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PURPOSE: A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study. METHODS: A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis. RESULTS: The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis. CONCLUSION: This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.
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Inteligencia Artificial , Axila , Neoplasias de la Mama , Ganglios Linfáticos , Metástasis Linfática , Humanos , Femenino , Neoplasias de la Mama/patología , Neoplasias de la Mama/diagnóstico por imagen , Metástasis Linfática/diagnóstico por imagen , Metástasis Linfática/patología , Persona de Mediana Edad , Estudios Retrospectivos , Axila/diagnóstico por imagen , Adulto , Ganglios Linfáticos/patología , Ganglios Linfáticos/diagnóstico por imagen , Ultrasonografía/métodos , Anciano , Aprendizaje Profundo , Biopsia del Ganglio Linfático Centinela/métodos , Curva ROC , Redes Neurales de la Computación , Valor Predictivo de las PruebasRESUMEN
BACKGROUND: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method. PURPOSE: To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI). STUDY TYPE: Cross-sectional retrospective cohort study. POPULATION: The study included 206 BC patients, with 136 in the training set [97 LVI(-) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(-) and 18 LVI(+) cases; median age: 48 years]. FIELD STRENGTH/SEQUENCE: 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, diffusion-weighted imaging (DWI), and DCE-MRI. ASSESSMENT: The MRI-MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE-MRI datasets, specifically the first and second post-contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI-MF, Radiomics, and DL approaches. STATISTICAL TESTS: Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis. RESULTS: Rim sign and peritumoral edema features were used to develop the MRI-MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI-MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI-MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI-MF (AUC = 0.796), DL + MRI-MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826). DATA CONCLUSION: The joint model incorporating MRI-MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.
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Large field-of-view optical imaging systems often face challenges in the presence of space-variant degradation. The existence of degradation leads to target detection and recognition being difficult or even unsuccessful. To address this issue, this paper proposes an adaptive anisotropic pixel-by-pixel space-variant correction method. First, we estimated region acquisition of local space-variant point spread functions (PSFs) based on Haar wavelet degradation degree distribution, and obtained initial PSF matrix estimation with inverse distance weighted spatial interpolation. Then, we established a pixel-by-pixel space-variant correction model based on the PSF matrix. Third, we imposed adaptive sparse regularization terms of the Haar wavelet based on the adaptive anisotropic iterative reweight strategy and non-negative regularization terms as the constraint in the pixel-by-pixel space-variant correction model. Finally, as the correction process is refined to each pixel, the split-Bregman multivariate separation solution algorithm was employed for the pixel-by-pixel spare-variant correction model to estimate the final PSF matrix and the gray value of each pixel. Through this algorithm, the "whole image correction" and "block correction" is avoided, the "pixel-by-pixel correction" is realized, and the final corrected images are obtained. Experimental results show that compared with the current advanced correction methods, the proposed approach in the space-variant wide field correction of a degraded image shows better performance in preserving the image details and texture information.
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BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. PURPOSE: To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). MATERIAL AND METHODS: In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland-Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. RESULTS: The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 (P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group (P < 0.001). CONCLUSION: Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.
