Automatic prediction of non-iodine-avid status in lung metastases for radioactive I131 treatment in differentiated thyroid cancer patients

ObjectivesThe growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI).MethodsPrimary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule’s largest diameter.ResultsThe SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852–0.906) and 0.713 (95% confidence interval: 0.613–0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I131 treatment.ConclusionThis study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation

Predictive value of MRI-detected extramural vascular invasion in stage T3 rectal cancer patients before neoadjuvant chemoradiation

PURPOSE:We set out to explore the probability of MRI-detected extramural vascular invasion (mr-EMVI) before chemoradiation to predict responses to chemoradiation and survival in stage T3 rectal cancer patients. METHODS:A total of 100 patients with T3 rectal cancer who underwent MRI examination and received neoadjuvant chemoradiation and surgery were enrolled. The correlation between mr-EMVI and other clinical factors were analyzed by chi-square. Logistic regression model was performed to select the potential factors influencing tumor responses to neoadjuvant chemoradiation. A Cox proportional hazards regression model was performed to explore potential predictors of survival.RESULTS:The positive mr-EMVI result was more likely to be present in patients with a higher T3 subgroup (T3a+b = 7.1% vs. T3c+d = 90.1%, P < 0.001) and more likely in patients with mesorectal fascia involvement than in those without MRF (65% vs. 38.8%, P = 0.034). Compared with mr-EMVI (+) patients, more mr-EMVI (-) patients showed a good response (staged ≤ ypT2N0) (odds ratio [OR], 3.020; 95% confidence interval [CI], 1.071–8.517; P = 0.037). In univariate analysis, mr-EMVI (+) (hazard ratio [HR], 5.374; 95% CI, 1.210–23.872; P = 0.027) and lower rectal cancers (HR, 3.326; 95% CI, 1.135–9.743; P = 0.028) were significantly associated with decreased disease-free survival. A positive mr-EMVI status (HR, 5.727; 95% CI, 1.286–25.594; P = 0.022) and lower rectal cancers (HR, 3.137; 95% CI, 1.127–8.729; P = 0.029) also served as prognostic factors related to decreased disease-free survival in multivariate analysis.CONCLUSION:The mr-EMVI status before chemoradiation is a significant prognostic factor and could be used for identifying T3 rectal cancer patients who might benefit from neoadjuvant chemoradiation

Deep Learning-Enabled Fully Automated Pipeline System for Segmentation and Classification of Single-Mass Breast Lesions Using Contrast-Enhanced Mammography: A Prospective, Multicentre Study

Background Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow. Methods A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists’ reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444). Findings The FAPS-based segmentation task achieved DSCs of 0.888 ± 0.101, 0.820 ± 0.148 and 0.837 ± 0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916–0.978), 0.940 (95% [CI]: 0.894–0.987) and 0.891 (95% [CI]: 0.816–0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6 s vs 3 min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies. Interpretation The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability. Funding This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055)

Internal mammary lymph node recurrence: rare but characteristic metastasis site in breast cancer

