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PURPOSE Triple-negative breast cancer (TNBC) is a heterogeneous disease with variable response to neoadjuvant therapy (NAT). Pathologic complete response (pCR) has become a prognostic marker for overall and disease-free survival. The aim of this study was to determine if dynamic contrast-enhanced (DCE)-MRI after 2 and/or 4 cycles of NAT can identify patients with a high likelihood of achieving pCR, triaging them to standard of care (SOC), or, when appropriate, to de-escalation trials. Conversely, we aimed to identify chemoresistant tumors that are unlikely to achieve pCR and may benefit from escalated targeted trials. METHOD AND MATERIALS 309 patients with stage I-III TNBC underwent DCE-MRI (temporal resolution: 9-12 sec) at baseline (BL), 2 cycles (C2), and 4 cycles (C4) of SOC doxorubicin/cyclophosphamide (AC) NAT as part of a prospective IRB-approved study (NCT02276443). Tumor volumes of the index lesion were calculated using 3 axis measurements during the early phase of the DCE-MRI (60s). Percent tumor volume reduction (TVR) between BL, C2, and C4 was calculated. Patients were randomly assigned to a training or a validation cohort in a 1:1 ratio. pCR was assessed at surgery after completion of SOC NAT. Correlation between pCR and TVR was evaluated using ROC analysis. RESULTS Of 309 TNBC patients, 136 (44%) achieved pCR. Following 2 cycles of NAT, TVR >80% was predictive of pCR (chemosensitivity), while TVR ≤ 55% was predictive of non-pCR (chemoresistance) with PPV 80%, NPV 89%, AUC 0.811 (0.73~0.893, p< 0.0001) in the training cohort, and PPV 82%, NPV 85%, AUC 0.815 (CI:0.736~0.894, p< 0.0001) in the validation cohort. Following 4 cycles of NAT, TVR >90% was predictive of pCR, while TVR ≤80% was predictive of non-pCR with PPV 80%, NPV 84%, AUC 0.827 (0.756~0.898, p< 0.0001) in the training cohort and with PPV 73%, NPV 82%, AUC 0.785 (CI:0.709~0.862, p< 0.001) in the validation cohort. Using this model, the pCR status was correctly classified in 50% of TNBC patients using C2 DCE-MRI in the training cohort, and 54% in the validation cohort. Only 8% were misclassified in the training cohort, and 10% in the validation cohort. Using C4 DCE-MRI, the pCR status of 61% and 57% of TNBC was correctly classified in the validation and the testing cohorts, respectively. 12% were misclassified in the validation cohort, and 21% in the testing cohort. CONCLUSION DCE-MRI after 2 and 4 cycles of AC-based NAT correctly predicted the pCR status of 54% and 57% of TNBC patients, respectively, as either excellent responders or nonresponders with high AUC 0.811 and 0.827. This may allow patients to be triaged to SOC NAT with option of de-escalation or early targeted therapies for non-responders. Citation Format: Mary S. Guirguis, Beatriz Adrada, Miral Patel, Frances Perez, Rosalind Candelaria, Wei Yang, Jia Sun, Rania M. Mohamed, Medine Boge, H. T. Carisa Le-Petross, Jessica Leung, Gary J. Whitman, Deanna L. Lane, Marion E. Scoggins, Tanya Moseley, Benjamin Musall, Jason White, Sanaz Pashapoor, Peng Wei, Jong Bum Son, Ken-Pin Hwang, Bikash Panthi, Mark Pagel, Lei Huo, Kelly K. Hunt, Elizabeth Ravenberg, Alastair M. Thompson, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Stacy Moulder, Clinton Yam, Jingfei Ma, Gaiane Rauch. DCE-MRI for early prediction of excellent response versus chemoresistance in triple negative breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-15.

Early blight of potato and tomato, caused principally by Alternaria solani, results in extensive damage to foliar tissues. Symptoms are seen as expanding necrotic lesions. Lesions are sometimes surrounded by a halo of chlorotic tissue, however the basis for presence or absence of the distinctive halo have not been determined. To dissect the basis for lesion development a series of Alternaria-associated toxins and potential elicitor proteins were tested in potato and tobacco. Infiltration of leaves demonstrated that, while most of the pure toxins did not have a visible effect, two anthraquinones, bostrycin and altersolanon A, elicited a strong necrosis-mediated response. Extracts of solid and liquid cultures of A. solani yielded anthraquinones that also elicited a necrotic response. Two potential elicitor proteins were identified and cloned from the potato brown spot and early blight pathogens Alternaria alternata and A. solani, respectively. Agrobacterium-mediated infiltration and expression of the A. alternata xyloglucanase cel12-A and A. solani Hrip1 revealed that cel12-A had no apparent effect, whereas Hrip1 induced expanding tissue death and development of chlorotic halos. Development of chlorotic halos was dependent on leaf age, with older leaves exhibiting halos. It is proposed that specific toxin and elicitor production, in conjunction with tissue susceptibility mediate the outcomes of early blight symptoms.

