Use of ultrasound and MRI to stage the axilla for breast cancer before and after neoadjuvant chemotherapy prior to targeted sentinel lymphadenectomy.

PURPOSE: Prior data from this Center demonstrated that for patients who had biopsy-proven axillary metastases, were ycN0 after neoadjuvant chemotherapy (NAC), and had a wire-directed (targeted) sentinel lymphadenectomy (WD-SLND), 60% were node negative. The hypothesis of this study was that results of axillary imaging either before or after NAC would be predictive of final pathologic status after WD-SLND. METHODS: For patients treated with NAC between 2015 and 2023, ultrasound and MRI images of the axilla were retrospectively reviewed by radiologists specializing in breast imaging, who were blinded to the surgical and pathology results. RESULTS: Of 113 patients who fit the clinical criteria, 66 (58%) were ypN0 at WD-SLND and 34 (30%) had a pathologic complete response to NAC. There was no correlation between the number of abnormal lymph nodes on pre-NAC ultrasound or MRI imaging and the final pathologic status of the lymph nodes. The positive predictive value (PPV) of abnormal post-NAC axillary imaging was 48% for ultrasound and 53% for MRI. The negative predictive value (NPV) for normal post-NAC axillary imaging was 67% for ultrasound and 68% for MRI. CONCLUSION: The results of axillary imaging were not adequate to identify lymph nodes after NAC that were persistently pathologically node positive or those which had become pathologically node negative.

Potential value of CT-based comprehensive nomogram in predicting occult lymph node metastasis of esophageal squamous cell paralaryngeal nerves: a two-center study.

PURPOSE: The aim of this study is to construct a combined model that integrates radiomics, clinical risk factors and machine learning algorithms to predict para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma. METHODS: A retrospective study included 361 patients with esophageal squamous cell carcinoma from 2 centers. Radiomics features were extracted from the computed tomography scans. Logistic regression, k nearest neighbor, multilayer perceptron, light Gradient Boosting Machine, support vector machine, random forest algorithms were used to construct radiomics models. The receiver operating characteristic curve and The Hosmer-Lemeshow test were employed to select the better-performing model. Clinical risk factors were identified through univariate logistic regression analysis and multivariate logistic regression analysis and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis. RESULTS: A total of 1024 radiomics features were extracted. Among the radiomics models, the KNN model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.84 in the training cohort and 0.62 in the internal test cohort. Furthermore, the combined model exhibited an AUC of 0.97 in the training cohort and 0.86 in the internal test cohort. CONCLUSION: A clinical-radiomics integrated nomogram can predict occult para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma and provide guidance for personalized treatment.

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). METHODS: We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively. RESULTS: The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis. CONCLUSIONS: In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.

Accuracy and Prognostic Impact of Nodal Status on Preoperative Imaging for Management of Pancreatic Neuroendocrine Tumors: A Multi-Institutional Study.

BACKGROUND: We sought to define the accuracy of preoperative imaging to detect lymph node metastasis (LNM) among patients with pancreatic neuroendocrine tumors (pNETs), as well as characterize the impact of preoperative imaging nodal status on survival. METHODS: Patients who underwent curative-intent resection for pNETs between 2000 and 2020 were identified from eight centers. Sensitivity and specificity of computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)-CT, and OctreoScan for LNM were evaluated. The impact of preoperative lymph node status on lymphadenectomy (LND), as well as overall and recurrence-free survival was defined. RESULTS: Among 852 patients, 235 (27.6%) individuals had LNM on final histologic examination (hN1). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 12.4%, 98.1%, 71.8%, and 74.4% for CT, 6.3%, 100%, 100%, and 80.1% for MRI, 9.5%, 100%, 100%, and 58.7% for PET, 11.3%, 97.5%, 66.7%, and 70.8% for OctreoScan, respectively. Among patients with any combination of these imaging modalities, overall sensitivity, specificity, PPV, and NPV was 14.9%, 97.9%, 72.9%, and 75.1%, respectively. Preoperative N1 on imaging (iN1) was associated with a higher number of LND (iN1 13 vs. iN0 9, p = 0.003) and a higher frequency of final hN1 versus preoperative iN0 (iN1 72.9% vs. iN0 24.9%, p < 0.001). Preoperative iN1 was associated with a higher risk of recurrence versus preoperative iN0 (median recurrence-free survival, iN1→hN1 47.5 vs. iN0→hN1 92.7 months, p = 0.05). CONCLUSIONS: Only 4% of patients with LNM on final pathologic examine had preoperative imaging that was suspicious for LNM. Traditional imaging modalities had low sensitivity to determine nodal status among patients with pNETs.