<p>Abstract</p> <p>Background</p> <p>To assess the frequency of IMLN recurrence, its associated risk factors with disease-free interval (DFI) and its predicting factors on overall survival time.</p> <p>Methods</p> <p>133 cases of breast cancer IMLN recurrence were identified via the computerized CT reporting system between February 2003 and June 2008, during which chest CT for patients with breast cancer (n = 8867) were performed consecutively at Cancer Hospital, Fudan University, Shanghai, China. Patients' charts were retrieved and patients' characteristics, disease characteristics, and treatments after recurrence were collected for analysis. The frequency was 1.5% (133/8867).</p> <p>Results</p> <p>IMLN recurrence was presented as the first metastatic site in 121 (91%) patients while 88 (66.2%) had other concurrent metastases. Typical chest CT images included swelling of the IMLN at the ipsilateral side with local lump and sternal erosion located mostly between the second and third intercostal space. The median disease-free interval (DFI) of IMLN recurrence was 38 months. The independent factors that could delay the IMLN recurrence were small tumor size (HR 0.5 95%CI: 0.4 - 0.8; <it>p </it>= 0.002), and positive ER/PR disease (HR 0.6, 95% CI: 0.4 - 0.9; <it>p </it>= 0.006). The median survival time after IMLN recurrence was 42 months, with a 5-year survival rate of 30%. Univariate analysis showed four variables significantly influenced the survival time: DFI of IMLN recurrence (p = 0.001), no concurrent distant metastasis (p = 0.024), endocrine therapy for patients with positive ER/PR (p = 0.000), radiotherapy (p = 0.040). The independent factors that reduced the death risk were no concurrent distant metastases (HR: 0.7, 95% CI: 0.4 - 0.9; <it>p </it>= 0.031), endocrine therapy for patients with positive ER/PR status (HR: 0.2, 95% CI: 0.1 - 0.5; <it>p </it>= 0.001) and palliative radiotherapy (HR: 0.3, 95% CI: 0.1- 0.9; <it>p </it>= 0.026).</p> <p>Conclusions</p> <p>The risk of IMLN recurrence is low and there are certain characteristics features on CT images. ER/PR status is both a risk factor for DFI of IMLN recurrence and a prognostic factor for overall survival after IMLN recurrence. Patients with only IMLN recurrence and/or local lesion have a good prognosis.</p

Feasibility study of dual parametric 2D histogram analysis of breast lesions with dynamic contrast-enhanced and diffusion-weighted MRI

Abstract Background This study aimed to investigate the diagnostic value of a dual-parametric 2D histogram classification method for breast lesions. Methods This study included 116 patients with 72 malignant and 44 benign breast lesions who underwent CAIPIRINHA-Dixon-TWIST-VIBE dynamic contrast-enhanced (CDT-VIBE DCE) and readout-segmented diffusion-weighted magnetic resonance examination. The volume of interest (VOI), which encompassed the entire lesion, was segmented from the last phase of DCE images. For each VOI, a 1D histogram analysis (mean, median, 10th percentile, 90th percentile, kurtosis and skewness) was performed on apparent diffusion coefficient (ADC) and volume transfer constant (Ktrans) maps; a 2D histogram image (Ktrans-ADC) was generated from the pixelwise aligned maps, and its kurtosis and skewness were calculated. Each parameter was correlated with pathological results using the Mann–Whitney test and receiver operating characteristic curve analysis. Results For the Ktrans histogram, the area under the curve (AUC) of the mean, median, 90th percentile and kurtosis had statistically diagnostic values (mean: 0.760; median: 0.661; 90th percentile: 0.781; and kurtosis: 0.620). For the ADC histogram, the AUC of the mean, median, 10th percentile, skewness and kurtosis had statistically diagnostic values (mean: 0.661; median: 0.677; 10th percentile: 0.656; skewness: 0.664; and kurtosis: 0.620). For the 2D Ktrans-ADC histogram, the skewness and kurtosis had statistically higher diagnostic values (skewness: 0.831, kurtosis: 0.828) than those of the 1D histogram (all P < 0.05). Conclusions The dual-parametric 2D histogram analysis revealed better diagnostic accuracy for breast lesions than single parametric histogram analysis of either Ktrans or ADC maps

Apparent Diffusion Coefficient (ADC) value: a potential imaging biomarker that reflects the biological features of rectal cancer.

OBJECTIVE: We elected to analyze the correlation between the pre-treatment apparent diffusion coefficient (ADC) and the clinical, histological, and immunohistochemical status of rectal cancers. MATERIALS AND METHODS: Forty-nine rectal cancer patients who received surgical resection without neoadjuvant therapy were selected that underwent primary MRI and diffusion-weighted imaging (DWI). Tumor ADC values were determined and analyzed to identify any correlations between these values and pre-treatment CEA or CA19-9 levels, and/or the histological and immunohistochemical properties of the tumor. RESULTS: Inter-observer agreement of confidence levels from two separate observers was suitable for ADC measurement (k  =  0.775). The pre-treatment ADC values of different T stage tumors were not equal (p  =  0.003). The overall trend was that higher T stage values correlated with lower ADC values. ADC values were also significantly lower for the following conditions: tumors with the presence of extranodal tumor deposits (p  =  0.006) and tumors with CA19-9 levels ≥ 35 g/ml (p  =  0.006). There was a negative correlation between Ki-67 LI and the ADC value (r  =  -0.318, p  =  0.026) and between the AgNOR count and the ADC value (r  =  -0.310, p  =  0.030). CONCLUSION: Significant correlations were found between the pre-treatment ADC values and T stage, extranodal tumor deposits, CA19-9 levels, Ki-67 LI, and AgNOR counts in our study. Lower ADC values were associated with more aggressive tumor behavior. Therefore, the ADC value may represent a useful biomarker for assessing the biological features and possible relationship to the status of identified rectal cancers

