Comparison of Mammography and Mammography with Supplemental Whole-Breast US Tomography for Cancer Detection in Patients with Dense Breasts.

BACKGROUND: Women with dense breasts benefit from supplemental cancer screening with US, but US has low specificity. PURPOSE: To evaluate the performance of breast US tomography (UST) combined with full-field digital mammography (FFDM) compared with FFDM alone for breast cancer screening in women with dense breasts. MATERIALS AND METHODS: This retrospective multireader multicase study included women with dense breasts who underwent FFDM and UST at 10 centers between August 2017 and October 2019 as part of a prospective case collection registry. All patients in the registry with cancer were included; patients with benign biopsy or negative follow-up imaging findings were randomly selected for inclusion. Thirty-two Mammography Quality Standards Act-qualified radiologists independently evaluated FFDM followed immediately by FFDM plus UST for suspicious findings and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. The superiority of FFDM plus UST versus FFDM alone for cancer detection (assessed with area under the receiver operating characteristic curve [AUC]), BI-RADS 4 sensitivity, and BI-RADS 3 sensitivity and specificity were evaluated using the two-sided significance level of α = .05. Noninferiority of BI-RADS 4 specificity was evaluated at the one-sided significance level of α = .025 with a -10% margin. RESULTS: Among 140 women (mean age, 56 years ±10 [SD]; 36 with cancer, 104 without), FFDM plus UST achieved superior performance compared with FFDM alone (AUC, 0.60 [95% CI: 0.51, 0.69] vs 0.54 [95% CI: 0.45, 0.64]; P = .03). For FFDM plus UST versus FFDM alone, BI-RADS 4 mean sensitivity was superior (37% [428 of 1152] vs 30% [343 of 1152]; P = .03) and BI-RADS 4 mean specificity was noninferior (82% [2741 of 3328] vs 88% [2916 of 3328]; P = .004). For FFDM plus UST versus FFDM, no difference in BI-RADS 3 mean sensitivity was observed (40% [461 of 1152] vs 33% [385 of 1152]; P = .08), but BI-RADS 3 mean specificity was superior (75% [2491 of 3328] vs 69% [2299 of 3328]; P = .04). CONCLUSION: In women with dense breasts, FFDM plus UST improved cancer detection by radiologists versus FFDM alone. Clinical trial registration nos. NCT03257839 and NCT04260620 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Mann in this issue.

Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset.

Purpose: We demonstrate continuous learning and assess its impact on the performance of artificial intelligence of breast dynamic contrast-enhanced magnetic resonance imaging in the task of distinguishing malignant from benign lesions on an independent clinical test dataset. Approach: The study included 1979 patients with 1990 lesions who underwent breast MR imaging during 2015, 2016, and 2017, retrospectively collected under an IRB-approved protocol; there were 1494 malignant and 496 benign lesions based on histopathology. AI was conducted in the task of distinguishing malignant and benign lesions, and independent testing was performed to assess the effect of increasing the numbers of training cases. Five training sets mimicking clinical implementation of continuous AI learning included cases from (1) first quarter of 2015, (2) first half of 2015, (3) all 2015, (4) all 2015 and first half of 2016, and (5) all 2015 and 2016. All classifiers were evaluated on the 2017 independent test set. The area under the ROC curve (AUC) served as the performance metric and was calculated over all lesions in the test set, as well as only mass lesions and only non-mass enhancements. The Mann-Kendall test was used to determine if continuous learning resulted in a positive trend in classification performance. P < 0.05 was considered to be statistically significant. Results: Over the continuous training period, the selected feature subsets tended to become more similar and stable. Performance of the five training conditions on the independent test dataset yielded AUCs of 0.86 (95% CI: [0.83,0.90]), 0.87 (95% CI: [0.83,0.90]), 0.88 (95% CI: [0.84,0.91]), 0.89 (95% CI: [0.85,0.92]), and 0.89 (95% CI: [0.86,0.92]). The Mann-Kendall test indicated a statistically significant positive trend ( P = 0.0167 ) in classification performance with continuous learning. Conclusions: Improved diagnostic performance over time was observed when continuous learning of AI was implemented on an independent clinical test dataset.

Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths.

Radiomic features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As medical centers transition from 1.5 T to 3.0 T magnetic resonance (MR) imaging, it is beneficial to identify potentially robust radiomic features across field strengths because images acquired at different field strengths could be used in machine learning models. Dynamic contrast-enhanced MR images of benign breast lesions and hormone receptor positive/HER2-negative (HR+/HER2-) breast cancers were acquired retrospectively, yielding 612 unique cases: 150 and 99 benign lesions imaged at 1.5 T and 3.0 T, and 223 and 140 HR+/HER2- cancerous lesions imaged at 1.5 T and 3.0 T, respectively. In addition, an independent set of seven lesions imaged at both field strengths, three benign lesions and four HR+/HER2- cancers, was analyzed separately. Lesions were automatically segmented using a 4D fuzzy c-means method; thirty-eight radiomic features were extracted. Feature value distributions were compared by cancer status and imaging field strength using the Kolmogorov-Smirnov test. Features that did not demonstrate a statistically significant difference were considered to be potentially robust. The area under the receiver operating characteristic curve (AUC), for the task of classifying lesions as benign or HR+/HER2- cancer, was determined for each feature at each field strength. Three features were found to be both potentially robust across field strength and of high classification performance, i.e., AUCs statistically greater than 0.5 in the classification task: one shape feature (irregularity), one texture feature (sum average) and one enhancement variance kinetics features (enhancement variance increasing rate). In the demonstration set of lesions imaged at both field strengths, two of the three potentially robust features showed qualitative agreement across field strength. These findings may contribute to the development of computer-aided diagnosis models that are robust across field strength for this classification task.

Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients.

The purpose of this study was to evaluate breast MRI radiomics in predicting, prior to any treatment, the response to neoadjuvant chemotherapy (NAC) in patients with invasive lymph node (LN)-positive breast cancer for two tasks: (1) prediction of pathologic complete response and (2) prediction of post-NAC LN status. Our study included 158 patients, with 19 showing post-NAC complete pathologic response (pathologic TNM stage T0,N0,MX) and 139 showing incomplete response. Forty-two patients were post-NAC LN-negative, and 116 were post-NAC LN-positive. We further analyzed prediction of response by hormone receptor subtype of the primary cancer (77 hormone receptor-positive, 39 HER2-enriched, 38 triple negative, and 4 cancers with unknown receptor status). Only pre-NAC MRIs underwent computer analysis, initialized by an expert breast radiologist indicating index cancers and metastatic axillary sentinel LNs on DCE-MRI images. Forty-nine computer-extracted radiomics features were obtained, both for the primary cancers and for the metastatic sentinel LNs. Since the dataset contained MRIs acquired at 1.5 T and at 3.0 T, we eliminated features affected by magnet strength using the Mann-Whitney U-test with the null-hypothesis that 1.5 T and 3.0 T samples were selected from populations having the same distribution. Bootstrapping and ROC analysis were used to assess performance of individual features in the two classification tasks. Eighteen features appeared unaffected by magnet strength. Pre-NAC tumor features generally appeared uninformative in predicting response to therapy. In contrast, some pre-NAC LN features were able to predict response: two pre-NAC LN features were able to predict pathologic complete response (area under the ROC curve (AUC) up to 0.82 [0.70; 0.88]), and another two were able to predict post-NAC LN-status (AUC up to 0.72 [0.62; 0.77]), respectively. In the analysis by a hormone receptor subtype, several potentially useful features were identified for predicting response to therapy in the hormone receptor-positive and HER2-enriched cancers.

Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.

BACKGROUND: As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists. METHODS: Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task. RESULTS: In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies. CONCLUSION: On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.

Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset.