Laparoscopic Tracer Technique for Liver Lymphatic Drainage by Injecting Indocyanine Green into the Glisson Pedicle.

BACKGROUND: Currently, an effective tracer technique for lymphatic drainage during laparoscopic surgery has not been established. This study aimed to elucidate a new fluorescence, imaging technique targeting the hepatic lymphatic drainage area, using indocyanine green (ICG). METHODS: A patient diagnosed with intrahepatic cholangiocarcinoma (ICC) located in segment 8 of the liver was injected with ICG into the connective tissue of the Glisson pedicle supplied by the lesion's liver segment, avoiding the bile duct, portal vein, and hepatic artery. This was performed under the guidance of laparoscopic ultrasonographic localization to trace the lymph nodes. RESULTS: The lymphatic drainage area traced intraoperatively by ICG was consistent with the definition of the right regional lymph nodes for ICC. The lymph nodes were dissected, followed by addition of a fluorescence tracer. CONCLUSIONS: Mastering intraoperative ultrasonic puncture technology can enable effective and accurate tracing of the lymph nodes of the liver segment where the lesion is located. However, the technical standards for this methodology need to be established through further studies.

Lymph node imaging for thyroid cancer.

Cervical lymph nodes (LNs) in the central (level VI) and lateral (levels II-V) compartments of the neck are the most common sites of locoregional metastases associated with thyroid cancer. Prophylactic nodal dissections are uncommon in modern thyroid surgery and are not routinely performed due to concern for increased morbidity and do not offer improved survival. Therefore, a selective approach for LN dissections is increasingly important. Preoperatively, this is most frequently assessed with cervical ultrasound (US). Contrast-enhanced computed tomography (CT) of the neck can also be used for preoperative assessment. Both US and CT imaging can be used to characterise LNs in levels II-VI and their risk of malignancy based on size, morphology, and growth. US-guided fine-needle aspiration of equivocal LN with thyroglobulin (Tg) washout can also determine if a LN harbours malignancy. For postoperative surveillance after total thyroidectomy, both US and CT continue to play an important role at 6-12 months intervals. These patients may also benefit from additional biochemical data such as Tg levels in addition to LN and thyroid bed imaging. Thyroid uptake scans may also play a role in LN surveillance postoperatively for well-differentiated thyroid carcinoma in certain clinical contexts. Less commonly, positron emitted tomography may play a role, but is typically reserved for patients with aggressive or radioactive iodine refractory disease.

Magnetically guided surgery after primary systemic therapy for breast cancer: implications for enhanced axillary mapping.

BACKGROUND: Superparamagnetic iron nanoparticles perform comparably to radioisotope ± blue dye for sentinel lymph node detection in breast cancer, even when injected up to 8 weeks before surgery. Using superparamagnetic iron nanoparticles for sentinel lymph node detection after primary systemic therapy, and the maximum time frame of superparamagnetic iron nanoparticle administration have not been investigated. METHODS: This cohort study included cN0/1-to-ycN0 patients undergoing sentinel lymph node detection or targeted axillary dissection. All patients received superparamagnetic iron nanoparticles either before primary systemic therapy or before surgery, and radioisotope on the day of surgery. RESULTS: For 113 patients analysed, superparamagnetic iron nanoparticles were injected a median of 3 (range 0-248) days before surgery, with a 97.4% detection rate compared with 91.2% for radioisotope (P = 0.057). Concordance for radioisotope was 97.1% and this was not affected by timing of superparamagnetic iron nanoparticle injection (Kendall's tau 0.027; P = 0.746). The median sentinel lymph node yield was 3 (interquartile range (i.q.r.) 2-3) for superparamagnetic iron nanoparticles and 2 (i.q.r. 2-3) for radioisotope (P < 0.001). In targeted axillary dissection, detection was 100% for superparamagnetic iron nanoparticles and 81.8% for radioisotope (P = 0.124). The index node was magnetic in 93.9% and radioactive in 66.7% (P = 0.007), an outcome that was not affected by any factors. For patients with metastases, superparamagnetic iron nanoparticle detection was 100% and radioisotope-based detection was 84.2% (P = 0.083), with superparamagnetic iron nanoparticles detecting more metastatic sentinel lymph nodes (median of 1 (i.q.r. 1-2) for superparamagnetic iron nanoparticles compared with a median of 1 (i.q.r. 0-1) for radioisotope; P = 0.005). CONCLUSION: Injection before primary systemic therapy is feasible and does not affect concordance with radioisotope. Superparamagnetic iron nanoparticles perform comparably to radioisotope, but detect more sentinel lymph nodes and have a higher rate of detection of metastatic sentinel lymph nodes.

