The pathways for differentiation of human CD4 ؉ T cells into functionally distinct subsets of memory cells in vivo are unknown. The identification of these subsets and pathways has clear implications for the design of vaccines and immune-targeted therapies. Here, we show that populations of apparently naïve CD4 ؉ T cells express the chemokine receptors CXCR3 or CCR4 and demonstrate patterns of gene expression and functional responses characteristic of memory cells. The proliferation history and T cell receptor repertoire of these chemokine-receptor ؉ cells suggest that they are very early memory CD4 ؉ T cells that have ''rested down'' before acquiring the phenotypes described for ''central'' or ''effector'' memory T cells. In addition, the chemokine-receptor ؉ ''naïve'' populations contain Th1 and Th2 cells, respectively, demonstrating that Th1͞Th2 differentiation can occur very early in vivo in the absence of markers conventionally associated with memory cells. We localized ligands for CXCR3 and CCR4 to separate foci in T cell zones of tonsil, suggesting that the chemokine-receptor ؉ subsets may be recruited and contribute to segregated, polarized microenvironments within lymphoid organs. Importantly, our data suggest that CD4 ؉ T cells do not differentiate according to a simple schema from naïve 3 CD45RO ؉ noneffector͞central memory 3 effector͞effector memory cells. Rather, developmental pathways branch early on to yield effector͞memory populations that are highly heterogeneous and multifunctional and have the potential to become stable resting cells.chemokines ͉ immunologic memory ͉ Th1͞Th2 cells U nderstanding how immunological memory develops and is sustained is a focus of ongoing research, in part because successful vaccination requires the production of long-lived memory (1, 2). Such an understanding depends on identifying subsets of memory cells, characterizing their functions, and mapping the pathways through which they are generated. As the molecular determinants of lymphocyte trafficking have been defined, the association between T cells' migratory capacities and their functions has been used to characterize T cell subsets. It was reported, for example, that expression of the chemokine receptor CCR7 identifies a subset of memory cells, so-called central memory (T CM ) cells, that coexpress L-selectin (CD62L) and, in addition to being able to enter lymphoid organs, are functionally distinct from the CCR7 Ϫ subset, so-called effector memory (T EM ) cells (3). The key findings were that T EM but not T CM cells could produce effector cytokines and that T CM cells could serve, in vitro, as precursors for effector cells. Sallusto et al. (4) have hypothesized a linear pathway of T cell differentiation of naïve 3 CD45RO ϩ ͞CD45RA Ϫ noneffector͞T CM 3 effector and T EM cells, driven by the strength of the activating signals. In this model, T CM cells provide ''reactive memory'' and serve as a long-lived pool of precursors for additional memory and effector cells.Our studies of how patterns of chemokine-recep...

Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of “size” (1.43 ± 0.54 × 10−3 mm2/s) and higher mean values of “shape” (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of “size” (2.33 ± 0.22 × 10−3 mm2/s) and lower mean values of “shape” (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.

The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.

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The aim of this study was to determine the range of apparent diffusion coefficient (ADC) values for benign axillary lymph nodes in contrast to malignant axillary lymph nodes, and to define the optimal ADC thresholds for three different ADC parameters (minimum, maximum, and mean ADC) in differentiating between benign and malignant lymph nodes. This retrospective study included consecutive patients who underwent breast MRI from January 2017–December 2020. Two-year follow-up breast imaging or histopathology served as the reference standard for axillary lymph node status. Area under the receiver operating characteristic curve (AUC) values for minimum, maximum, and mean ADC (min ADC, max ADC, and mean ADC) for benign vs malignant axillary lymph nodes were determined using the Wilcoxon rank sum test, and optimal ADC thresholds were determined using Youden’s Index. The final study sample consisted of 217 patients (100% female, median age of 52 years (range, 22–81), 110 with benign axillary lymph nodes and 107 with malignant axillary lymph nodes. For benign axillary lymph nodes, ADC values (×10−3 mm2/s) ranged from 0.522–2.712 for mean ADC, 0.774–3.382 for max ADC, and 0.071–2.409 for min ADC; for malignant axillary lymph nodes, ADC values (×10−3 mm2/s) ranged from 0.796–1.080 for mean ADC, 1.168–1.592 for max ADC, and 0.351–0.688 for min ADC for malignant axillary lymph nodes. While there was a statistically difference in all ADC parameters (p&lt;0.001) between benign and malignant axillary lymph nodes, boxplots illustrate overlaps in ADC values, with the least overlap occurring with mean ADC, suggesting that this is the most useful ADC parameter for differentiating between benign and malignant axillary lymph nodes. The mean ADC threshold that resulted in the highest diagnostic accuracy for differentiating between benign and malignant lymph nodes was 1.004×10−3 mm2/s, yielding an accuracy of 75%, sensitivity of 71%, specificity of 79%, positive predictive value of 77%, and negative predictive value of 74%. This mean ADC threshold is lower than the European Society of Breast Imaging (EUSOBI) mean ADC threshold of 1.300×10−3 mm2/s, therefore suggesting that the EUSOBI threshold which was recently recommended for breast tumors should not be extrapolated to evaluate the axillary lymph nodes.

Objective To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images. Methods This IRB–approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted. Each axial image was subdivided into two subimages: one of the ipsilateral cancer-containing breast and one of the contralateral healthy breast. Cases were randomly divided into training, validation, and testing sets. A convolutional neural network was trained to classify subimages into “cancer” and “no cancer” categories. Accuracy, sensitivity, and specificity of the classification system were determined using pathology as the reference standard. A two-reader study was performed to measure the time savings of the deep learning algorithm using descriptive statistics. Results Two hundred and seventy-three patients with unilateral breast cancer met study criteria. On the held-out test set, accuracy of the deep learning system for tumor detection was 92.8% (648/706; 95% confidence interval: 89.7%–93.8%). Sensitivity and specificity were 89.5% and 94.3%, respectively. Readers spent 3 to 45 seconds to scroll to the tumor-containing slices without use of the deep learning algorithm. Conclusion In breast MR exams containing breast cancer, deep learning can be used to identify the tumor-containing slices. This technology may be integrated into the picture archiving and communication system to bypass scrolling when viewing stacked images, which can be helpful during nonsystematic image viewing, such as during interdisciplinary tumor board meetings.