Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

Background: The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score. Results: The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63). Conclusions: The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.

Investigation of correlation between shear wave elastography and lymphangiogenesis in invasive breast cancer and diagnosis of axillary lymph node metastasis.

BACKGROUND: Accurate evaluation of axillary lymph node metastasis (LNM) in breast cancer is very important. A large number of hyperplastic and dilated lymphangiogenesis cases can usually be found in the pericancerous tissue of breast cancer to promote the occurrence of tumor metastasis.Shear wave elastography (SWE) can be used as an important means for evaluating pericancerous stiffness. We determined the stiffness of the pericancerous by SWE to diagnose LNM and lymphangiogenesis in invasive breast cancer (IBC). METHODS: Patients with clinical T1-T2 stage IBC who received surgical treatment in our hospital from June 2020 to December 2020 were retrospectively enrolled. A total of 299 patients were eventually included in the preliminary study, which included an investigation of clinicopathological features, ultrasonic characteristics, and SWE parameters. Multivariable logistic regression analysis was used to establish diagnostic model and evaluated its diagnostic performance of LNM. The correlation among SWE values, collagen volume fraction (CVF), and microlymphatic density (MLD) in primary breast cancer lesions was analyzed in another 97 patients. RESULTS: The logistic regression model is Logit(P)=-1.878 + 0.992*LVI-2.010*posterior feature enhancement + 1.230*posterior feature shadowing + 0.102*posterior feature combined pattern + 0.009*Emax. The optimum cutoff value of the logistic regression model was 0.365, and the AUC (95% CI) was 0.697 (0.636-0.758); the sensitivity (70.7 vs. 54.3), positive predictive value (PPV) (54.0 vs. 50.8), negative predictive value (NPV) (76.9 vs. 69.7), and accuracy (65.2 vs. 61.9) were all higher than Emax. There was no correlation between the SWE parameters and MLD in primary breast cancer lesions. CONCLUSIONS: The logistic regression model can help us to determine LNM, thus providing more imaging basis for the selection of preoperative treatment. The SWE parameter of the primary breast cancer lesion cannot reflect the peritumoral lymphangiogenesis, and we still need to find a new ultrasonic imaging method.

Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study.

BACKGROUND: Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance. METHODS: From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity. RESULTS: In total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P < 0.001) by using the AI model for assistance. CONCLUSIONS: The AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists. TRIAL REGISTRATION: We registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

BACKGROUND: The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. METHODS: Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. RESULTS: DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). CONCLUSIONS: DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network.

Purpose To develop a custom deep convolutional neural network (CNN) for noninvasive prediction of breast cancer nodal metastasis. Materials and Methods This retrospective study included patients with newly diagnosed primary invasive breast cancer with known pathologic (pN) and clinical nodal (cN) status who underwent dynamic contrast-enhanced (DCE) breast MRI at the authors' institution between July 2013 and July 2016. Clinicopathologic data (age, estrogen receptor and human epidermal growth factor 2 status, Ki-67 index, and tumor grade) and cN and pN status were collected. A four-dimensional (4D) CNN model integrating temporal information from dynamic image sets was developed. The convolutional layers learned prognostic image features, which were combined with clinicopathologic measures to predict cN0 versus cN+ and pN0 versus pN+ disease. Performance was assessed with the area under the receiver operating characteristic curve (AUC), with fivefold nested cross-validation. Results Data from 350 female patients (mean age, 51.7 years ± 11.9 [SD]) were analyzed. AUC, sensitivity, and specificity values of the 4D hybrid model were 0.87 (95% CI: 0.83, 0.91), 89% (95% CI: 79%, 93%), and 76% (95% CI: 68%, 88%) for differentiating pN0 versus pN+ and 0.79 (95% CI: 0.76, 0.82), 80% (95% CI: 77%, 84%), and 62% (95% CI: 58%, 67%), respectively, for differentiating cN0 versus cN+. Conclusion The proposed deep learning model using tumor DCE MR images demonstrated high sensitivity in identifying breast cancer lymph node metastasis and shows promise for potential use as a clinical decision support tool. Keywords: MR Imaging, Breast, Breast Cancer, Breast MRI, Machine Learning, Metastasis, Prognostic Prediction Supplemental material is available for this article. Published under a CC BY 4.0 license.