Nodal positivity in patients with clinically and radiologically node-negative breast cancer treated with neoadjuvant chemotherapy: multicentre collaborative study.

BACKGROUND: The necessity of performing a sentinel lymph node biopsy in patients with clinically and radiologically node-negative breast cancer after neoadjuvant chemotherapy has been questioned. The aim of this study was to determine the rate of nodal positivity in these patients and to identify clinicopathological features associated with lymph node metastasis after neoadjuvant chemotherapy (ypN+). METHODS: A retrospective multicentre study was performed. Patients with cT1-3 cN0 breast cancer who underwent sentinel lymph node biopsy after neoadjuvant chemotherapy between 2016 and 2021 were included. Negative nodal status was defined as the absence of palpable lymph nodes, and the absence of suspicious nodes on axillary ultrasonography, or the absence of tumour cells on axillary nodal fine needle aspiration or core biopsy. RESULTS: A total of 371 patients were analysed. Overall, 47 patients (12.7%) had a positive sentinel lymph node biopsy. Nodal positivity was identified in 22 patients (29.0%) with hormone receptor+/human epidermal growth factor receptor 2- tumours, 12 patients (13.8%) with hormone receptor+/human epidermal growth factor receptor 2+ tumours, 3 patients (5.6%) with hormone receptor-/human epidermal growth factor receptor 2+ tumours, and 10 patients (6.5%) with triple-negative breast cancer. Multivariable logistic regression analysis showed that multicentric disease was associated with a higher likelihood of ypN+ (OR 2.66, 95% c.i. 1.18 to 6.01; P = 0.018), whilst a radiological complete response in the breast was associated with a reduced likelihood of ypN+ (OR 0.10, 95% c.i. 0.02 to 0.42; P = 0.002), regardless of molecular subtype. Only 3% of patients who had a radiological complete response in the breast were ypN+. The majority of patients (85%) with a positive sentinel node proceeded to axillary lymph node dissection and 93% had N1 disease. CONCLUSION: The rate of sentinel lymph node positivity in patients who achieve a radiological complete response in the breast is exceptionally low for all molecular subtypes.

Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

2-[18F]FDG PET-based quantification of lymph node metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer.

BACKGROUND AND PURPOSE: The pre-surgical estimation of lymph node (LN) metastasis in colorectal cancer (CRC) poses a significant diagnostic predicament. The associations between LN morphology, density, and metabolic heterogeneity and LN metastasis status in CRCs have been seldomly examined through the lens of radiomics. This research aimed to assess 2-[18F]FDG PET-based quantification of intratumoral metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer. MATERIALS AND METHODS: The construction of the model utilized data from 264 CRC patients, all of whom underwent preoperative 2-[18F]FDG PET/CT. Radiomic features were extracted from PET and CT images of LNs. Least absolute shrinkage and selection operator (LASSO) regression was implemented for selecting pertinent imaging features with a tenfold cross-validation. The predictive accuracy for LN metastasis status was juxtaposed against traditional methodologies (comprising CT-reported LN status and PET/CT-reported LN status) by deploying the receiver operating characteristic (ROC) curve analysis. The radiomics signature was evaluated based on discrimination, calibration, and clinical utility parameters. The model was further subjected to validation using an independent cohort of 132 patients from the period of January 2012 to June 2020. RESULTS: The radiomics model was composed of eight significant radiomic features (five from PET and three from CT), encapsulating metabolic and density heterogeneity. The radiomics signature (area under the curve (AUC), 0.908) showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.563, P < 0.001) and PET/CT-reported LN status (AUC, 0.64, P < 0.001) for predicting LN-positive or LN-negative status. The radiomics signature (AUC, 0.885) also showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.587, P < 0.001) and PET/CT-reported LN status (AUC, 0.621, P < 0.001) to identify N1 and N2. This signature maintained its independence from clinical risk factors and exhibited robustness in the validation test set. Decision curve analysis attested to the clinical utility of the radiomics signature. CONCLUSIONS: The radiomics signature based on 2-[18F]FDG PET/CT, which derived image features directly from LNs irrespective of clinical risk factors, displayed enhanced diagnostic performance compared to conventional CT or PET/CT-reported LN status. This allows for the identification of pre-surgical LN metastasis status and facilitates a patient-specific prediction of LN metastasis status in CRC patients.