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Adenocarcinoma del Pulmón , Adenocarcinoma , Aprendizaje Profundo , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Estudios Retrospectivos , Invasividad Neoplásica , Adenocarcinoma del Pulmón/patología , Adenocarcinoma/patología , Tomografía Computarizada por Rayos X/métodos , AlgoritmosRESUMEN
BACKGROUND: To investigate retrospectively an association between the number of metastatic sentinel lymph nodes (SLNs) per total number of SLNs per patient (i.e., the SLN positive rate, or SLN-PR) and non-SLN metastasis in breast cancer. METHODS: A large population (n = 2250) underwent SLN dissection from January 1, 2014 to January 1, 2020; 627 (27.87%) had at least one positive SLN (SLN+). Among these, 283 underwent axillary lymph node (ALN) dissection, and formed the test group. Four external validation groups comprised 43 patients treated in 2019. SLN mappings were examined using methylene blue and indocyanine green. Lymph node ultrasound, SLN-PR, and pathological characteristics were compared between patients with and without non-SLN metastasis. An SLN-PR cutoff value was calculated using receiver operating characteristic (ROC) curves. Associations between clinicopathological variables and SLN-PR with non-SLN metastasis were analyzed by multivariate logistic regression model. RESULTS: The median age was 47 years (IQR: 42-56 y). The median number of resected SLNs was 4. Patients with positive non-SLNs (126/283, 44.52%) had a median of 2 positive node. SLN-PR > 0.333 was a risk factor for non-SLN positivity (area under the ROC curve, 0.726); and carried significantly higher risk of non-SLN metastasis (P < 0.001). This was validated in the external group. CONCLUSIONS: SLN-PR > 0.333 was associated with greater risk of non-SLN metastasis. This provides a reference to non-SLN metastasis in patients with SLN metastasis, an indication for ALN dissection and choice of adjuvant treatment.
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Neoplasias de la Mama , Ganglio Linfático Centinela , Axila/patología , Neoplasias de la Mama/patología , Neoplasias de la Mama/cirugía , Femenino , Humanos , Escisión del Ganglio Linfático , Ganglios Linfáticos/patología , Ganglios Linfáticos/cirugía , Metástasis Linfática/patología , Persona de Mediana Edad , Estudios Retrospectivos , Ganglio Linfático Centinela/patología , Ganglio Linfático Centinela/cirugía , Biopsia del Ganglio Linfático CentinelaRESUMEN
OBJECTIVE: This study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with stage IB non-small-cell lung cancer (NSCLC). METHODS: In total, 5513 patients with stage IB NSCLC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and used as the training cohort. We enrolled 440 patients from the Cancer Hospital, Chinese Academy of Medical Sciences, for the external validation cohort. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. The discrimination and calibration of the nomogram were evaluated by C-indexes and calibration curves. RESULTS: Six independent risk factors for OS were identified and integrated into the nomogram. The discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.637 (95% CI 0.634-0.641) and 0.667 (95% CI 0.656-0.678) for the training cohort and the external validation cohort, respectively. Additionally, the patients were divided into two groups according to risk (sum-score > 185), and significant differences in OS were observed between the high-risk and low-risk groups in the training and external validation cohorts (P < 0.001). Finally, chemotherapy was significantly associated with OS in patients with differentiation grades II-IV (P = 0.004) and patients with adenocarcinoma (P = 0.005). CONCLUSION: This nomogram provides a convenient and reliable tool for individual evaluations and clinical decision-making for patients with stage IB NSCLC; among these patients, those with differentiation grades II-IV or adenocarcinoma could benefit from chemotherapy.
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Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/patología , Estadificación de Neoplasias , Nomogramas , Pronóstico , Programa de VERFRESUMEN
OBJECTIVE: To explore the prognostic significance of tumor deposits (TDs), isolated tumor foci lacking residual lymph nodes, in esophageal cancer (EC). METHODS: A retrospective review of patients with EC undergoing esophagectomy between 2005 and 2017 was conducted. The prognostic value of TD was evaluated using a Cox regression model. Patients from different sources and periods were split into discovery and validation sets. A propensity score matching model was used in the validation set to reduce the confounding bias. The impact of TD on the TNM classification system was evaluated. RESULTS: The discovery and validation sets included 179 and 2875 patients, respectively. Propensity-matched patients with and without TDs were constructed in the validation set with 132 patients in each group. Overall survival (p < .001 and p = .004, respectively) and disease-free survival (p < .001 and p = .019, respectively) were both decreased in TD positive patients in the discovery set and propensity-matched groups of validation set. Classifying patients with TDs into pN3 stage improved the discriminative power of the current TNM staging system. CONCLUSIONS: TD is an independent prognostic factor for EC. The inclusion of TD in the TNM staging system may upstage appropriate patients to help guide therapy, and future studies are warranted.