RATIONALE AND OBJECTIVES: The objective of this study was to demonstrate improvement in distinguishing between benign lesions and luminal A breast cancers in a large clinical breast magnetic resonance imaging database by using quantitative radiomics over maximum linear size alone. MATERIALS AND METHODS: In this retrospective study, 264 benign lesions and 390 luminal A breast cancers were automatically segmented from dynamic contrast-enhanced breast magnetic resonance images. Thirty-eight radiomic features were extracted. Tenfold cross validation was performed to assess the ability to distinguish between lesions and cancers using maximum linear size alone and lesion signatures obtained with stepwise feature selection and a linear discriminant analysis classifier including and excluding size features. Area under the receiver operating characteristic curve (AUC) was used as the figure of merit. RESULTS: For maximum linear size alone, AUC and 95% confidence interval was 0.684 (0.642, 0.724) compared to 0.728 (0.687, 0.766) (P = 0.005) and 0.729 (0.689, 0.767) (P = 0.005) for lesion signature feature selection protocols including and excluding size features, respectively. The features of irregularity and entropy were chosen in all folds when size features were included and excluded. AUC for the radiomic signature using feature selection from all features was statistically equivalent to using feature selection from all features excluding size features, within an equivalence margin of 2%. CONCLUSIONS: Inclusion of multiple radiomic features, automatically extracted from magnetic resonance images, in a lesion signature significantly improved the ability to distinguish between benign lesions and luminal A breast cancers, compared to using maximum linear size alone. The radiomic features of irregularity and entropy appear to play an important but not a solitary role within the context of feature selection and computer-aided diagnosis.

Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers.

Radiomic features extracted from magnetic resonance (MR) images have potential for diagnosis and prognosis of breast cancer. However, presentation of lesions on images may be affected by biopsy. Thirty-four nonsize features were extracted from 338 dynamic contrast-enhanced MR images of benign lesions and luminal A cancers (80 benign/34 luminal A prebiopsy; 46 benign/178 luminal A postbiopsy). Feature value distributions were compared by biopsy condition using the Kolmogorov-Smirnov test. Classification performance was assessed by biopsy condition in the task of distinguishing between lesion types using the area under the receiver operating characteristic curve (AUCROC) as performance metric. Superiority and equivalence testing of differences in AUCROC between biopsy conditions were conducted using Bonferroni-Holm-adjusted significance levels. Distributions for most nonsize features for each lesion type failed to show a statistically significant difference between biopsy conditions. Fourteen features outperformed random guessing in classification. Their differences in AUCROC by biopsy condition failed to reach statistical significance, but we were unable to prove equivalence using a margin of Δ AUCROC = ± 0.10 . However, classification performance for lesions imaged either prebiopsy or postbiopsy appears to be similar when taking into account biopsy condition.

Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breast Tissue.

OBJECTIVE: The purpose of this study was to compare diagnostic accuracy and interpretation time of screening automated breast ultrasound (ABUS) for women with dense breast tissue without and with use of a recently U.S. Food and Drug Administration-approved computer-aided detection (CAD) system for concurrent read. MATERIALS AND METHODS: In a retrospective observer performance study, 18 radiologists interpreted a cancer-enriched set (i.e., cancer prevalence higher than in the original screening cohort) of 185 screening ABUS studies (52 with and 133 without breast cancer). These studies were from a large cohort of ABUS-screened patients interpreted as BI-RADS density C or D. Each reader interpreted each case twice in a counterbalanced study, once without the CAD system and once with it, separated by 4 weeks. For each case, each reader identified abnormal findings and reported BI-RADS assessment category and level of suspicion for breast cancer. Interpretation time was recorded. Level of suspicion data were compared to evaluate diagnostic accuracy by means of the Dorfman-Berbaum-Metz method of jackknife with ANOVA ROC analysis. Interpretation times were compared by ANOVA. RESULTS: The ROC AUC was 0.848 with the CAD system, compared with 0.828 without it, for a difference of 0.020 (95% CI, -0.011 to 0.051) and was statistically noninferior to the AUC without the CAD system with respect to a margin of -0.05 (p = 0.000086). The mean interpretation time was 3 minutes 33 seconds per case without the CAD system and 2 minutes 24 seconds with it, for a difference of 1 minute 9 seconds saved (95% CI, 44-93 seconds; p = 0.000014), or a reduction in interpretation time to 67% of the time without the CAD system. CONCLUSION: Use of the concurrent-read CAD system for interpretation of screening ABUS studies of women with dense breast tissue who do not have symptoms is expected to make interpretation significantly faster and produce noninferior diagnostic accuracy compared with interpretation without the CAD system.

Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer.

BACKGROUND: The hypothesis of this study was that MRI-based radiomics has the ability to predict recurrence-free survival "early on" in breast cancer neoadjuvant chemotherapy. METHODS: A subset, based on availability, of the ACRIN 6657 dynamic contrast-enhanced MR images was used in which we analyzed images of all women imaged at pre-treatment baseline (141 women: 40 with a recurrence, 101 without) and all those imaged after completion of the first cycle of chemotherapy, i.e., at early treatment (143 women: 37 with a recurrence vs. 105 without). Our method was completely automated apart from manual localization of the approximate tumor center. The most enhancing tumor volume (METV) was automatically calculated for the pre-treatment and early treatment exams. Performance of METV in the task of predicting a recurrence was evaluated using ROC analysis. The association of recurrence-free survival with METV was assessed using a Cox regression model controlling for patient age, race, and hormone receptor status and evaluated by C-statistics. Kaplan-Meier analysis was used to estimate survival functions. RESULTS: The C-statistics for the association of METV with recurrence-free survival were 0.69 with 95% confidence interval of [0.58; 0.80] at pre-treatment and 0.72 [0.60; 0.84] at early treatment. The hazard ratios calculated from Kaplan-Meier curves were 2.28 [1.08; 4.61], 3.43 [1.83; 6.75], and 4.81 [2.16; 10.72] for the lowest quartile, median quartile, and upper quartile cut-points for METV at early treatment, respectively. CONCLUSION: The performance of the automatically-calculated METV rivaled that of a semi-manual model described for the ACRIN 6657 study (published C-statistic 0.72 [0.60; 0.84]), which involved the same dataset but required semi-manual delineation of the functional tumor volume (FTV) and knowledge of the pre-surgical residual cancer burden.

Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breasts: Reader Study of Mammography-Negative and Mammography-Positive Cancers.

OBJECTIVE: The objective of our study was to assess and compare, in a reader study, radiologists' performance in the detection of breast cancer using full-field digital mammography (FFDM) alone and using FFDM with 3D automated breast ultrasound (ABUS). MATERIALS AND METHODS: In this multireader, multicase, sequential-design reader study, 17 Mammography Quality Standards Act-qualified radiologists interpreted a cancer-enriched set of FFDM and ABUS examinations. All imaging studies were of asymptomatic women with BI-RADS C or D breast density. Readers first interpreted FFDM alone and subsequently interpreted FFDM combined with ABUS. The analysis included 185 cases: 133 noncancers and 52 biopsy-proven cancers. Of the 52 cancer cases, the screening FFDM images were interpreted as showing BI-RADS 1 or 2 findings in 31 cases and BI-RADS 0 findings in 21 cases. For the cases interpreted as BI-RADS 0, a forced BI-RADS score was also given. Reader performance was compared in terms of AUC under the ROC curve, sensitivity, and specificity. RESULTS: The AUC was 0.72 for FFDM alone and 0.82 for FFDM combined with ABUS, yielding a statistically significant 14% relative improvement in AUC (i.e., change in AUC = 0.10 [95% CI, 0.07-0.14]; p < 0.001). When a cutpoint of BI-RADS 3 was used, the sensitivity across all readers was 57.5% for FFDM alone and 74.1% for FFDM with ABUS, yielding a statistically significant increase in sensitivity (p < 0.001) (relative increase = 29%). Overall specificity was 78.1% for FFDM alone and 76.1% for FFDM with ABUS (p = 0.496). For only the mammography-negative cancers, the average AUC was 0.60 for FFDM alone and 0.75 for FFDM with ABUS, yielding a statistically significant 25% relative improvement in AUC with the addition of ABUS (p < 0.001). CONCLUSION: Combining mammography with ABUS, compared with mammography alone, significantly improved readers' detection of breast cancers in women with dense breast tissue without substantially affecting specificity.