Diagnosis of invasive cutaneous squamous cell carcinoma, imaging and staging.

The assessment of patients with a lesion raising the suspicion of an invasive cutaneous squamous cell carcinoma (cSCC) is a frequent clinical scenario. The management of patients with cSCC is a multistep approach, starting with the correct diagnosis. The two main diagnostic goals are to differentiate from other possible diagnoses and correctly recognize the lesion as cSCC, and then to determine the tumor spread (perform staging), that is if the patient has a common primary cSCC or a locally advanced cSCC, or a metastatic cSCC (with in-transit, regional lymph nodal, or rarely distant metastasis). The multistep diagnostic approach begins with the clinical characteristics of the primary cSCC, it is complemented with features with dermoscopy and, if available, reflectance confocal microscopy and is confirmed with histopathology. The tumor spread is assessed by physical examination and, in some cases, ultrasound and/or computed tomography or magnetic resonance imaging, mainly to investigate for regional lymph node metastasis or for local infiltration into deeper structures. In the last step, the clinical, histologic and radiologic findings are incorporated into staging systems.

Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study.

Background: In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Methods: Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. Results: A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. Conclusions: The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

[99mTc]Tc-HYNIC-ALUG SPECT/CT in the initial staging of 227 consecutive patients with newly diagnosed prostate cancer: a 5-year monocentric retrospective study.

Purpose: The purpose of this study was to assess the effectiveness of [99mTc]Tc-HYNIC-ALUG SPECT/CT in the initial staging of patients with newly diagnosed PCa. Methods: A retrospective analysis was conducted on 227 consecutive patients who underwent [99mTc]Tc-HYNIC-ALUG SPECT/CT imaging for the primary staging of newly diagnosed PCa. The presence and location of PSMA-positive lesions were determined, and the maximum standardized uptake values (SUVmax) of the primary prostate tumor were also measured. The metastatic findings and SUVmax were stratified according to International Society of Urological Pathology (ISUP) grade, prostate-specific antigen (PSA) levels, and D'Amico classification. Furthermore, the [99mTc]Tc-HYNIC-ALUG SPECT/CT findings were compared to the histopathological findings in patients who had undergone radical prostatectomy with pelvic lymph node dissection (PLND). Results: Of the 227 patients, 92.1% (209/227) had positive [99mTc]Tc-HYNIC-ALUG SPECT/CT findings. Advanced disease was detected in 38.8% (88/227) of the patients and was positively correlated with increasing ISUP grade and PSA levels. Lymph node metastases (both pelvic and extrapelvic), bone metastases, and visceral metastases were detected in 30.0% (68/227), 25.6% (58/227), and 3.1% (7/227) of the patients, respectively. For the 129 patients who underwent radical prostatectomy with PLND, the sensitivity of [99mTc]Tc-HYNIC-ALUG SPECT/CT in the evaluation of PCa was 90.7% (117/129). The sensitivity, specificity, accuracy, and positive and negative predictive values for detecting pelvic lymph node metastases on [99mTc]Tc-HYNIC-ALUG SPECT/CT were 23.5% (12/51), 93.6% (73/78), 65.9% (85/129), 70.6% (12/17), and 65.2% (73/112), respectively. Among the 209 patients with PSMA-avid primary prostate disease, the SUVmax of the primary prostate tumor was significantly associated with ISUP grade (p<0.0001), PSA levels (p<0.0001), D'Amico classification (p<0.0001), and advanced disease (p<0.0001). Receiver operating characteristic (ROC) analysis revealed that a PSA level >19.8 ng/ml and SUVmax of the primary prostate tumor >7.4 had a sensitivity of 71.6% and 71.6% and specificity of 76.9% and 82.6%, respectively, for detecting metastatic disease. Conclusions: [99mTc]Tc-HYNIC-ALUG SPECT/CT emerges as a valuable imaging tool for the initial staging of newly diagnosed PCa. The presence of advanced disease and the SUVmax of the primary prostate tumor were positively correlated with ISUP grade and PSA levels.

Local imaging to interpret tumor size in F18 fluorodeoxyglucose positron emission tomography/CT in lung cancers.