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Adenocarcinoma/patología , Neoplasias Esofágicas/patología , Esofagectomía/mortalidad , Ganglios Linfáticos/patología , Estadificación de Neoplasias/normas , Adenocarcinoma/cirugía , Adulto , Anciano , Anciano de 80 o más Años , Neoplasias Esofágicas/cirugía , Extensión Extranodal , Femenino , Estudios de Seguimiento , Humanos , Ganglios Linfáticos/cirugía , Masculino , Persona de Mediana Edad , Pronóstico , Estudios Retrospectivos , Tasa de SupervivenciaRESUMEN
BACKGROUND The aim of this study was to investigate the diagnostic value of diffusion-weighted imaging (DWI) in combination with susceptibility-weighted imaging (SWI) for differentiating benign parotid gland lesions from malignant ones. MATERIAL AND METHODS This retrospective study was approved by the Ethics Committee of our hospital. A total of 36 patients (26 benign cases and 10 malignant cases) were confirmed by surgical pathology. The apparent diffusion coefficient (ADC), normalized ADC (ADCNormalized), intratumoral susceptibility signals (ITSS), and morphological characteristics were analyzed with SPSS 19.0 software. RESULTS The mean ADC values of parotid gland lesions was not different between malignant and benign lesions (P=0.07), while the differences between ADCNormalized (P=0.026) and ITSS grading (P=0.014) were statistically significant. Logistic regression analysis identified use of ADCNormalized and ITSS as the only independent predictor of malignant lesions (odds ratio 0.038; 95% confidence interval 0.001~0.988; P=0.011) and (odds ratio 4.867; 95% confidence interval 1.442~16.423; P=0.049), respectively. The optimum threshold of the ADCNormalized values was -0.45%, ITSS grade was 2, the corresponding areas under the receiver operating characteristic curve (AUC) were 0.750 and 0.787 respectively, and the combination of the 2 was 0.846. CONCLUSIONS DWI integrated with SWI can significantly improve the diagnostic efficacy in distinguishing benign from malignant parotid lesions.
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Imagen de Difusión por Resonancia Magnética/métodos , Glándula Parótida/diagnóstico por imagen , Adulto , Anciano , Anciano de 80 o más Años , Diagnóstico Diferencial , Femenino , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Persona de Mediana Edad , Glándula Parótida/patología , Estudios Retrospectivos , Sensibilidad y EspecificidadRESUMEN
PURPOSE: To determine the value of non-enhanced MRI in combination with color Doppler flow imaging (CDFI) for differentiating malignant parotid tumors from benign ones. METHODS: This retrospective study analyzed 51 parotid gland lesions (39 benign and 12 malignant) in 51 patients who underwent preoperative CDFI as well as non-enhanced MRI including T1-weighted, T2-weighted, and diffusion-weighted imaging (DWI). Degrees of intratumor vascularity were categorized into four grades basing on CDFI findings. The relationships between the lesion and its adjacent external carotid artery and retromandibular vein were inspected on T1-weighted and T2-weighted images. Apparent diffusion coefficient (ADC) values were calculated from diffusion-weighted images, and were used to classify the parotid gland lesions with and without reference to the CDFI findings. The classification results were compared using the McNemar test. Sensitivity, specificity, and accuracy percentages were calculated when the non-enhanced MRI/CDFI findings were used to differentiate benign lesions from malignant ones. RESULTS: The diagnostic accuracy (96.1 vs 82.4%) was significantly improved when ADCs were used together with CDFI findings for classifying parotid gland lesions compared to when ADCs were used alone. Pleomorphic adenomas had the highest ADCs. The ADC thresholds were 1.425 × 10-3 mm2/s for differentiating pleomorphic adenomas from carcinomas, 0.999 × 10-3 mm2/s for differentiating pleomorphic adenomas from other benign lesions, and 0.590 × 10-3 mm2/s for differentiating benign lesions other than pleomorphic adenomas from lymphomas. CONCLUSION: Combining CDFI with non-enhanced MRI can improve the diagnostic accuracy of MRI for classifying parotid gland lesions.