Background and Purpose: There is currently lack of recognized imaging criteria for prediction of treatment response to NAST in breast cancer patients. And early identification of treatment response to neoadjuvant systemic therapy (NAST) in Triple Negative Breast Cancer (TNBC) patients is important for appropriate treatment selection and response monitoring. A novel MRI sequence, Magnetic Resonance Image Compilation (MagIC) is capable of simultaneous quantitation of several tissue water properties including longitudinal (T1), transverse (T2) relaxation times, and proton density (PD). In this study we evaluated the ability of a radiomic model extracted from a novel MagIC sequence acquired early during NAST to predict pathologic complete response to NAST in TNBC. Materials and Methods: This IRB approved prospective ARTEMIS trial (NCT02276443) included 184 women (122 training dataset, 62 testing dataset) diagnosed with stage I-III TNBC. All patients were scanned with MagIC on a 3T MRI scanner at baseline (184 patients), and after 4 cycles (156 Patients) of NAST. T1, T2 and PD maps were generated from the source images using SyMRI (SyntheticMR, Linkoping, Sweden). Histopathology at surgery was used to determine pathologic complete response (pCR) which was defined as absence of the invasive cancer in the breast and axillary lymph nodes. 3D contouring of the tumors was performed using an in-house toolbox. 310 (10 first-order, 300 GLCM) textural features were extracted from each map, with total of 930 features/patient. Radiomic features were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. To build a multivariate, predictive model, logistic regression with elastic net regularization was performed for texture feature selection. The tuning parameter was optimized using 5-fold cross-validation based on the average area under curve (AUC) of each fold of a cross-validation using training data. Then the testing data were used to compare model’s performance by AUC. Results: Univariate analysis found 23 PD, 17 T1 and 10 T2 radiomic features at C4 time point to be able to predict pCR status with AUC >70% in both training and testing cohort. The top performing radiomic features were Entropy, Variance, Homogeneity and Energy (Tables1-2). Multivariate radiomics models from C4-PD, and C4-T1 maps showed best performance during both cross validation and independent testing. The radiomic signature of C4-T1 map that included 27features had best performance, with an AUC of 0.77, 0.70 (95% CI: 0.571-0.868) in training and testing cohort respectively. C4-PD map radiomic signature that included 6features was able to predict the pCR status with AUC of 0.73, 0.72 (95% CI: 0.571-0.868) in training and testing cohort respectively. Conclusion: Our data found that MagIC-based radiomics signature could potentially predict pathologic complete response in TNBC early during NAST. This data shows the potential application of MagIC radiomic model for improvement of response assessment in TNBC. Table 1.Best performing radiomic features from PD map after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valuePD-mapAngular Variance of Sum entropy1060.73820.6437-0.8328500.73240.5895-0.8752 Table 2.Best performing radiomic features from T1-T2 maps after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valueT1-mapAngular Variance of Sum entropy1060.76530.6762-0.8544500.70510.5524-0.8579 Citation Format: Nabil Elshafeey, Ken-Pin Hwang, Beatriz Elena Adrada, Rosalind Pitpitan Candelaria, Medine Boge, Rania M Mahmoud, Huiqin Chen, Jia Sun, Wei Yang, Aikaterini Kotrotsou, Benjamin C Musall, Jong Bum Son, Gary J Whitman, Jessica Leung, Huong Le-Petross, Lumarie Santiago, Deanna Lynn Lane, Marion Elizabeth Scoggins, David Allen Spak, Mary Saber Guirguis, Miral Mahesh Patel, Frances Perez, Abeer H Abdelhafez, Jason B White, Lei Huo, Elizabeth Ravenberg, Wei Peng, Alastair Thompson, Senthil Damodaran, Debu Tripathy, Stacey L Moulder, Clinton Yam, Mark David Pagel, Jingfei Ma, Gaiane Margishvili Rauch. Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-06.

Background: TNBC constitutes an aggressive and heterogeneous group of tumors with variable response to neoadjuvant therapy (NAT) that currently lacks clinically available profiling strategies for prediction. We aimed to develop an integrated model based on imaging, pathological and clinical data capable to predict NAT response in TNBC early during therapy. METHOD AND MATERIALS:125 Stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433) who had DCE-MRI at baseline (BL) and post 2 cycles (C2) of NAT, and had surgery were included in this analysis. Tumor volume was calculated using 3D measurements at BL and C2 time points DCE-MRI. Percent tumor volume reduction (TVR) between BL and C2 was calculated. Demographic, clinical, and pathological data (age, T and N stage, histology, androgen receptor expression, Ki-67, stromal tumor infiltrating lymphocytes level (sTIL), and PD-L1 expression), and treatment response at surgery (pCR vs non-pCR) were documented. Recursive partitioning was used to identify TVR cutoff value. Multivariate logistic regression and ROC analysis were used to assess associations and build and evaluate predictive models. RESULTS: 61 (49%) TNBC pts showed pCR at surgery, and 64 (51%) non-pCR. Recursive partitioning analysis identified ≥ 55% TVR as the optimal cutoff values for pCR prediction at C2. TVR, N stage and sTIL were significantly associated with pCR in the multivariate analyses (p Citation Format: Gaiane Margishvili Rauch, Rosalind P. Candelaria, Mary Saber Guirguis, Medine Boge, Rania M. M. Mohamed, Nabil Elshafeey, Jia Sun, Gary J Whitman, Jessica Leung, Huong C Le-Petross, Lumarie Santiago, Deanna Lane, Marion Scoggins, David Spak, Miral M Patel, Frances Perez, Jason B. White, Elizabeth Ravenberg, Wei Peng, Debu Tripathy, Vicente Valero, Jennifer Litton, Lei Huo, Clinton Yam, Alastair Thompson, Jingfei Ma, Stacy L. Moulder, Wei Yang, Beatriz E. Adrada. Integrated model for early prediction of neoadjuvant systemic therapy response in triple negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-07.