Clinically missed cancer: how effectively can radiologists use computer-aided detection?

OBJECTIVE: The purpose of this study was to determine the effectiveness with which radiologists can use computer-aided detection (CADe) to detect cancer missed at screening. MATERIALS AND METHODS: An observer study was performed to measure the ability of radiologists to detect breast cancer on mammograms with and without CADe. The images in the study were from 300 analog mammographic examinations. In 234 cases the mammograms were read clinically as normal and free of cancer for at least 2 subsequent years. In the other 66 cases, cancers were missed clinically. In 256 cases, current and previous mammograms were available. Eight radiologists read the dataset and recorded a BI-RADS assessment, the location of the lesion, and their level of confidence that the patient should be recalled for diagnostic workup for each suspicious lesion. Jackknife alternative free-response receiver operating characteristic analysis was used. RESULTS: The jackknife alternative free-response receiver operating characteristic figure of merit was 0.641 without aid and 0.659 with aid (p = 0.06; 95% CI, -0.001 to 0.036). The sensitivity increased 9.9% (95% CI, 3.4-19%) and the callback rate 12.1% (95% CI, 7.3-20%) with CADe. Both increases were statistically significant (p < 0.001). Radiologists on average ignored 71% of correct computer prompts. CONCLUSION: Use of CADe can increase radiologist sensitivity 10% with a comparable increase in recall rate. There is potential for CADe to have a bigger clinical impact because radiologists failed to recognize a correct computer prompt in 71% of missed cancer cases [corrected].

Significant modification of the I(-)3 Lewis base character in the ß-cyclodextrin polyiodide inclusion complex with Co2+ ion: an FT-Raman investigation.

The ß-cyclodextrin (ß-CD) polyiodide inclusion complex (ß-CD)(2)·Co(0.5)·I(7)·21H(2)O has been synthesized, characterized and further investigated via FT-Raman spectroscopy in the temperature range of 30-120°C. The experimental results point to the coexistence of I(-)(7) units (I(2)·I(-)(3)·I(2)) that seem not to interact with the Co(2+) ions and I(-)(7) units that display such interactions. The former units exhibit a disorder-order transition of both their I(2) molecules above 60°C due to a symmetric charge-transfer interaction with the central I(-)(3) [I(2)←I(-)(3)→I(2)], whereas in the latter units only one of the two I(2) molecules becomes well-ordered above 30°C. The other I(2) molecule remains disordered presenting no charge-transfer phenomena. The Co(2+) ion induces a considerable asymmetry on the geometry of the I(-)(3) anion and a significant modification of its Lewis base character. Complementary dielectric measurements suggest no important involvement of H···I contacts in the observed modification of the I(-)(3) electron-transfer properties.

Metal-heptaiodide interactions in cyclomaltoheptaose (beta-cyclodextrin) polyiodide complexes as detected via Raman spectroscopy.