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Carcinoma , Glándula Parótida/diagnóstico por imagen , Neoplasias de la Parótida , Ultrasonografía Doppler en Color/métodos , Adulto , Anciano , Carcinoma/diagnóstico , Carcinoma/patología , Diagnóstico Diferencial , Imagen de Difusión por Resonancia Magnética/métodos , Precisión de la Medición Dimensional , Femenino , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Persona de Mediana Edad , Glándula Parótida/patología , Neoplasias de la Parótida/diagnóstico , Neoplasias de la Parótida/patología , Estudios Retrospectivos , Sensibilidad y EspecificidadRESUMEN
RATIONALE AND OBJECTIVES: To quantify intratumor heterogeneity (ITH) in clinical T1 stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGN) on computed tomography, assessing its value in distinguishing histological subtypes. MATERIALS AND METHODS: An ITH score was developed for quantitative measurement by integrating local radiomics features and global pixel distribution patterns. Diagnostic efficacy in distinguishing histological subtypes was evaluated using receiver operating characteristic curve analysis and area under the curve (AUC) values. The ITH score's performance was compared to those of conventional radiomics (C-radiomics), and radiological assessments conducted by experienced radiologists. RESULTS: The ITH score demonstrated excellent performance in distinguishing lepidic-predominant adenocarcinoma (LPA) from other histological subtypes of clinical T1 stage lung adenocarcinoma presenting as pGGN. It outperformed both C-radiomics and radiological findings, exhibiting higher AUCs of 0.784 (95% confidence interval [CI]: 0.742-0.826) and 0.801 (95% CI: 0.739-0.863) in the training and validation cohorts, respectively. The AUCs of C-radiomics were 0.764 (95% CI: 0.718-0.810, DeLong test, p = 0.025) and 0.760 (95% CI: 0.692-0.829, p = 0.023) and those of radiological findings were 0.722 (95% CI: 0.673-0.771, p = 0.003) and 0.754 (95% CI: 0.684-0.823, p = 0.016) in the training and validation cohorts, respectively. Subgroup analysis revealed varying diagnostic efficacy across clinical T1 stages, with the highest efficacy in the T1a stage, followed by the T1b stage, and lowest in the T1c stage. CONCLUSION: The ITH score presents a superior method for evaluating histological subtypes and distinguishing LPA from other subtypes in clinical T1 stage lung adenocarcinoma presenting as pGGN.
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RATIONALE AND OBJECTIVES: Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC. MATERIALS AND METHODS: In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05. RESULTS: GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set. CONCLUSION: The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.
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BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (PFS) in patients with non-metastatic NPC. METHODS: A cohort of 653 patients with non-metastatic NPC was divided into a training (n = 457) and validation (n = 196) dataset (7:3 ratio). The study included clinicopathological characteristics, hematologic markers, and MRI findings in three machine learning models-random forest (RF), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO)-to predict progression-free survival (PFS). A Venn diagram identified the overlapping signatures from the three ML algorithms. Cox proportional hazard analysis determined the MLDPS for PFS. RESULTS: The RF, XGBoost, and LASSO algorithms identified six consensus factors from the 33 signatures. Cox proportional hazards analysis showed that the MLDPS includes age, lymphocyte count, number of positive lymph nodes, and regional lymph node density. Additionally, MLDPS effectively stratified prognosis, with low-risk individuals showing better PFS than high-risk individuals (p < 0.001). CONCLUSION: MLDPS, based on clinicopathological characteristics, hematologic markers, and MRI findings, is crucial for guiding clinical management and personalizing treatments for patients with non-metastatic NPC.
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Objective: This study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). Methods: This study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018-July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. Results: A total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555-0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. Conclusion: The combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.
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OBJECTIVE: To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. METHODS: We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. RESULTS: The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. CONCLUSION: This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.