Introduction: Neoadjuvant chemotherapy (NACT) is becoming standard of care for presurgical treatment of triple negative breast cancer (TNBC) patients. Achievement of pathological complete response (pCR) after NACT is associated with improved outcomes. There is currently an unmet need in development of imaging and clinical tools for prediction of pCR to NACT in TNBC. We investigated use of deep learning convolution neural networks (CNNs) for early prediction of pCR in a TNBC cohort on the basis of MRI acquired before the initiation and at the midpoint, after completion of four cycles of NACT (C4). Materials and Methods: Baseline and C4 MRIs of 112 TNBC patients were collected from an ongoing prospective clinical trial (NCT02276443). Four patients were excluded because they underwent different treatment for the second regimen. Among the 108 patients, 52 patients (48%) had pCR confirmed at surgery. Positive enhancement integral (PEI) derived from the early phases of DCE MRI, and apparent diffusion coefficients (ADC) derived from DWI MRI (b = 100 and 800 s/mm2), were used for our investigation. The images were aligned and the tumor regions were cropped from all images. All tumor patches were normalized between [0, 1], and were padded to form matrices of the same size of 192×192×64 for PEI, or the size of 192×192×16 for ADC. The CNN was constructed using stacked 3D convolution and MaxPooling layers. It consisted of up to four channels for the inputs (baseline and C4 PEI and ADC). Features extracted from each channel were concatenated and regressed for pCR prediction via three densely connected layers. Binary cross-entropy was used as the loss function for CNN training, and the loss was optimized using an Adam optimizer with the initial learning rate of 0.0001. Because of the currently limited sample size, four-fold cross-validation was used for CNN training and evaluation. The patients were divided into four groups, each group had 27 patients and the pCR:non-pCR ratio was controlled as 13:14. For each fold, one group was reserved as the independent testing group, and the other three groups were combined for network training and internal validation. Receiver operating characteristic (ROC) curve was plotted for each fold of testing, and area under the curve (AUC) was calculated. Final performance of the CNN was determined by averaging the AUCs of the four testing folds. Additionally, to test the prediction efficacy of each input, we trained the CNN under the same settings but used PEI or ADC only as input, and the results were compared. Results: The CNN trained with PEI only achieved an average AUC of 0.65 ± 0.09. The second CNN trained with ADC only achieved an average AUC of 0.72 ± 0.07. The third CNN trained with both PEI and ADC achieved an average AUC of 0.73 ± 0.06. Conclusion and Discussion: Using baseline and mid-treatment MRIs, deep learning CNN showed promising performance to predict pCR in the early course of NACT. The prediction AUC for the independent testing groups was largely improved by using ADC to train the network, indicating that ADC can have more critical information than PEI in assisting pCR prediction during the early course of NACT. Future work includes curation of a larger patient data for network training and evaluation to improve the prediction performance and further validate generalization of the network. We will also explore more advanced network structures, through which the prediction performance can be improved. Four-fold cross-validation AUCs of the network using different data as inputs.PEIADCPEI+ADCFold 10.570.640.66Fold 20.760.800.77Fold 30.660.700.68Fold 40.590.740.79Average0.65 ± 0.090.72 ± 0.070.73 ± 0.06 Citation Format: Zijian Zhou, Nabil A Elshafeey, David E Rauch, Beatriz E Adrada, Rosalind P Candelaria, Mary S Guirguis, Wei Yang, Medine Boge, Rania M Mohamed, Gary J Whitman, Deanna L Lane, Huong C Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral M Patel, Frances Perez, Debu Tripathy, Vicente Valero, Clinton Yam, Stacy Moulder, Jason B White, Jong Bum Son, Mark D Pagel, Jingfei Ma. Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-03.

A collaborative approach to treating patients is well taught in medical training. However, collaboration and team building in clinical and laboratory research may have been given less emphasis. More scientific discoveries are now being made with multidisciplinary teams, requiring a thoughtful approach in order to achieve research goals while mitigating potential conflicts. Specific steps for a successful team science project include building the team, assigning roles and responsibilities, allocating rules, and discussing authorship guidelines. Building a team involves bringing individuals together and developing a common research goal while establishing psychological safety for all members of the team. Clear assignment of roles and responsibilities avoids confusion and allows each member’s contributions to be acknowledged. Allocating rules involves discussing how decisions in the team will be made, how data and knowledge sharing will occur, and how potential conflicts will be resolved. Discussing authorship at the start of the project ensures that the entire team knows what work must be completed for authorship to be obtained.

PURPOSE Triple negative breast cancer (TNBC) is an aggressive and heterogeneous subtype of breast cancer. Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) predicts better survival. Early prediction of the treatment response can potentially triage non-responding patients to alternative protocol treatments, spare them of the unneeded toxicity, and improve pCR. We evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on the dynamic contrast enhanced (DCE) and diffusion-weighted imaging (DWI) MRI images obtained early during NAST to predict pCR. MATERIALS AND METHODS This IRB-approved prospective study (NCT02276443) included 182 patients with biopsy proven stage I-III TNBC who had multiparametric MRIs at baseline (BL), post 2 cycles (C2), and post 4 cycles (C4) of NAST before surgery. Tumors and peritumoral regions of 5 mm and 10 mm in thickness were segmented on the 2.5 minutes DCE subtraction images and on the b=800 DWI images. Ten histogram-based first order texture features including mean, minimum, maximum, standard deviation, kurtosis, skewness, 1st, 5th, 95th, and 99th percentile, and 300 radiomic Grey Level Co-occurrence matrix (GLCM) features along with their absolute and relative differences between the 3 imaging time points were extracted from the tumors and from the peritumoral regions with an in-house Matlab toolbox. Treatment response at surgery (pCR vs non-pCR) was documented. The samples were divided into training and testing datasets by a 2:1 ratio. Area under the receiver operating characteristics curve (AUC ROC) was calculated for univariate analysis in predicting pCR. Logistic regression with elastic net regularization was performed for texture feature selection. Parameter optimization was performed by using 5-fold cross-validation based on mean cross-validated AUC in the training set. RESULTS Of 182 TNBC patients, 88 (48%) had pCR and 94 (52%) did not achieve pCR. Eight multivariate models combining radiomic features from both DCE and DWI tumoral and peritumoral regions had AUC > 0.8 (0.807-0.831) with p-value < 0.001 in both training and testing sets. The highest AUC=0.831 was obtained from a model consisting of 15 radiomic features: tumor DWI (5 GLCM features) at C2, peritumoral region on DCE (skewness) at C2, tumor DCE (1st, 5th percentile) at C4, tumor DWI (3 GLCM features) at C4, peritumoral region DWI (1 GLCM feature) at C4, and the relative difference between C4/C2 on DCE (5th, 95th percentile and mean). CONCLUSION Multi-parametric MRI-based radiomics models from the tumor and the peritumoral regions showed high accuracy as potential early predictors of NAST response in TNBC patients. Citation Format: Rania M. Mohamed, Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Elizabeth Ravenberg, Alyson Clayborn, Mark Pagel, Huiqin Chen, Jia Sun, Peng Wei, Alastair M. Thompson, Stacy Moulder, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Jingfei Ma, Gaiane Rauch. Multi-Parametric MRI-Based Radiomics Models from Tumor and Peritumoral Regions as Potential Predictors of Treatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-06.