[68 Ga]Ga-FAPI-46 PET/CT for locoregional lymph node staging in urothelial carcinoma of the bladder prior to cystectomy: initial experiences from a pilot analysis.

INTRODUCTION: [68 Ga]Ga-FAPI-46 PET/CT is a novel hybrid imaging method that previously showed additional diagnostic value in the assessment of distant urothelial carcinoma lesions. We hypothesized that patients with bladder cancer benefit from [68 Ga]Ga-FAPI-46 PET/CT prior to radical cystectomy for locoregional lymph node staging. MATERIALS AND METHODS: Eighteen patients underwent [68 Ga]Ga-FAPI-46 PET/CT for evaluation of lymph node (LN) status in predefined LN regions. Two hundred twenty-nine intraoperatively removed LN served as histopathological reference standard. RESULTS: Urothelial carcinoma (UC) spread was found in ten LN in seven different regions (14.3%). Hereby, [68 Ga]Ga-FAPI-46 PET/CT was positive in four out of seven regions (57.1%) and showed significantly increased FAPI uptake compared to non-pathological regions. In the remaining three out of seven (42.9%) regions, [68 Ga]Ga-FAPI-46 PET/CT was rated negative since no pathological increased FAPI uptake was detected or the proximity of the urinary tract prevented a differentiation from physiological uptake. CT was inconspicuous in these three regions. In total, two FAP-positive LN regions were found without histopathological counterpart. Overall, sensitivity, specificity, positive predictive value, and negative predictive value were 57.1%, 95.2%, 66.7%, and 93.0% for PET imaging. CONCLUSION: In summary, this innovative [68 Ga]Ga-FAPI-46 PET/CT method showed high specificity and negative predictive value in patients with bladder UC with a future potential to optimize therapy planning.

Preoperative evaluation of mediastinal lymph nodes in non-small cell lung cancer using [68Ga]FAPI-46 PET/CT: a prospective pilot study.

PURPOSE: Mediastinal nodal staging is crucial for surgical candidate selection in non-small cell lung cancer (NSCLC), but conventional imaging has limitations often necessitating invasive staging. We investigated the additive clinical value of fibroblast activation protein inhibitor (FAPI) PET/CT, an imaging technique targeting fibroblast activation protein, for mediastinal nodal staging of NSCLC. METHODS: In this prospective pilot study, we enrolled patients scheduled for surgical resection of NSCLC based on specific criteria designed to align with indications for invasive staging procedures. Patients were included when meeting at least one of the following: (1) presence of FDG-positive N2 lymph nodes, (2) clinical N1 stage, (3) central tumor location or tumor diameter of ≥ 3 cm, and (4) adenocarcinoma exhibiting high FDG uptake. [68Ga]FAPI-46 PET/CT was performed before surgery after a staging workup including [18F]FDG PET/CT. The diagnostic accuracy of [68Ga]FAPI-46 PET/CT for "N2" nodes was assessed through per-patient visual assessment and per-station quantitative analysis using histopathologic results as reference standards. RESULTS: Twenty-three patients with 75 nodal stations were analyzed. Histopathologic examination confirmed that nine patients (39.1%) were N2-positive. In per-patient assessment, [68Ga]FAPI-46 PET/CT successfully identified metastasis in eight patients (sensitivity 0.89 (0.52-1.00)), upstaging three patients compared to [18F]FDG PET/CT. [18F]FDG PET/CT detected FDG-avid nodes in six (42.8%) of 14 N2-negative patients. Among them, five were considered non-metastatic based on calcification and distribution pattern, and one was considered metastatic. In contrast, [68Ga]FAPI-46 PET/CT correctly identified all non-metastatic patients solely based on tracer avidity. In per-station analysis, [68Ga]FAPI-46 PET/CT discriminated metastasis more effectively compared to [18F]FDG PET/CT-based (AUC of ROC curve 0.96 (0.88-0.99) vs. 0.68 (0.56-0.78), P < 0.001). CONCLUSION: [68Ga]FAPI-46 PET/CT holds promise as an imaging tool for preoperative mediastinal nodal staging in NSCLC, with improved sensitivity and the potential to reduce false-positive results, optimizing the need for invasive staging procedures.