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Neoplasias de la Mama , Medios de Contraste , Imagen por Resonancia Magnética , Invasividad Neoplásica , Nomogramas , Radiómica , Adulto , Anciano , Femenino , Humanos , Persona de Mediana Edad , Mama/diagnóstico por imagen , Mama/patología , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/patología , Metástasis Linfática/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Invasividad Neoplásica/diagnóstico por imagen , Reproducibilidad de los Resultados , Estudios RetrospectivosRESUMEN
OBJECTIVES: To meet the demand for personalized treatment, effective stratification of patients with metastatic nasopharyngeal carcinoma (mNPC) is essential. Hence, our study aimed to establish an M1 subdivision for prognostic prediction and treatment planning in patients with mNPC. MATERIALS AND METHODS: This study included 1239 patients with mNPC from three medical centers divided into the synchronous mNPC cohort (smNPC, n = 556) to establish an M1 stage subdivision and the metachronous mNPC cohort (mmNPC, n = 683) to validate this subdivision. The primary endpoint was overall survival. Univariate and multivariate Cox analyses identified covariates for the decision-tree model, proposing an M1 subdivision. Model performance was evaluated using time-dependent receiver operating characteristic curves, Harrell's concordance index, calibration plots, and decision curve analyses. RESULTS: The proposed M1 subdivisions were M1a (≤5 metastatic lesions), M1b (>5 metastatic lesions + absent liver metastases), and M1c (>5 metastatic lesions + existing liver metastases) with median OS of 34, 22, and 13 months, respectively (p < 0.001). This M1 subdivision demonstrated superior discrimination (C-index = 0.698; 3-year AUC = 0.707) and clinical utility over those of existing staging systems. Calibration curves exhibited satisfactory agreement between predictions and actual observations. Internal and mmNPC cohort validation confirmed the robustness. Survival benefits from local metastatic treatment were observed in M1a, while immunotherapy improved survival in patients with M1b and M1c disease. CONCLUSION: This novel M1 staging strategy provides a refined approach for prognostic prediction and treatment planning in patients with mNPC, emphasizing the potential benefits of local and immunotherapeutic interventions based on individualized risk stratification.
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Árboles de Decisión , Carcinoma Nasofaríngeo , Humanos , Masculino , Femenino , Persona de Mediana Edad , Carcinoma Nasofaríngeo/patología , Carcinoma Nasofaríngeo/mortalidad , Carcinoma Nasofaríngeo/terapia , Estudios Retrospectivos , Adulto , Estadificación de Neoplasias , Neoplasias Nasofaríngeas/patología , Neoplasias Nasofaríngeas/terapia , Neoplasias Nasofaríngeas/mortalidad , Pronóstico , AncianoRESUMEN
OBJECTIVE: We investigated the efficacy of metastatic lesion radiotherapy (MLRT) in patients with metastatic nasopharyngeal carcinoma (mNPC). MATERIALS AND METHODS: Patients with mNPC from three institutions were included in this study. Propensity score matching (PSM) was employed to ensure comparability between patient groups. Overall survival (OS) rates were assessed using the Kaplan-Meier method and compared using the log-rank test. Prognostic factors were identified using univariate and multivariate Cox hazard analyses. Subgroup analyses were conducted to assess the effects of MLRT on specific patient populations. RESULTS: We analyzed data from 1157 patients with mNPC. Patients who received MLRT had significantly better OS than those who did not, both in the original (28 vs. 21 months) and PSM cohorts (26 vs. 23 months). MLRT was identified as an independent favorable predictor of OS in multivariate analyses, with hazard ratios of 0.67. The subgroup analysis results indicated that radiotherapy effectively treated liver, lung, and bone metastatic lesions, particularly in patients with a limited tumor burden. Higher total radiation doses of MLRT (biologically effective dose (BED) ≥ 56 Gy) were associated with improved OS, while neither radiation technique nor dose fractionation independently influenced prognosis. CONCLUSIONS: MLRT offers survival advantages to patients diagnosed with mNPC. Patients with limited metastatic burden derive the most benefit from MLRT, and the recommended regimen for MLRT is a minimum BED of 56 Gy for optimal outcomes.