Background and Purpose Triple negative breast cancer (TNBC) is a biologically aggressive tumor and a refractory subtype of breast cancer due to the lack of therapeutic targets, such as estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2. In this study, we investigated the accuracy of radiomic models based on the dynamic contrast enhanced (DCE) MRI images obtained after the completion of NAST as discriminators of treatment response in TNBC patients. Materials and Methods This IRB-approved prospective study (ARTEMIS trial, NCT02276443) included 181 patients with biopsy proven stage I-III TNBC who Had MRIs after completion of NAST and before surgery. Patients were classified as pathologic complete response (pCR) and non-pCR at the surgery. Tumors were segmented on the 2.5 minutes DCE subtraction images. Regions with necrosis or clip artifacts were excluded from the contour. If tumors were not visible, the tumor bed was contoured. Whole-tumor histogram-based first order texture features (p=10) including mean, minimum, maximum, Standard deviation, kurtosis, skewness, 1st, 5th, 95th, and 99th percentiles, and radiomic (p=300) Grey Level Co-occurrence matrix (GLCM) features were extracted with an in-house Matlab toolbox. The samples were split into training and testing data sets by a 2:1 ratio. For univariate analysis area under the receiver operating characteristics curve (AUC ROC) was performed for pCR status prediction. For texture feature selection logistic regression with elastic net regularization was performed. Parameter optimization was performed by using 5-fold cross-validation based on mean cross-validated AUC in the training set. A P-value less than 0.05 was considered statistically significant. Results Of the total 181 patients, 88 (49%) had pCR and 93 (51%) had non-pCR. Univariate analysis identified 7 statistically significant first order imaging features (Minimum, Maximum, Mean, 1st Percentile, 5th Percentile, 95th Percentile, and 99th Percentile) with AUC >= 0.7 (p< 0.001), in both training and testing data sets. Percentile 5 showed highest AUC = 0.78 (p< 0.001). Two multivariate models were statistically significant at cross-validation with AUC>=0.7. The first model combined 2 first order data (Percentile 1 and Percentile 5) with AUC = 0.73 (p< 0.001). The second model combined 8 first order features (Percentile 1, 5, 95, 99, Mean, Minimum, Maximum, and Skewness) and 24 GLCM features with AUC = 0.7 (p=0.003). Conclusion DCE-MRI radiomic features from tumor and tumor bed regions in TNBC may be helpful imaging biomarkers for predicting treatment response after NAST. Citation Format: Rania M. Mohamed, Bikash Panthi, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Huiqin Chen, Jia Sun, Peng Wei, Jennifer K. Litton, Vicente Valero, Clinton Yam, Mark Pagel, Jingfei Ma, Gaiane Rauch. A Pre-operative Dynamic Contrast Enhanced MRI-Based Radiomics Models as Predictors of Treatment Response after Neoadjuvant Systemic Therapy in Triple Negative Breast Cancer Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-35.

Background and Purpose Early prediction of neoadjuvant systemic therapy (NAST) response in triple negative breast cancer (TNBC) patients could potentially aid in the selection of alternative therapies and avoid unnecessary toxicity in patients unlikely to achieve pathologic complete response (pCR) with NAST. In this study, we investigated the radiomic features of the peritumoral and the tumoral regions from dynamic contrast enhanced (DCE) MRI acquired at different time points of NAST for early treatment response prediction in TNBC. Methods and Materials This study included 182 biopsy-confirmed stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433). All patients underwent DCE-MRI on a GE 3T MRI scanner at baseline (BL), after two (C2) and four (C4) cycles of doxorubicin/cyclophosphamide based chemotherapy and before surgery. The peritumoral and the tumoral regions were segmented manually by two fellowship-trained radiologists using early phase (2.5 min) DCE-MRI subtraction images. Ten first order radiomic features, 300 grey-level-co-occurrence matrix (GLCM) features along with their absolute and relative differences (C4/BL, C2/BL, C4/C2) between the 3 imaging time points were extracted from the peritumoral and the tumoral regions. Patients were randomly divided into training and testing sets in a 2:1 ratio. For univariate analysis, area under the receiver operating characteristics curve (AUC ROC) was measured to determine the features most predictive of pCR/non-pCR. Wilcoxon Rank Sum test was used to test the statistical significance of predictive performance. In multivariate analysis, radiomic models were established using logistic regression with elastic net regularization followed by 5-fold cross validation for performance assessment. Results Eighty-eight (48%) patients had pCR (59 training, 29 testing) and 94 (52%) patients had non-pCR (63 training, 31 testing). Twenty-five radiomic features (4 from peritumoral C4, 5 from tumoral C4, 4 from peritumoral C4/BL, 6 from tumoral C4/BL, 2 from peritumoral C4/C2 and 4 from tumoral C4/C2) were statistically significant with AUC ≥ 0.75 in both the training and the testing sets at the univariate analysis. The significant features at C4 had AUCs of 0.75-0.79 for the training set and 0.76-0.81 for the testing set. Changes measured between C4 and BL or C2 showed AUC of 0.76-0.84 in the training and 0.75-0.81 in the testing datasets. Eleven multivariate regression models comprised of radiomic features at BL, C2, C4 and their changes (C4/BL, C4/C2 and C2/BL) showed an AUC of 0.80-0.84 for cross validation and an AUC of 0.80-0.82 for independent testing. Conclusions Radiomic models using longitudinal DCE MRI parameters of peritumoral and tumoral regions during NAST have the potential to predict pCR in TNBC patients undergoing NAST. Citation Format: Bikash Panthi, Rania M. Mohamed, Beatriz Adrada, Rosalind Candelaria, Mary S. Guirguis, Wei Yang, Medine Boge, Miral Patel, Nabil Elshafeey, Sanaz Pashapoor, Zijian Zhou, Jong Bum Son, Ken-Pin Hwang, H. T. Carisa Le-Petross, Jessica Leung, Marion E. Scoggins, Gary J. Whitman, Zhan Xu, Deanna L. Lane, Tanya Moseley, Frances Perez, Jason White, Elizabeth Ravenberg, Alyson Clayborn, Mark Pagel, Huiqin Chen, Jia Sun, Peng Wei, Alastair M. Thompson, Stacy Moulder, Anil Korkut, Lei Huo, Kelly K. Hunt, Jennifer K. Litton, Vicente Valero, Debu Tripathy, Clinton Yam, Jingfei Ma, Gaiane Rauch. Longitudinal DCE-MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy (NAST) in Triple Negative Breast Cancer (TNBC) Patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-34.