Focused Ultrasound-Responsive Nanocomposite with Near-Infrared II Mechanoluminescence for Spatiotemporally Selective Immune Activation in Lymph Nodes.

The immune regulation of the lymphatic system, especially the lymph node (LN), is of great significance for the treatment of diseases and the inhibition of pathogenic organisms spreading in the body. However, achieving precise spatiotemporal control of immune cell activation in LN in vivo remains a challenge due to tissue depth and off-target effects. Furthermore, minimally invasive and real-time feedback methods to monitor the regulation of the immune system in LN are lacking. Here, focused ultrasound responsive immunomodulator loaded nanoplatform (FURIN) with near-infrared II (NIR-II) luminescence is designed to achieve spatiotemporally controllable immune activation in LN in vivo. The NIR-II persistent luminescence of FURIN can track its delivery in LN through bioimaging. Under focused ultrasound (FUS) stimulation, the immunomodulator encapsulated in FURIN can be released locally in the LN to activate immune cells such as dendritic cells and the NIR-II mechanoluminescence of FURIN provides real-time optical feedback signals for immune activation. This work points to a FUS mediated, spatiotemporal selective immune activation strategy in vivo with the feedback control of luminescence signals via ultrasound responsive nanocomposite, which is of great significance in improving the efficacy and reducing the side effect of immune regulation for the development of potential immunotherapeutic methods in the future.

Diagnostic value of applying preoperative breast ultrasound and clinicopathologic features to predict axillary lymph node burden in early invasive breast cancer: a study of 1247 patients.

BACKGROUND: Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer. METHODS: The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard. RESULTS: Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702. CONCLUSION: Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans.

Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM). METHODS: The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram. RESULTS: A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present. CONCLUSION: CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT.

BACKGROUND: To compare the diagnostic performance of the Node-RADS scoring system and lymph node (LN) size in preoperative LN assessment for rectal cancer (RC), and to investigate whether the selection of size as the primary criterion whereas morphology as the secondary criterion for LNs can be considered the preferred method for clinical assessment. METHODS: Preoperative CT data of 146 RC patients treated with radical resection surgery were retrospectively analyzed. The Node-RADS score and short-axis diameter of size-prioritized LNs and the morphology-prioritized LNs were obtained. The correlations of Node-RADS score to the pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological lymph node metastasis were compared between Node-RADS score and short-axis diameter. A nomogram combined the Node-RADS score and clinical features was also evaluated. RESULTS: Node-RADS score showed significant correlation with pN stage, LNM number and LNR (Node-RADS of size-prioritized LN: r = 0.600, 0.592, and 0.606; Node-RADS of morphology-prioritized LN: r = 0.547, 0.538, and 0.527; Node-RADSmax: r = 0.612, 0.604, and 0.610; all p < 0.001). For size-prioritized LN, Node-RADS achieved an AUC of 0.826, significantly superior to short-axis diameter (0.826 vs. 0.743, p = 0.009). For morphology-prioritized LN, Node-RADS exhibited an AUC of 0.758, slightly better than short-axis diameter (0.758 vs. 0.718, p = 0.098). The Node-RADS score of size-prioritized LN was significantly better than that of morphology-prioritized LN (0.826 vs. 0.758, p = 0.038). The nomogram achieved the best diagnostic performance (AUC = 0.861) than all the other assessment methods (p < 0.05). CONCLUSIONS: The Node-RADS scoring system outperforms the short-axis diameter in predicting lymph node metastasis in RC. Size-prioritized LN demonstrates superior predictive efficacy compared to morphology-prioritized LN. The nomogram combined the Node-RADS score of size-prioritized LN with clinical features exhibits the best diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

Prediction of the axillary lymph-node metastatic burden of breast cancer by 18F-FDG PET/CT-based radiomics.