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Carcinoma , Carcinoma Nasofaríngeo , Neoplasias Nasofaríngeas , Humanos , Masculino , Femenino , Estudios Retrospectivos , Persona de Mediana Edad , Neoplasias Nasofaríngeas/radioterapia , Neoplasias Nasofaríngeas/patología , Neoplasias Nasofaríngeas/mortalidad , Carcinoma Nasofaríngeo/radioterapia , Carcinoma Nasofaríngeo/patología , Carcinoma Nasofaríngeo/mortalidad , Carcinoma/radioterapia , Carcinoma/secundario , Carcinoma/mortalidad , Adulto , Anciano , Puntaje de Propensión , Pronóstico , Tasa de Supervivencia , Neoplasias Óseas/secundario , Neoplasias Óseas/radioterapia , Neoplasias Óseas/mortalidad , Neoplasias Pulmonares/radioterapia , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/mortalidad , Resultado del Tratamiento , Neoplasias Hepáticas/secundario , Neoplasias Hepáticas/radioterapia , Neoplasias Hepáticas/mortalidadRESUMEN
Background: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology. Methods: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters. Results: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively). Conclusions: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.
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Background: Myelin oligodendrocyte glycoprotein antibody disease (MOGAD) is a newly defined autoimmune inflammatory demyelinating central nervous system (CNS) disease characterized by antibodies against MOG. Leptomeningeal enhancement (LME) on contrast-enhanced fluid-attenuated inversion recovery (CE-FLAIR) images has been reported in patients with other diseases and interpreted as a biomarker of inflammation. This study retrospectively analyzed the prevalence and distribution of LME on CE-FLAIR images in children with MOG antibody-associated encephalitis (MOG-E). The corresponding magnetic resonance imaging (MRI) features and clinical manifestations are also presented. Methods: The brain MRI images (native and CE-FLAIR) and clinical manifestations of 78 children with MOG-E between January 2018 and December 2021 were analyzed. Secondary analyses evaluated the relationship between LME, clinical manifestations, and other MRI measures. Results: Forty-four children were included, and the median age at the first onset was 70.5 months. The prodromal symptoms were fever, headache, emesis, and blurred vision, which could be progressively accompanied by convulsions, decreased level of consciousness, and dyskinesia. MOG-E showed multiple and asymmetric lesions in the brain by MRI, with varying sizes and blurred edges. These lesions were hyperintense on the T2-weighted and FLAIR images and slightly hypointense or hypointense on the T1-weighted images. The most common sites involved were juxtacortical white matter (81.8%) and cortical gray matter (59.1%). Periventricular/juxtaventricular white matter lesions (18.2%) were relatively rare. On CE-FLAIR images, 24 (54.5%) children showed LME located on the cerebral surface. LME was an early feature of MOG-E (P = 0.002), and cases without LME were more likely to involve the brainstem (P = 0.041). Conclusion: LME on CE-FLAIR images may be a novel early marker among patients with MOG-E. The inclusion of CE-FLAIR images in MRI protocols for children with suspected MOG-E at an early stage may be useful for the diagnosis of this disease.
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Encefalitis , Humanos , Glicoproteína Mielina-Oligodendrócito , Estudios Retrospectivos , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Imagen por Resonancia Magnética/métodos , AnticuerposRESUMEN
BACKGROUND OR PURPOSE: A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status. METHODS: Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models. RESULTS: In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability. CONCLUSION: This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.
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Neoplasias de la Mama , Metástasis Linfática , Ganglio Linfático Centinela , Femenino , Humanos , Neoplasias de la Mama/cirugía , Neoplasias de la Mama/patología , Escisión del Ganglio Linfático , Ganglios Linfáticos/patología , Metástasis Linfática/patología , Nomogramas , Estudios Retrospectivos , Ganglio Linfático Centinela/patología , Biopsia del Ganglio Linfático CentinelaRESUMEN
BACKGROUND: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening. MATERIALS AND METHODS: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital. RESULTS: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals. CONCLUSIONS: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.