Ultrasound evaluation of the axilla plays a critical role in the setting of newly diagnosed breast cancer as surgical management evolves toward more targeted axillary nodal resection. Regional nodal involvement by metastatic carcinoma is one of most important prognostic factors in breast cancer and guides local, regional, and systemic treatment. Ultrasound also evaluates response to neoadjuvant chemotherapy. This article will review ultrasound techniques and the anatomy and the morphology of axillary lymph nodes. Lymph node staging in breast cancer will also be discussed. Ultrasound-guided interventions and localizations and emerging technologies of elastography and contrast-enhanced ultrasound will be discussed. In addition, this article will discuss the role of ultrasound as it applies to management of the axilla since the American College of Surgeons Oncology Group Z011 and Z1071 trials. Finally, other causes of benign and malignant axillary lymphadenopathy, not related to breast cancer, are discussed.

Lenten rose (Hellebores hybridus) is an herbaceous perennial grown in landscapes and valued for early spring flowers and high levels of deer resistance. An additional benefit as a landscape plant c...

Background: Triple negative breast cancer (TNBC) has a poor prognosis. In particular, TNBC patients who have significant residual disease at the time of surgery following completion of neoadjuvant systemic therapy (NST) have an especially poor prognosis. In an effort to identify patients who are unlikely to achieve pathologic complete response (pCR), we investigated if pre-treatment breast MRI morphological characteristics and imaging response patterns during NST can predict pCR in TNBC patients. Materials and Methods: As part of a prospective IRB-approved clinical trial (ARTEMIS, NCT02276443), 199 patients with biopsy-proven stage I-III TNBC received NST and were classified as pCR or non-pCR based on histopathology at surgery. Patients underwent breast MRI at baseline (BL), after 2 cycles (C2), and 4 cycles (C4) of Adriamycin-based chemotherapy (AC). Subsequently, patients received either taxane-based NST or targeted therapy guided by mid-treatment imaging response. MRI studies were reviewed by two fellowship-trained breast radiologists who were blinded to the pathology results. ACR MRI BIRADS lexicon (5th Ed) was used to describe BL tumor morphology. Imaging response pattern at C2 and C4 MRI was classified as follows: type 0 (complete), type 1 (concentric shrinkage), type 2 (crumble), type 3 (diffuse enhancement), type 4 (stable), or type 5 (progression). Morphological baseline features and response patterns were summarized and compared to the pCR status on surgical pathology using Fisher’s exact test. P values less than 0.05 were considered statistically significant. Results: Median age was 53 years, range 24-79. Of 199 patients, 95 (48%) had pCR and 104 (52%) had non-pCR. At BL MRI, an irregularly-shaped mass and homogenous or clumped non-mass enhancement were associated with pCR (p=0.026 and p=0.013, respectively). Multifocality, peritumoral edema, and intratumoral necrosis were independent of pCR. Following NST, the most common MRI response pattern was type 1, seen with equal frequency in pCR and non-pCR at C2 (58% and 42%, respectively) and C4 (47% and 53%, respectively). The following response pattern associations were found: type 0 was associated with pCR at both C2 and C4 timepoints (p Citation Format: Mary S Guirguis, Beatriz E Adrada, Rosalind P Candelaria, Jia Sun, Gary J Whitman, Wei T Yang, Medine Boge, Rania M Mohamed, Nabil A Elshafeey, Deanna L Lane, Huong Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral Patel, Frances Perez, Peng Wei, Debu Tripathy, Jason White, Elizabeth Ravenberg, Lei Huo, Jennifer Litton, Banu Arun, Vincente Valero, Alastair Thompson, Stacy Moulder, Clinton Yam, Gaiane M Rauch. Prediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-06.

Falls can result in injuries that require rehabilitation and long-term care after hospital discharge. Identifying factors that contribute to prediction of discharge disposition is crucial for efficient resource utilization and reducing cost. Several factors may influence discharge location after hospitalization for a fall. The aim of this study was to examine clinical and non-clinical factors that may predict discharge disposition after a fall. We hypothesized that age, injury type, insurance type, and functional status would affect discharge location.This two-year retrospective study was performed at an urban, adult level-1 trauma center. Fall patients who were discharged home or to a facility after hospital admission were included in the study. Data was obtained from the trauma registry and electronic medical records. Logistic regression modeling was used to assess independent predictors.A total of 1,121 fallers were included in the study. 621 (55.4%) were discharged home and 500 (44.6%) to inpatient rehabilitation (IRF)/skilled nursing facility (SNF). The median age was 64 years (IQR: 49-79) and 48.4% (543) were male. The median length of hospital stay was 5 days (IQR: 2.5-8). Increasing age (p 0.001), length of stay in the ICU (p 0.001), injury severity (p 0.001), number of comorbidities (p = 0.038), having Medicare insurance (p = 0.025), having a fracture at any body region (p 0.001), and ambulation status (p = 0.025) significantly increased the odds of being discharged to IRF/SNF compared to home. The removal of injury severity score and ICU length of stay from the "late/regular discharge" model, to create an "early discharge" model, decreased the accuracy of the prediction rate from 78.5% to 74.9% (p 0.001).A combination of demographic, clinical, social, economic, and functional factors can together predict discharge disposition after a fall. The majority of these factors can be assessed early in the hospital stay, which may facilitate a timely discharge plan and shorter stays in the hospital.