Magnetic Resonance Imaging Characteristics of Ovarian Clear Cell Carcinoma.

To probe the magnetic resonance imaging (MRI) features of ovarian clear cell carcinoma (OCCC).This study retrospectively collected MRI data for 21 pathology-confirmed OCCCs from 19 female patients. The MRI findings were analyzed to determine the tumor size, shape/edge, shape and number of protrusions within the cyst, cystic or necrotic components, signal intensity (SI) and enhancement features.The age of the 19 patients ranged from 28 to 63 years (mean age: 53 years). Unilateral tumors were found in 17 patients (17/19, 89%); the average size of all tumors was 10.8 cm. The tumors on MRI were classified into two categories: (a) "cystic adnexal mass with solid protrusions" in 12 (57%) and (b) "solid adnexal mass with cystic areas or necrosis" in 9 (43%). For group a, high to very high SI was observed for most tumors (10/12, 83%) on T1-weighted images (T1WIs), and very high SI was observed on T2-weighted images (T2WIs) for all 12 tumors. Most solid protrusions were irregular and few in number and exhibited heterogeneous intermediate SI on T1WIs and T2WIs and prolonged enhanced SI in the contrast study. All 9 OCCCs in group b were predominantly solid masses with unequally sized necrotic or cystic areas in which some cysts were located at the periphery of the tumor (4/9, 44%). The solid components in all 9 tumors showed iso- or slightly high SI on T1WIs, heterogeneous iso-high SI on T2WIs and heterogeneous prolonged enhancement. According to FIGO classification, 14 tumors (14/19, 74%) were stages I-II, and 5 (5/19, 26%) were stages III-IV.On MRI, OCCCs present as large unilateral multilocular or unilocular cystic masses with irregular intermediate SI solid protrusions or predominantly solid masses with cysts or necrosis at an early FIGO stage

The role of mammographic calcification in the neoadjuvant therapy of breast cancer imaging evaluation.

INTRODUCTION: Investigate the patterns of mammographically detected calcifications before and after neoadjuvant chemotherapy (NACT) to determine their value for efficacy evaluation and surgical decision making. METHODS: 187 patients with malignant mammographic calcifications were followed to record the appearances and changes in the calcifications and to analyze their responses to NACT. RESULTS: Patients with calcifications had higher rates of hormonal receptor (HR) positive tumors (74.3% versus 64.6%) and HER2 positive tumors (51.3% versus 33.4%, p = 0.004) and a similar pathologic complete response (pCR) rate compared to patients without calcifications (35.4% versus 29.8%). After NACT, the range of calcification decreased in 40% of patients, increased in 7.5% and remained stable in 52.5%; the calcification density decreased in 15% of patients, increased in 7.5% and remained stable in 77.5%; none of these change patterns were related to tumor response rate. No significant correlation was observed between the calcification appearance (morphology, distribution, range, diameter or density) and tumor subtypes or pCR rates. Among patients with malignant calcifications, 54 showed calcifications alone, 40 occurred with an architectural distortion (AD) and 93 with a mass. Calcifications were observed inside the tumor in 44% of patients and outside in 56%, with similar pCR rates and patterns of change. CONCLUSIONS: Calcification appearance did not clearly change after NACT, and calcification patterns were not related to pCR rate, suggesting that mammogram may not accurate to evaluate tumor response changes. Microcalcifications visible after NACT is essential for determining the extent of excision, patients with calcifications that occurred outside of the mass still had the opportunity for breast conservation