an AUC of 0.881. When b = 1500 s/mm2, the ADC had an AUC of 0.896. When b = 2000 s/mm2, the ADC had an AUC of 0.871. The DeLong test showed comparable diagnostic effectiveness among the different b-value groups except for between b-values of 2000 s/mm2and 1500 s/mm2, with a b-value of 2000 s/mm2showing lower effectiveness. This study suggests that 1500 s/mm2may be a suitable b-value to differentiate benign and malignant thyroid nodules in ZOOMit-DWI images, which yielded better image quality.

Modified systemic inflammation score for prediction of malignancy in indeterminate thyroid nodules- an effective tool in low-resource settings!

Modified systemic inflammation score for prediction of malignancy in indeterminate thyroid nodules- an effective tool in low-resource settings!

Autonomous AI-based diagnostic system for predicting malignancy in thyroid nodules

Autonomous AI-based diagnostic system for predicting malignancy in thyroid nodules

Differential diagnosis of thyroid nodules using heterogeneity quantification software on ultrasound images: correlation with the Bethesda system and surgical pathology

Ultrasonography (US)-guided fine-needle aspiration cytology (FNAC) is the primary modality for evaluating thyroid nodules. However, in cases of atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), supplemental tests are necessary for a definitive diagnosis. Accordingly, we aimed to develop a non-invasive quantification software using the heterogeneity scores of thyroid nodules. This cross-sectional study retrospectively enrolled 188 patients who were categorized into four groups according to their diagnostic classification in the Bethesda system and surgical pathology [II-benign (B) (n = 24); III-B (n = 52); III-malignant (M) (n = 54); V/VI-M (n = 58)]. Heterogeneity scores were derived using an image pixel-based heterogeneity index, utilized as a coefficient of variation (CV) value, and analyzed across all US images. Differences in heterogeneity scores were compared using one-way analysis of variance with Tukey’s test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristic (AUROC) curve. The results of this study indicated significant differences in mean heterogeneity scores between benign and malignant thyroid nodules, except in the comparison between III-M and V/VI-M nodules. Among malignant nodules, the Bethesda classification was not observed to be associated with mean heterogeneity scores. Moreover, there was a positive correlation between heterogeneity scores and the combined diagnostic category, which was based on the Bethesda system and surgical cytology grades (R = 0.639, p < 0.001). AUROC for heterogeneity scores showed the highest diagnostic performance (0.818; cut-off: 30.22% CV value) for differentiating the benign group (normal/II-B/III-B) from the malignant group (III-M/V&VI-M), with a diagnostic accuracy of 72.5% (161/122). Quantitative heterogeneity measurement of US images is a valuable non-invasive diagnostic tool for predicting the likelihood of malignancy in thyroid nodules, including AUS or FLUS.

Pitfalls and strategies of Sonazoid enhanced ultrasonography in differentiating metastatic and benign hepatic lesions.

This article aims to clarify pitfalls and find strategies for the detecting and diagnosing hyperechoic liver metastases (LMs) using Sonazoid-contrast enhanced ultrasonography (Sonazoid-CEUS). This study was a prospective self-controlled study. Patients with hepatic lesions suspected as LMs or benign lesions were included in the study. Baseline ultrasonography (BUS) and Sonazoid-CEUS were performed on every patient. Characteristics of LMs and benign nodules were compared by chi-square test and fisher test. Factors influenced the CEUS were demonstrated by univariate analysis and multivariate logistic regression analysis. 54 patients were included in this study. CEUS found additional 75 LMs from 19 patients in Kupffer phase. We found hyperechoic focal liver lesions and deep seated in liver are main confounding factors in CEUS diagnosis. Sensitivity would be improved from 16.67% to 78.57%, negative predictive value (NPV) would be improved from 28.57% to 76.92% and accuracy would be improved from 37.5% to 87.50% when using rapid "wash-in" and "wash-out" as main diagnostic criteria. Hyperechoic LMs especially deeply seated ones are usually not shown typical "black hole" sign in Kupffer phase. Quickly "wash-in and wash out" shows high accuracy in diagnosing malignant nodules. We highly recommend CEUS as a routing exam to detect and diagnose LMs.