Pregnancy associated breast cancer (PABC) is a subset of cancer that is too often diagnosed at a more advanced stage due to physiologic changes of the breast and lack of awareness among patients and physicians, resulting in higher mortality rates. While PABC is rare, it is postulated that as women delay childbearing, the rate of PABC may increase. Therefore, it is important to discuss appropriate workup, safety of mammography during pregnancy, and biopsy techniques.

Xyloglucan-specific endoglucanase inhibitor proteins (XEGIPs) are present in a wide range of dicots, where they are believed to play a role in defense from pathogens. XEGIPs are generally present as two or three copies; however, they are reported to be present as a cluster of 10 copies in potato and tomato on chromosome 1. We have identified a second set of XEGIP-encoding genes, where two inverted copies are present on chromosome 8 of potato (Solanum tuberosum L.). The same set exists in the same order on chromosome 8 of tomato (Solanum pennellii Correll). Transcript expression analysis indicates that XEGIP10 is transcribed only in roots, while XEGIP11 transcripts were not detected under normal growth conditions. Transformed potatoes (‘Bintje’ and ‘Kennebec’) overexpressing these genes did not show any changes in phenology. Foliar screening of transgenic lines for resistance to the late blight pathogen Phytophthora infestans (Mont.) de Bary did not demonstrate a large reduction in disease progression or p...

Since its arrival in the British Isles in 1845 Phytophthora infestans has remained the most destructive pathogen of potato. In the ensuing period, the British and Irish P. infestans populations have undergone major displacements following the immigration of novel strains. Here we report the re-emergence of the Ib mitochondrial DNA haplotype in the British and Irish P. infestans populations associated with the 6_A1 genotype. Historically associated with the previously panglobally distributed clonal lineage US-1, the Ib haplotype has not been detected (with the exception of a single isolate in the mid 1990s) in the British or Irish P. infestans populations since the early 1980s. The 6_A1 isolates analysed possessed mtDNA Ib, but were otherwise quite unlike US-1, having the Pep allozyme genotype 96/96 and novel RG57 and SSR fingerprints. These genetic characteristics strongly suggest that the appearance of the 6_A1 genotype in these populations has resulted from migration (possibly after a recombination event elsewhere). This study highlights the advantages of utilising a range of different markers in pathogen monitoring.

Late blight, caused by Phytophthora infestans, affects tomatoes and potatoes in Florida during the winter–spring crop season. During the 2005 season, severe late blight epidemics were observed in Florida prompting our survey. Isolates from 2005 to 2007 were characterized phenotypically based on growth on three media, mating type, pathogenicity, and sensitivity to metalaxyl and genotypically based on two isozymes, mitochondrial DNA (mtDNA), and genomic profiling using the RG57 probe. Isolates collected in this survey were all A2, mtDNA Ia, and either 100/100 (2005), or 100/122 (2006/2007) at the Gpi locus, and homozygous 100 at the Pep locus. Novel genotypes infecting tomato in Florida were observed based on the Gpi locus and RG57 genomic profile. We propose US-20 for the collection of clonal isolates recovered during the 2005 season and US-21 for clones recovered during 2006 and 2007. In addition to these novel genotypes recovered from tomato, one isolate was recovered from potato representing the US-8 clonal lineage. The findings of the survey in south Florida and their implications are presented.

Potato production on the island of Jersey, in the English Channel, is dominated by Jersey Royal, a selection of the early cultivar International Kidney. Jersey Royal is very susceptible to Phytophthora infestans, the cause of potato late blight, and Jersey’s climate is frequently conducive to infection. During 2004–2006, isolates of P. infestans were obtained from Jersey Royal plant samples from 41 different sites (24 crops, one infected tuber, 16 volunteers) and from five outdoor tomato crops and characterised by mating type, mtDNA haplotype, Gpi and Pep allozyme genotype, metalaxyl resistance and RG57 fingerprint. A subset of 17 isolates was additionally characterised by simple sequence repeat (SSR). All but one isolate from potato belonged to a single A1 multilocus genotype or a variant. The five isolates from tomato represented three distinct genotypes, none of which was found on potato and included one which was A2 mating type. The populations of P. infestans on potato and tomato in Jersey appear distinct, with that on potato being highly clonal.

Competition between genotypes of Phytophthora infestans was studied by inoculating potato cultivars with differing susceptibility to late blight in field experiments over three years in Northern Ireland, UK, and Michigan, USA. Multiple isolates of six genotype groups of P. infestans were chosen from the local populations in both N. Ireland and Michigan for inoculation of separate field trials planted in 2003, 2004 and 2005. Four cultivars were used in each trial; two (susceptible cv. Atlantic and the partially resistant cv. Stirling) were common to both locations, whereas the two additional cultivars (with partial resistance to late blight) were cvs Sante and Milagro in N. Ireland and cvs Pike and Jacqueline Lee in Michigan. Single-lesion isolates of P. infestans were obtained from leaves at 1% level of infection, characterized using pre-assigned markers and re-assigned to their respective genotype groups. Extreme selection occurred within the population of genotypes of P. infestans in N. Ireland in each year, with different genotype groups dominating the infection of different cultivars. Selection was observed on all cultivars tested, but was greatest on the more resistant cultivars. Over the 3 years, all of the 114 isolates obtained from cv. Milagro belonged to a single group, whereas among the 118 isolates from cv. Atlantic all six groups were represented. By contrast, in Michigan, the US-8 genotype dominated infection in all cultivars in each year; only 12 of 374 isolates characterized belonged to other genotypes (11 US-14 and a single US-10 isolate).