OBJECTIVE: This study aimed to determine the thoracic and extra-thoracic extension of the disease in patients diagnosed with lung cancer and who had whole-body F18-fluorodeoxyglucose positron emission tomography/CT imaging and to investigate whether there is a relationship between tumor size and extrathoracic spread. METHODS: A total of 308 patients diagnosed with lung cancer were included in this study. These 308 patients were first classified as group 1 (SPN 30 mm>longest lesion diameter ≥10 mm) and group 2 (lung mass (longest lesion diameter ≥30 mm), and then the same patients were classified as group 3 (nodular diameter of ≤20 mm) and group 4 (nodular size of >20 mm). Group 1 was compared with group 2 in terms of extrathoracic metastases. Similarly, group 3 was compared with group 4 in terms of frequency of extrathoracic metastases. F18 fluorodeoxyglucose positron emission tomography/CT examination was used to detect liver, adrenal, bone, and supraclavicular lymph node metastasis, besides extrathoracic metastasis. RESULTS: Liver, bone, and extrathoracic metastasis in group 1 was statistically lower than in group 2 (p<0.001, p<0.01, and p=0.03, respectively). Liver, extrathoracic, adrenal, and bone metastasis in group 3 was statistically lower than that in group 4 (p<0.001, p=0.01, and p=0.04, p<0.01, respectively). The extrathoracic extension was observed in only one patient in group 3. In addition, liver, adrenal, and bone metastases were not observed in group 3 patients. CONCLUSION: Positron emission tomography/CT may be more appropriate for cases with a nodule diameter of ≤20 mm. Performing local imaging in patients with a nodule diameter of ≤20 mm could reduce radiation exposure and save radiopharmaceuticals used in positron emission tomography/CT imaging.

68Ga-FAPI-04 PET/CT in Non-Small Cell Lung Cancer: Accurate Evaluation of Lymph Node Metastasis and Correlation with Fibroblast Activation Protein Expression.

Fibroblast activation protein (FAP) is a promising diagnostic and therapeutic target in various solid tumors. This study aimed to assess the diagnostic efficiency of 68Ga-labeled FAP inhibitor (FAPI)-04 PET/CT for detecting lymph node metastasis in non-small cell lung cancer (NSCLC) and to investigate the correlation between tumor 68Ga-FAPI-04 uptake and FAP expression. Methods: We retrospectively enrolled 136 participants with suspected or biopsy-confirmed NSCLC who underwent 68Ga-FAPI-04 PET/CT for initial staging. The diagnostic performance of 68Ga-FAPI-04 for the detection of NSCLC was evaluated. The final histopathology or typical imaging features were used as the reference standard. The SUVmax and SUVmean, 68Ga-FAPI-avid tumor volume (FTV), and total lesion FAP expression (TLF) were measured and calculated. FAP immunostaining of tissue specimens was performed. The correlation between 68Ga-FAPI-04 uptake and FAP expression was assessed using the Spearman correlation coefficient. Results: Ninety-one participants (median age, 65 y [interquartile range, 58-70 y]; 69 men) with NSCLC were finally analyzed. In lesion-based analysis, the diagnostic sensitivity and positive predictive value of 68Ga-FAPI-04 PET/CT for detection of the primary tumor were 96.70% (88/91) and 100% (88/88), respectively. In station-based analysis, the diagnostic sensitivity, specificity, and accuracy for the detection of lymph node metastasis were 72.00% (18/25), 93.10% (108/116), and 89.36% (126/141), respectively. Tumor 68Ga-FAPI-04 uptake (SUVmax, SUVmean, FTV, and TLF) correlated positively with FAP expression (r = 0.470, 0.477, 0.582, and 0.608, respectively; all P ≤ 0.001). The volume parameters FTV and TLF correlated strongly with FAP expression in 31 surgical specimens (r = 0.700 and 0.770, respectively; both P < 0.001). Conclusion: 68Ga-FAPI-04 PET/CT had excellent diagnostic efficiency for detecting lymph node metastasis, and 68Ga-FAPI-04 uptake showed a close association with FAP expression in participants with NSCLC.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Background: Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Methods: Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n = 128) and a validation cohort (n = 64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Results: Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. Conclusion: The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Feasibility of active surveillance in patients with clinically T1b papillary thyroid carcinoma ≤1.5 cm in preoperative ultrasonography: MASTER study.