The Raman spectra of the cyclomaltoheptaose (beta-cyclodextrin, beta-CD) polyiodide complexes (beta-CD)(2).NaI(7).12H(2)O, (beta-CD)(2).RbI(7).18H(2)O, (beta-CD)(2).SrI(7).17H(2)O, (beta-CD)(2).BiI(7).17H(2)O and (beta-CD)(2).VI(7).14H(2)O (named beta-M, M stands for the corresponding metal) are investigated in the temperature range of 30-140 degrees C. At room temperature all systems show an initial strong band at 178 cm(-1) that reveals similar intramolecular distances of the disordered I(2) units (approximately 2.72 A). During the heating process beta-Na and beta-Rb display a gradual shift of this band to the final single frequency of 166 cm(-1). In the case of beta-Sr and beta-Bi, the band at 178 cm(-1) is shifted to the final single frequencies of 170 and 172 cm(-1), respectively. These band shifts imply a disorder-order transition of the I(2) units whose I-I distance becomes elongated via a symmetric charge-transfer interaction I(2)<--I3(-)-->I(2). The different final frequencies correspond to different bond lengthening of the disordered I(2) units during their transformation into well-ordered ones. In the Raman spectra of beta-V, the initial band at 178 cm(-1) is not shifted to a single band but to a double one of frequencies 173 and 165 cm(-1), indicating a disorder-order transition of the I(2) molecules via a non-symmetric charge-transfer interaction I(2)<--I3(-)-->I(2). The above spectral data show that the ability of I3(-) to donate electron density to the attached I(2) units is determined by the relative position of the different metal ions and their ionic potential q/r. The combination of the present results with those obtained from our previous investigations reveals that cations with an ionic potential that is lower than approximately 1.50 (Cs(+), Rb(+), Na(+), K(+) and Ba(2+)) do not affect the Lewis base character of I3(-). However, when the ionic potential of the cation is greater than approximately 1.50 (Li(+), Sr(2+), Cd(2+), Bi(3+) and V(3+)), the M(n+)...I3(-) interactions become significant. In the case of a face-on position of the metal (Sr(2+), Bi(3+)) relative to I3(-), the charge-transfer interaction is symmetric. On the contrary, when the metal (Li(+), Cd(2+), V(3+)) presents a side-on position relative to I3(-), the charge-transfer interaction is non-symmetric.

Two interconverting pentaiodide forms in the cyclomaltohexaose (alpha-cyclodextrin) polyiodide inclusion complex with sodium ion: dielectric and Raman spectroscopy studies.

The polycrystalline inclusion complex of cyclomaltohexaose, (alpha-CD)(2) x NaI(5) x 8H(2)O, has been investigated via dielectric spectroscopy over a frequency range of 0-100 kHz and the temperature range of 125-450 K. Additionally, a Raman spectroscopy study was accomplished in the temperature ranges of (i) 153-298 K and (ii) 303-413 K. The ln sigma versus 1/T variation revealed the order-disorder transition of some normal hydrogen bonds to those of a flip-flop type at 200.9 K. From 278.3 up to 357.1K, the progressive transformation (H(2)O)(tightly bound)-->(H(2)O)(easily movable) takes place resulting in an Arrhenius linear increment of the ac-conductivity with activation energy E(a)=0.32 eV. In the range of 357.1-386.1K a second linear part with E(a)=0.55 eV is observed, indicating the contribution of sodium ions via the water-net. The rapid decrease of the ac-conductivity at T>386.1K is due to the removal of the water molecules from the crystal lattice, whereas the abrupt increase at T>414.9 K is caused by the sublimation of iodine. The Raman bands at 160 and 169 cm(-1) indicate the coexistence of (I(2) x I(-) x I(2)) and (I3(-) x I(2)<-->I(2) x I3(-)) units, respectively. The (I3(-) x I(2)<-->I(2) x I3(-)) units are presented as form (I), and their central I(-) ion is disordered in occupancy ratio different from 50/50 (e.g., ...60/40...70/30...). The(I(2) x I(-) x I(2)) units are displayed by the 2 equiv forms (IIa) and (IIb). In (IIa) the central I(-) ion is twofold disordered in an occupancy ratio of 50:50, whereas in (IIb) the central I(-) ion is well-ordered and equidistant from the two I(2) molecules. At low temperatures the transformation (I)-->(IIa) takes place, whereas at high temperatures the inverse one (IIa)-->(I) happens. X-ray powder diffraction and Rietveld analysis revealed a triclinic crystal form with space group P1 and lattice parameters that are in good agreement with the theoretical values.