Late blight, caused by Phytophthora infestans, is one of the most destructive diseases of tomato in Taiwan. A total of 655 isolates of P. infestans, including 29 isolates from potato, was collected from major tomato and potato production areas of Taiwan during 1991 to 2006. Isolates were characterized for i their pathogenicity, mating type, in vitro metalaxyl ; sensitivity and molecular genotype (including allo- i zyme pattern, mitochondrial genomic haplotype and DNA fingerprint) to monitor population changes in P. infestans. The population of P. infestans in Taiwan underwent a dramatic genetic shift in the 1997-1998 cool growing season. Isolates collected from tomato before 1997 were aggressive to tomato but not potato; most isolates obtained after 1998, were aggressive to both hosts. Metalaxyl sensitivity of isolates changed from sensitive/intermediate before 1997 to resistant since 1998. Similarly, the isolates obtained before 1997 were all US-1 clonal lineage (including variants US-1.1, US-1.2, US-1.3 and US-1.4). During the 1997-1998 cool growing season, the US-11 clonal lineage and the TW-1 genotype appeared, possibly introduced on imported table potatoes. The US-11 lineage spread rapidly and since 1999 has almost completely displaced the old population in Taiwan. Mating type determined by pairing with Al and A2 reference isolages of P. parasitica, showed all isolates were of the Al mating type, suggesting that the A2 mating type had not become established in Taiwan. The increasing percentage (up to 42.3% in 2006) of the US-11 variants (including US-11.1, US-11.2, US-11.3 and US-11.4) implied that genomic diversity of the pathogen is changing quickly. Therefore, it is important to continuously monitor the population changes of P. infestans and develop an integrated management strategy for this disease.

The oomycete, Phytophthora infestans, is a devastating pathogen of potato worldwide. Several genotypes of P. infestans are able to infect other cultivated and weed species of the family Solanaceae and cause symptoms similar to late blight. Changes in P. infestans populations have stimulated investigations to determine if potato strains from new immigrant populations infect nonpotato hosts more often than those from the older population. Expansion of the effective host range may be one of the mechanisms involved in pathogenic changes in natural populations of P. infestans and to determine its significance, it is necessary to establish if the pathogen strains on nonpotato hosts represent distinct genotypes/populations or are freely exchanging with those on potato. This article reports characterization of P. infestans isolates from four solanaceous hosts (black nightshade, hairy nightshade, petunia, and tomato) growing within and around fields of blighted potatoes in four U.S. locations and one U.K. location and their comparison with isolates collected from adjacent infected potatoes. Isolates were characterized for mitochondrial DNA haplotype, mating type, metalaxyl resistance, allozymes of glucose-6-phosphate isomerase and peptidase, and DNA fingerprint with the RG57 probe. Analysis showed close similarity of the petunia, hairy and black nightshade isolates to potato isolates. However, tomatoes from New Jersey and Pennsylvania, respectively, were infected by two distinct and previously unreported pathogen genotoypes, which had quite different fingerprints from P. infestans isolates recovered from nearby infected potatoes. Potato growers should be aware that both weed and cultivated solanaceous species can be infected with P. infestans and may serve as clandestine reservoirs of inoculum. Because some of these plants do not show conspicuous symptoms, they may escape detection and fail to be either removed or treated and so may play a major role in the introduction and spread of pathogens to new locations.

A total of 204 isolates of Phytophthora infestans from Northern Ireland, almost all from commercial potato crops, were collected over 5 years (1998-2002). Phenotypic diversity was assessed using mating type and metalaxyl resistance; genotypic diversity was assessed using two allozyme loci (glucose-6-phosphate isomerase, Gpi, and peptidase, Pep), mitochondrial DNA haplotype and the multilocus RFLP probe RG57. All isolates were A1 mating type and Gpi 100/100. The majority were Pep 100/100, but four Pep 83/100 and six Pep 96/100 isolates were identified. Three mtDNA haplotypes were detected; haplotype IIa was the most common, but each year up to 2001 its frequency declined, with a concomitant increase in Ia isolates. Three isolates had the rare haplotype IIb, but this was only detected in 1998. Metalaxyl resistance and mtDNA haplotype were markedly associated: most haplotype Ia isolates were metalaxyl-resistant, whereas haplotype IIa was more commonly associated with metalaxyl sensitivity. Analysis of a subsample of 91 isolates revealed nine RG57 genotypes, three associated exclusively with haplotype IIa and six exclusively with haplotype Ia. The most common RG57 genotype (51% of isolates) comprised both metalaxyl-resistant and -sensitive haplotype IIa isolates. However, of haplotype Ia isolates, all metalaxyl-resistant phenotypes belonged to one of four RG57 types, one of which was the second most frequent overall (29% of isolates), while all metalaxyl-sensitive isolates belonged to one of two other types. The P. infestans population in Northern Ireland appears to consist of a limited number of clones related to, but differentiated from, the populations in mainland Britain and elsewhere in Europe.