Objective: Active surveillance (AS) is generally accepted as an alternative to immediate surgery for papillary thyroid carcinoma (PTC) measuring ≤1.0 cm (cT1a) without risk factors. This study investigated the clinicopathologic characteristics of PTCs measuring ≤2.0 cm without cervical lymph node metastasis (cT1N0) by tumor size group to assess the feasibility of AS for PTCs between 1.0 cm and 1.5 cm (cT1b≤1.5). Design: This study enrolled clinically T1N0 patients with preoperative ultrasonography information (n= 935) from a cohort of 1259 patients who underwent lobectomy and were finally diagnosed with PTC from June 2020 to March 2022. Results: The cT1b≤1.5 group (n = 171; 18.3 %) exhibited more lymphatic invasion and occult central lymph node (LN) metastasis with a higher metastatic LN ratio than the cT1a group (n = 719; 76.9 %). However, among patients aged 55 years or older, there were no significant differences in occult central LN metastasis and metastatic LN ratio between the cT1a, cT1b≤1.5, and cT1b>1.5 groups. Multivariate regression analyses revealed that occult central LN metastasis was associated with age, sex, tumor size, extrathyroidal extension, and lymphatic invasion in patients under 55, while in those aged 55 or older, it was associated only with age and lymphatic invasion. Conclusion: For PTC patients aged 55 years or older with cT1b≤1.5, AS could be a viable option due to the absence of a significant relationship between tumor size and occult central LN.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.

FD-Net: Feature Distillation Network for Oral Squamous Cell Carcinoma Lymph Node Segmentation in Hyperspectral Imagery.

Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical lymph metastases. Therefore, it is necessary to rely on a reasonable screening method to quickly judge the cervical lymph metastastic condition of OSCC patients and develop appropriate treatment plans. In this study, the widely used pathological sections with hematoxylin-eosin (H&E) staining are taken as the target, and combined with the advantages of hyperspectral imaging technology, a novel diagnostic method for identifying OSCC lymph node metastases is proposed. The method consists of a learning stage and a decision-making stage, focusing on cancer and non-cancer nuclei, gradually completing the lesions' segmentation from coarse to fine, and achieving high accuracy. In the learning stage, the proposed feature distillation-Net (FD-Net) network is developed to segment the cancerous and non-cancerous nuclei. In the decision-making stage, the segmentation results are post-processed, and the lesions are effectively distinguished based on the prior. Experimental results demonstrate that the proposed FD-Net is very competitive in the OSCC hyperspectral medical image segmentation task. The proposed FD-Net method performs best on the seven segmentation evaluation indicators: MIoU, OA, AA, SE, CSI, GDR, and DICE. Among these seven evaluation indicators, the proposed FD-Net method is 1.75%, 1.27%, 0.35%, 1.9%, 0.88%, 4.45%, and 1.98% higher than the DeepLab V3 method, which ranks second in performance, respectively. In addition, the proposed diagnosis method of OSCC lymph node metastasis can effectively assist pathologists in disease screening and reduce the workload of pathologists.

Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer.

BACKGROUND: Medullary Thyroid Carcinoma (MTC) is a rare type of thyroid cancer. Accurate prediction of lateral cervical lymph node metastases (LCLNM) in MTC patients can help guide surgical decisions and ensure that patients receive the most appropriate and effective surgery. To our knowledge, no studies have been published that use radiomics analysis to forecast LCLNM in MTC patients. The purpose of this study is to develop a radiomics combined with thyroid imaging reporting and data system (TI-RADS) model that can use preoperative thyroid ultrasound images to noninvasively predict the LCLNM status of MTC. METHODS: We retrospectively included 218 MTC patients who were confirmed from postoperative pathology as LCLNM negative (n=111) and positive (n=107). Ultrasound features were selected using the Student's t-test, while radiomics features are first extracted from preoperative thyroid ultrasound images, and then a two-step feature selection approach was used to select features. These features are then used to establish three regularized logistic regression models, namely the TI-RADS model (TM), the radiomics model (RM), and the radiomics-TI-RADS model (RTM), in 5-fold cross-validation to determine the likelihood of the LCLNM. The Delong's test and decision curve analysis (DCA) were used to evaluate and compare the performance of the models. RESULTS: The ultrasound features of margin and TI-RADS level, and a total of 12 selected radiomics features, were significantly different between the LCLNM negative and positive groups (p<0.05). The TM, RM, and RTM yielded an averaged AUC of 0.68±0.05, 0.78±0.06, and 0.82±0.05 in the 5-fold cross-validation dataset, respectively. RM and RTM are statistically better than TM (p<0.05 and p<0.001) according to Delong test. DCA demonstrates that RTM brings more benefit than TM and RM. CONCLUSIONS: We have developed a joint radiomics-based model for noninvasive prediction of the LCLNM in MTC patients solely using preoperative thyroid ultrasound imaging. It has the potential to be used as a complementary tool to help guide treatment decisions for this rare form of thyroid cancer.