BACKGROUND: The axillary lymph-node metastatic burden is closely associated with treatment decisions and prognosis in breast cancer patients. This study aimed to explore the value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT)-based radiomics in combination with ultrasound and clinical pathological features for predicting axillary lymph-node metastatic burden in breast cancer. METHODS: A retrospective analysis was conducted and involved 124 patients with pathologically confirmed early-stage breast cancer who had undergone 18F-FDG PET/CT examination. The ultrasound, PET/CT, and clinical pathological features of all patients were analysed, and radiomic features from PET images were extracted to establish a multi-parameter predictive model. RESULTS: The ultrasound lymph-node positivity rate and PET lymph-node positivity rate in the high nodal burden group were significantly higher than those in the low nodal burden group (χ2 = 19.867, p < 0.001; χ2 = 33.025, p < 0.001). There was a statistically significant difference in the PET-based radiomics score (RS) for predicting axillary lymph-node burden between the high and low lymph-node burden groups. (-1.04 ± 0.41 vs. -1.47 ± 0.41, t = -4.775, p < 0.001). The ultrasound lymph-node positivity (US_LNM) (odds ratio [OR] = 3.264, 95% confidence interval [CI] = 1.022-10.423), PET lymph-node positivity (PET_LNM) (OR = 14.242, 95% CI = 2.960-68.524), and RS (OR = 5.244, 95% CI = 3.16-20.896) are all independent factors associated with high lymph-node burden (p < 0.05). The area under the curve (AUC) of the multi-parameter (MultiP) model was 0.895, which was superior to those of US_LNM, PET_LNM, and RS models (AUC = 0.703, 0.814, 0.773, respectively), with statistically significant differences (Z = 2.888, 3.208, 3.804, respectively; p = 0.004, 0.002, < 0.001, respectively). Decision curve analysis indicated that the MultiP model provided a higher net benefit for all patients. CONCLUSION: A MultiP model based on PET-based radiomics was able to effectively predict axillary lymph-node metastatic burden in breast cancer. TRIAL REGISTRATION: This study was registered with ClinicalTrials.gov (registration number: NCT05826197) on May 7, 2023.

Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study.

BACKGROUND: Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC. METHODS: This study included NSCLC patients who underwent neck ultrasound examination followed by cervical lymph node (LN) biopsy between January 2019 and January 2022 from three institutes. Radiomic features were extracted from the ultrasound images at the maximum cross-sectional areas of cervical LNs. Logistic regression (LR) and random forest (RF) models were developed. Model performance was assessed by the area under the curve (AUC) and accuracy, validated internally and externally by fivefold cross-validation and hold-out method, respectively. RESULTS: In total, 313 patients with a median age of 64 years were included, and 276 (88.18%) had cervical LNM. Three descriptive semantic features, including long diameter, shape, and corticomedullary boundary, were selected by multivariate analysis. Out of the 474 identified radiomic features, 9 were determined to fit the LR model, while 15 fit the RF model. The average AUCs of the semantic and radiomics models were 0.876 (range: 0.781-0.961) and 0.883 (range: 0.798-0.966), respectively. However, the average AUC was higher for the semantic-radiomics combined LR model (0.901; range: 0.862-0.927). When the RF algorithm was applied, the average AUCs of the radiomics and semantic-radiomics combined models were improved to 0.908 (range: 0.837-0.966) and 0.922 (range: 0.872-0.982), respectively. The models tested by the hold-out method had similar results, with the semantic-radiomics combined RF model achieving the highest AUC value of 0.901 (95% CI, 0.886-0.968). CONCLUSIONS: The ultrasound radiomic models showed potential for accurately diagnosing cervical LNM in patients with NSCLC when integrated with descriptive semantic features. The RF model outperformed the conventional LR model in diagnosing cervical LNM in NSCLC patients.

The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.

BACKGROUND: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR). METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively. CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.

Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.

BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE: To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE: Retrospective. POPULATION: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE: 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT: DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.