Anomalous origin of the stenosed circumflex artery from the right coronary artery.

During recent years, anatomic variations of coronary arteries have been described with the aid of elective coronary arteriography. We report a case of anomalous origin of the stenosed circumflex artery from the right coronary artery. The patient was operated upon successfully, and the right internal mammary artery was placed on the circumflex artery.

Kugel's artery: an anatomical and angiographic study using a new technique.

In this study, we tried to resolve the confusion in the literature regarding the existence and course of Kugel's artery. With the aid of a new technique, we studied 100 human hearts ex vivo by radiography and by direct observation through dissection, to demonstrate anatomical and postmortem angiographic findings of Kugel's artery. Kugel's artery was found in only 6 hearts out of 100 (6%). It originated from the proximal left circumflex artery and ended in the right coronary artery in 2 cases; from the right coronary artery and ended in the same artery in 2 cases; from the left circumflex artery and ended in the same artery in 1 case; and from the right coronary artery through the sinus node artery, ending in the left circumflex artery, in 1 case. In all 100 hearts, an anastomotic network of small atrial branches was found in the same area (lower portion of the interatrial septum), connecting the large vessels indirectly. Branches of the sinus node artery in all hearts, and of the atrioventricular node artery in 66 hearts, participated in this network. Our procedure showed the detailed course of Kugel's artery and its course independent from the atrioventricular node artery and from the anastomotic network. In conclusion, in all cases an anastomotic network of small atrial branches courses through the lower interatrial septum and connects indirectly the proximal and distal ends of the larger coronary arteries. Kugel's artery provides an additional direct arterial anastomosis in the same area in 6% of the hearts.

Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance.

Precise segmentation of microcalcifications is essential in the development of accurate mammographic computer-aided diagnosis (CAD) schemes. We have designed a radial gradient-based segmentation method for microcalcifications, and compared it to both the region-growing segmentation method currently used in our CAD scheme and to the watershed segmentation method. Two observer studies were conducted to subjectively evaluate the proposed segmentation method. The first study (A) required observers to rate the segmentation accuracy on a 100-point scale. The second observer evaluation (B) was a preference study in which observers selected their preferred method from three displayed segmentation methods. In study A, the observers gave an average accuracy rating of 88 for the radial gradient-based and 50 for the region-growing segmentation method. In study B, the two observers selected the proposed method 56% and 62% of the time. We also investigated the effect of the proposed segmentation method on the performance of computerized classification scheme in differentiating malignant from benign clustered microcalcifications. The performances of the classification scheme using a linear discriminant analysis (LDA) or a Bayesian artificial neural network classifier both showed statistically significant improvements when using the proposed segmentation method. The areas under the receiver-operating characteristic curves for case-based performance when using the LDA classifier were 0.86 with the proposed segmentation method, 0.80 with the region-growing method, and 0.83 with the watershed method.

The use of a priori information in the detection of mammographic microcalcifications to improve their classification.

In this work, we present a calcification-detection scheme that automatically localizes calcifications in a previously detected cluster in order to generate the input for a cluster-classification scheme developed in the past. The calcification-detection scheme makes use of three pieces of a priori information: the location of the center of the cluster, the size of the cluster, and the approximate number of calcifications in the cluster. This information can be obtained either automatically from a cluster-detection scheme or manually by a radiologist. It is used to analyze only the portion of the mammogram that contains a cluster and to identify the individual calcifications more accurately, after enhancing them by means of a "Difference of Gaussians" filter. Classification performances (patient-based Az=0.92; cluster-based Az=0.72) comparable to those obtained by using manually-identified calcifications (patient-based Az=0.92; cluster-based Az=0.82) can be achieved.