Three-dimensional pseudo-continuous arterial spin-labelled perfusion imaging for diagnosing upper cervical lymph node metastasis in patients with nasopharyngeal carcinoma: a whole-node histogram analysis.

AIM: To evaluate whole-node histogram parameters of blood flow (BF) maps derived from three-dimensional pseudo-continuous arterial spin-labelled (3D pCASL) imaging in discriminating metastatic from benign upper cervical lymph nodes (UCLNs) for nasopharyngeal carcinoma (NPC) patients. MATERIALS AND METHODS: Eighty NPC patients with a total of 170 histologically confirmed UCLNs (67 benign and 103 metastatic) were included retrospectively. Pre-treatment 3D pCASL imaging was performed and whole-node histogram analysis was then applied. Histogram parameters and morphological features, such as minimum axis diameter (MinAD), maximum axis diameter (MaxAD), and location of UCLNs, were assessed and compared between benign and metastatic lesions. Predictors were identified and further applied to establish a combined model by multivariate logistic regression in predicting the probability of metastatic UCLNs. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic performance. RESULTS: Metastatic UCLNs had larger MinAD and MinAD/MaxAD ratio, greater energy and entropy values, and higher incidence of level II (upper jugular group), but lower BF10th value than benign nodes (all p<0.05). MinAD, BF10th, energy, and entropy were validated as independent predictors in diagnosing metastatic UCLNs. The combined model yielded an area under the curve (AUC) of 0.932, accuracy of 84.42 %, sensitivity of 80.6 %, and specificity of 90.29 %. CONCLUSIONS: Whole-node histogram analysis on BF maps is a feasible tool to differentiate metastatic from benign UCLNs in NPC patients, and the combined model can further improve the diagnostic efficacy.

Quantitative parameters of contrast-enhanced ultrasound effectively promote the prediction of cervical lymph node metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC), the most common endocrine tumor, often spreads to cervical lymph nodes metastasis (CLNM). Preoperative diagnosis of CLNM is important when selecting surgical strategies. Therefore, we aimed to explore the effectiveness of quantitative parameters of contrast-enhanced ultrasound (CEUS) in predicting CLNM in PTC. We retrospectively analyzed 193 patients with PTC undergoing conventional ultrasound (CUS) and CEUS. The CUS features and quantitative parameters of CEUS were evaluated according to PTC size ≤ 10 or > 10 mm, using pathology as the gold standard. For the PTC ≤ 10 mm, microcalcification and multifocality were significantly different between the CLNM (+) and CLNM (-) groups (both P < 0.05). For the PTC > 10 mm, statistical significance was noted between the two groups with respect to the margin, capsule contact, and multifocality (all P < 0.05). For PTC ≤ 10 mm, there was no significant difference between the CLNM (+) and CLNM (-) groups in all quantitative parameters of CEUS (all P > 0.05). However, for PTC > 10 mm, the peak intensity (PI), mean transit time, and slope were significantly associated with CLNM (all P < 0.05). Multivariate analysis showed that PI > 5.8 dB was an independent risk factor for predicting CLNM in patients with PTC > 10 mm (P < 0.05). The area under the curve of PI combined with CUS (0.831) was significantly higher than that of CUS (0.707) or PI (0.703) alone in the receiver operator characteristic curve analysis (P < 0.05). In conclusion, PI has significance in predicting CLNM for PTC > 10 mm; however, it is not helpful for PTC ≤ 10 mm.

Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.

OBJECTIVE: The accurate clinical diagnosis of cervical lymph node metastasis plays an important role in the treatment of differentiated thyroid cancer (DTC). This study aimed to explore and summarize a more objective approach to detect cervical malignant lymph node metastasis of DTC via radiomics models. METHODS: PubMed, Web of Science, MEDLINE, EMBASE, and Cochrane databases were searched for all eligible studies. Articles using radiomics models based on ultrasound, computed tomography, or magnetic resonance imaging to assess cervical lymph node metastasis preoperatively were included. Characteristics and diagnostic accuracy measures were extracted. Bias and applicability judgments were evaluated by the revised QUADAS-2 tool. The estimates were pooled using a random-effects model. Additionally, the leave-one-out method was conducted to assess the heterogeneity. RESULTS: Twenty-nine radiomics studies with 6160 validation set patients were included in the qualitative analysis, and 11 studies with 3863 validation set patients were included in the meta-analysis. Four of them had an external independent validation set. The studies were heterogeneous, and a significant risk of bias was found in 29 studies. Meta-analysis showed that the pooled sensitivity and specificity for preoperative prediction of lymph node metastasis via US-based radiomics were 0.81 (95% CI, 0.73-0.86) and 0.87 (95% CI, 0.83-0.91), respectively. CONCLUSIONS: Although radiomics-based models for cervical lymphatic metastasis in DTC have been demonstrated to have moderate diagnostic capabilities, broader data, standardized radiomics features, robust feature selection, and model exploitation are still needed in the future. ADVANCES IN KNOWLEDGE: The radiomics models showed great potential in detecting malignant lymph nodes in thyroid cancer.

Distinct impacts of radiological appearance on lymph node metastasis and prognosis based on solid size in clinical T1 non-small cell lung cancer.

BACKGROUND: Solid nodules (SN) had more aggressive features and a poorer prognosis than part-solid nodules (PSN). This study aimed to evaluate the specific impacts of nodule radiological appearance (SN vs. PSN) on lymph node metastasis and prognosis based on solid size in cT1 non-small cell lung cancer (NSCLC). METHODS: Patients with cT1 NSCLC who underwent anatomical resection between 2010 and 2019 were retrospectively screened. Univariable and multivariable logistic regression analyses were adopted to evaluate the associations between nodule radiological appearance and lymph node metastasis. The log-rank test and Cox regression analyses were applied for prognostic evaluation. The cumulative recurrence risk was evaluated by the competing risk model. RESULTS: There were 958 and 665 NSCLC patients with PSN and SN. Compared to the PSN group, the SN arm had a higher overall lymph node metastasis rate (21.7% vs. 2.7%, P < 0.001), including nodal metastasis at N1 stations (17.7% vs. 2.1%), N2 stations (14.0% vs. 1.6%), and skip nodal metastasis (3.9% vs. 0.6%). However, for cT1a NSCLC, no significant difference existed between SN and PSN (0 vs. 0.4%, P = 1). In addition, the impacts of nodule radiological appearance on lymph node metastasis varied between nodal stations. Solid NSCLC had an inferior prognosis than part-solid patients (5-year disease-free survival: 79.3% vs. 96.2%, P < 0.001). The survival inferiority only existed for cT1b and cT1c NSCLC, but not for cT1a. Strikingly, even for patients with nodal involvement, SN still had a poorer disease-free survival (P = 0.048) and a higher cumulative incidence of recurrence (P < 0.001) than PSN. Specifically, SN had a higher recurrence risk than PSN at each site. Nevertheless, the distribution of recurrences between SN and PSN was similar, except that N2 lymph node recurrences were more frequent in solid NSCLC (28.21% vs. 7.69%, P = 0.041). CONCLUSION: SN had higher risks of lymph node metastasis and poorer prognosis than PSN for cT1b and cT1c NSCLC, but not for cT1a. SN exhibited a greater proportion of N2 lymph node recurrence than PSN. SN and PSN needed distinct strategies for nodal evaluation and postoperative follow-up.