Isolates of Phytophthora infestans were obtained from late blighted plants from several potato-growing regions of Uruguay in 1998 and 1999. Of these, 25 representative isolates (4 from 1998, 21 from 1999) from the main potato-growing areas of the country, were characterised in terms of mating type, metalaxyl resistance, allozyme genotype, mitochondrial haplotype, RG57 fingerprint (1999 isolates only) and pathotype. All isolates proved to be A2 mating type, monomorphic and homozygous at the loci coding for glucose-6-phosphate isomerase and peptidase (Gpi 100/100, Pep 100/100) and to possess mitochondrial haplotype IIa. Metalaxyl-resistant isolates constituted 92% of the total. All the 1999 isolates possessed the same RG57 fingerprint, which was that previously reported as associated with the clonal lineage BR-1 from Brazil and Bolivia, which is also A2, Gpi 100/100, Pep 100/100. Most of the isolates displayed broad-spectrum virulence and five carried virulence to 10 of the 11 R genes tested despite the absence of R genes in commercially grown potato cultivars. It was concluded that the Uruguayan P. infestans isolates resembled isolates from neighbouring South American countries, notably Brazil, and belong to the new populations of the pathogen now predominant in many countries.

In recent years, late blight, caused by Phytophthora infestans (Mont) De Bary, has increased in severity in many parts of the world, and this has been associated with migrations which have introduced new, arguably more aggressive, populations of the pathogen. In Taiwan, late blight has been endemic on outdoor tomato crops grown in the highlands since the early 1900s, but recent epidemics have been more damaging. To ascertain the present status of the Taiwanese population of P infestans, 139 isolates of the pathogen collected and maintained by the Asian Vegetable Research and Development Center (AVRDC) were characterized using mating type, metalaxyl sensitivity, allozyme genotype, mitochondrial haplotype and RFLP fingerprinting. Up to 1997, all isolates were found to belong to the old clonal lineage of P infestans (US-1 and variants), but in isolates from 1998 a new genotype appeared, and by 2000 this had apparently completely displaced the old population. This new genotype was an A1 mating type and has the dilocus allozyme genotype 100/100/111, 100/100 for the loci coding for glucose-6-phosphate isomerase and peptidase, respectively. These characters, together with RG57 fingerprinting, indicated that these isolates belonged to the US-11 clonal lineage, a minority (11%) being a previously unreported variant of US-11. Whereas metalaxyl-resistant isolates were not detected in the old population, 96% of the new genotypes proved resistant, with the remainder being intermediate in sensitivity. It may be inferred from this sudden, marked change in the characteristics of the Taiwanese P infestans that a new population of the pathogen was introduced around 1997-98 and that this may well have already been metalaxyl-resistant when it arrived, although a role for in situ selection cannot be excluded.

Neurons from chemosensory hairs on the galeae of adult Colorado potato beetle (CPB), Leptinotarsa decemlineata(Say), were investigated for responses to glycoalkaloids of the family Solanaceae. While solanine and tomatine elicited irregular firing by multiple neurons and bursting activity at 1 mM concentration in most sensory hairs, stimulation with leptine I resulted in consistently high-frequency, slowly adapting responses with a dose-dependent effect between 0.03 and 0.3 mM concentrations. Responses to a mixture of solanine and leptine I suggested possible modification of the leptine I response by other glycoalkaloids, resulting in reduced neural activity relative to leptine I alone. These results establish a method for specifically evaluating leptine I and other glycoalkaloids for effects on feeding behavior of CPB and provide a sensory component for incorporating deterrent chemistry into biorational control methods for the CPB.

Woody nightshade (Solanum dulcamara) is a common hedgerow herbaceous perennial in the United States, one of only three native Solanum spp. S. dulcamara is a known host of Phytophthora infestans (3), but infection is rarely reported. There is a U.S. record from Maryland (2); in 1947, Peterson (4) stated that this species had never been found blighted in its natural habitat, although in 1960 it was listed as a host of P. infestans in New York (1). The A2 mating type has not been reported on this host. On 2 July, 2009, leaf lesions similar to those of P. infestans on potato were found on wild S. dulcamara at Riverhead, NY. The plant was growing in a home garden within 10 m of potato and tomato plants infected with P. infestans. When two infected leaves of S. dulcamara were incubated for 24 h under high humidity, a pathogen growth developed around the lesion margins that was characterized by hyaline mycelium bearing lemon-shaped sporangia that released motile zoospores after chilling in water, which is consistent with P. infestans. The caducous and limoniform to ovoid sporangia were 39 to 50 μm (average 45 μm) × 26 to 28 μm (average 27 μm) with a length/breadth ratio of 1.66. No oospores were observed. Three isolates were obtained from this plant during July 2009. Growth on rye agar was indistinguishable from that of local tomato isolates of P. infestans. Detached leaflets of S. dulcamara and S. tuberosum, inoculated with the woody nightshade isolates and kept in a humid chamber, became infected and developed profuse sporulation within 5 days. The pathogen isolated was confirmed as P. infestans by morphological, biochemical, and molecular characteristics. Inoculations of attached leaves of potted S. dulcamara plants resulted in necrotic lesions with many sporangia; sporulation also developed on inoculated, attached, and detached tomato leaves. P. infestans was reisolated and identity confirmed as before. The three isolates were A2 mating type, metalaxyl-resistant, mitochondrial haplotype Ia. All were glucose-6-phosphate isomerase 100/122 and peptidase 100/100, as confirmed with single-spore isolates. RG57 fingerprint analysis confirmed that isolates from woody nightshade, tomato, and potato obtained from the same and nearby sites were identical. Although P. infestans in the United States belongs to the new population, which may infect a wider host range than the old US-1 clonal lineage, S. dulcamara infections have only been found when late blight is already widespread in neighboring fields and there is no evidence to suggest that woody nightshade acts as an overwintering host in the United States. References: (1) Anonymous. Index of Plant Diseases in the United States. Page 456 in: Agric. Handb. No. 165, 1960 (2) C. Cox. Phytopathology 38:575, 1948. (3) D. C. Erwin and O. K. Ribeiro. Page 190 in: Phytophthora Diseases Worldwide. The American Phytopathological Society, St. Paul, MN, 1996. (4) L. C. Peterson. Am. Potato J. 24:188, 1947.