Abstract PO3-20-10: Case Series: Invasive Secretory Carcinoma

Abstract Secretory variant invasive ductal carcinoma is not well described in the literature but is defined by its unique histopathology. These tumors stain positive for S100 and are typically triple negative hormonal cancers with an overall good prognosis. There is speculation that secretory carcinoma in adults is more aggressive with a greater likelihood for tumor recurrence. We present two case studies of this rare histopathologic diagnosis. First, a 76-year-old female with a recurrent secretory variant, invasive ductal carcinoma of the left breast. Second, a 68-year-old male who presented with invasive and in situ secretory carcinoma. A thorough chart review of all available institutional and outside institution records was performed from initial diagnosis to current treatment. Background information and de-identified patient information was summarized for this case series. This is a 76-year-old female who presented to her physician in 2011 with a palpable left breast and axillary mass. Her workup demonstrated ER+/PR+/HER2- invasive ductal carcinoma with lobular features of the left breast measuring 2.3 cm, along with a 1.5 cm malignant axillary lymph node. She underwent a left breast lumpectomy with axillary dissection. She underwent adjuvant whole breast radiation, axillary nodal radiation, and was placed on anastrozole therapy for 5 years. Seven years later a recurrence was noted in the superior aspect of the left breast. MRI guided biopsy revealed recurrent left breast cancer with pathology of invasive ductal carcinoma, secretory variant. She elected to undergo lumpectomy alone. Final pathology demonstrated invasive ductal carcinoma, secretory variant, ER negative (0%), PR negative (0%), HER2 negative (0%), pT1bNx. She underwent accelerated adjuvant partial breast radiation. Follow up and surveillance with bilateral breast MRI two years later demonstrated a new left breast mass, found to be triple negative, recurrent secretory variant invasive ductal carcinoma. She underwent definitive treatment with a left skin sparing mastectomy, right sentinel lymph node biopsy, and a latissimus dorsi flap with implant placement. Final pathology demonstrated multifocal recurrent secretory carcinoma, two foci measuring 6 and 11 mm. Our second patient was a 68-year-old male who initially presented to the clinic for evaluation of a ventral incisional hernia. During his initial assessment, a palpable retro-areolar nodule was noted on physical exam. Subsequent mammography demonstrated a hypoechoic mass with irregular margins measuring 2.8 x 2.3 x 1.7 cm along with increased vascularity and calcifications. An ultrasound guided biopsy demonstrated invasive secretory carcinoma. Initial pathologic testing of the tumor demonstrated ER positive (5%), PR positive (1%), and HER2 negative. The patient was taken to the operating room for a total mastectomy with sentinel lymph node biopsy. His postoperative pathology demonstrated negative margins, and invasive secretory carcinoma with lymph nodes negative for metastatic disease. Secretory variant of invasive ductal carcinoma is rare and is determined by its unique immunohistochemical features. We present two cases of secretory carcinoma at our institution. Data suggests that local recurrence is common in patients with secretory breast cancer, occurring in 33-44% of cases. Ki-67 index has been used as a prognostic indicator for metastases and recurrence of disease. Current treatment regimens are diverse; however, surgery is favored as the primary method. This report shows that although indolent, multifocality and recurrence is plausible. Given the limited data on this subset of carcinoma, more investigation is warranted to describe the effectiveness of adjuvant treatment options. Case 1 –insert figure image– Histopathologic examination at 200x shows invasive carcinoma with dense eosinophilic secretory material. By immunohistochemistry, the cells are negative for ER (0%) and PR (0%) with a HER2Neu score of 0, supporting the diagnosis of invasive secretory carcinoma. Case 2 –insert figure image– Histopathologic examination at 200x shows invasive carcinoma with dense eosinophilic secretory material. The tumor cells are positive for CK5/6 (patchy), CD117, ER (focal and weakly), and S100; negative for p63 and calponin, supporting the diagnosis of invasive secretory carcinoma. NTRK3 gene rearrangement was detected on fluorescence in situ hybridization (FISH). Citation Format: Lauren Geisel, Hemanth Venkatesh, Alyssa Obermiller, Quyen Chu, Jessica Gielow, Danielle Henry. Case Series: Invasive Secretory Carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-20-10.

Lung Cancer Detection Using a Modified Convolutional Neural Network (CNN)

&lt;p&gt;&lt;span&gt;Image processing is used to classify lung images with malignant or normal nodules. The Convolutional Neural Network (CNN) method is often used to classify images. This study uses a modified CNN architecture with various layers, filters, batch size, dropout, and epoch values. Variations were made to determine the best accuracy value and reduce the overfitting value of the proposed CNN architecture. This study implements the method using the Keras library with the Python programming language. The data is in the form of CT-Scan images of lung cancer and normal lungs. The results of several experiments from the proposed model produce an accuracy value of 95% using three layers, 128 filters on the first layer, 256 on the second layer, and 512 filters on the third layer, then with 32 batch sizes, 0.5 dropout.&lt;/span&gt;&lt;/p&gt;

Diagnosis of Medical Images Using Convolutional Neural Networks

Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.

Congestive Hepatopathy: Pathophysiology, Workup, and Imaging Findings with Pathologic Correlation.

Liver congestion is increasingly encountered in clinical practice and presents diagnostic pitfalls of which radiologists must be aware. The complex altered hemodynamics associated with liver congestion leads to diffuse parenchymal changes and the development of benign and malignant nodules. Distinguishing commonly encountered benign hypervascular lesions, such as focal nodular hyperplasia (FNH)-like nodules, from hepatocellular carcinoma (HCC) can be challenging due to overlapping imaging features. FNH-like lesions enhance during the hepatic arterial phase and remain isoenhancing relative to the background liver parenchyma but infrequently appear to wash out at delayed phase imaging, similar to what might be seen with HCC. Heterogeneity, presence of an enhancing capsule, washout during the portal venous phase, intermediate signal intensity at T2-weighted imaging, restricted diffusion, and lack of uptake at hepatobiliary phase imaging point toward the diagnosis of HCC, although these features are not sensitive individually. It is important to emphasize that the Liver Imaging Reporting and Data System (LI-RADS) algorithm cannot be applied in congested livers since major LI-RADS features lack specificity in distinguishing HCC from benign hypervascular lesions in this population. Also, the morphologic changes and increased liver stiffness caused by congestion make the imaging diagnosis of cirrhosis difficult. The authors discuss the complex liver macro- and microhemodynamics underlying liver congestion; propose a more inclusive approach to and conceptualization of liver congestion; describe the pathophysiology of liver congestion, hepatocellular injury, and the development of benign and malignant nodules; review the imaging findings and mimics of liver congestion and hypervascular lesions; and present a diagnostic algorithm for approaching hypervascular liver lesions. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model

IntroductionEarly diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN). MethodologyProspective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes’ theorem. ResultsThe radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80. ConclusionsIncorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.