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At least 253 records · Page 14Linked to original sources
PubMed1999

Prostate-specific antigen adjusted for the transition zone volume versus free-to-total prostate-specific antigen ratio in predicting prostate cancer.

BACKGROUND: We performed this study to assess the efficacy of prostate-specific antigen adjusted for the transition zone volume (PSATZ) and free-to-total prostate-specific antigen (PSA) ratio (F/T ratio) in predicting prostate cancer in men with intermediate PSA levels of 4.1-10.0 ng/mL. METHODS: Between March 1997 and September 1998, PSATZ was obtained from 67 patients who underwent ultrasonography guided systemic sextant biopsies and had a PSA of 4.1-10.0 ng/mL. PSATZ was compared with F/T ratio via receiver operating characteristic (ROC) curves. RESULTS: Of 67 patients, 22 (32.8%) had prostate cancer and 45 (67.2%) had benign prostatic hyperplasia (BPH) on pathologic examination. Mean PSA, PSA density, F/T ratio and PSATZ were 7.96+/-2.01ng/mL, 0.28+/-0.14 ng/mL/cc, 0.10+/-0.06 and 0.70+/-0.28 ng/mL/cc in patients with prostate cancer and 6.39+/-1.68 ng/mL, 0.16+/-0.06 ng/mL/cc, 0.15+/-0.05 and 0.29+/-0.10 ng/mL/cc in patients with BPH, respectively. The ROC curve analysis demonstrated that PSATZ predicted the biopsy outcome significantly better than F/T ratio in all 67 patients (P<0.01) and in a subset of 53 men with normal digital rectal examination (P<0.01). With a cut-off value of 0.35 ng/mL/cc, PSATZ had a sensitivity of 86% and a specificity of 89% for predicting prostate cancer. CONCLUSIONS: These results suggest that PSATZ and F/T ratio may be useful in diagnosing prostate cancer with intermediate levels of PSA. Prostate-specific antigen adjusted for the transition zone volume is more accurate than F/T ratio in distinguishing benign prostatic disease from prostate cancer. But large prospective studies are required to assess the precise role of PSATZ and F/T ratio in early prostate cancer detection.

Age Factors↗
PubMed2006

Preoperative plasma osteopontin level as a biomarker complementary to carbohydrate antigen 125 in predicting ovarian cancer.

AIM: New biomarkers other than carbohydrate antigen (CA) 125 are needed for the detection of ovarian cancer. Osteopontin (OPN) is one of the candidates identified by high-throughput complementary DNA microarray techniques. We evaluated the preoperative plasma OPN level as a diagnostic biomarker for ovarian cancer in comparison with CA125. METHODS: Preoperative plasma OPN and CA125 levels were measured and compared in 32 patients with ovarian cancer, 34 patients with benign ovarian tumor, 30 patients with other gynecologic cancers and 31 healthy women. Preoperative plasma OPN levels were also assessed according to tumor stage, the volume of ascites and histological types. The sensitivity and specificity for predicting ovarian cancer was compared between OPN and CA125. RESULTS: Preoperative plasma OPN levels were significantly higher in patients with ovarian cancer than in those with benign ovarian tumor, in other gynecologic patients or in healthy women. Stage IV ovarian cancer patients and ovarian cancer patients with ascites had higher plasma OPN levels than those without ascites and in a lower stage. There was no relation between OPN and the histological type. The sensitivity of preoperative plasma OPN in detecting ovarian cancer was 81.3% and almost reached that of CA125. The specificity was moderate. Sensitivity increased to 93.8% with the combination of CA125, compared to 84.4% with CA125 alone. CONCLUSION: Preoperative OPN is a useful biomarker for predicting ovarian cancer. It is especially useful when used complementary to CA125. Larger studies of patients with ovarian cancer showing a low CA125 level or in early stages of ovarian cancer are needed.

Adolescent↗
PubMed1986

Do the predictions for cancer incidence come true? Experience from Finland.

The observed occurrence of cancer in Finland in 1980 was compared with that predicted by extrapolation of the trends in cancer incidence by site in 1957 to 1968. The predictions had been constructed as 90% confidence intervals. Of the mutually exclusive 33 incidence predictions, 22 (67%) included the observed incidence rate. The incidence rate was successfully predicted for intestinal, pancreatic, and urinary cancers, as well as for lymphomas, whereas incidence forecasts concerning cancers of the lung, breast, and cervix uteri, and melanomas of the skin did not come true. The incidence of male lung cancer did not increase as was expected, breast cancer incidence increased even more than expected, and the decrease in the incidence of cervical cancer was steeper than expected. Because of failure in the population forecast, only 55% of the predictions (90% confidence intervals) for the numbers of new cancer cases included the observed number. For administrative purposes it would be important for the predictions to come true. On the other hand, the successes or failures of the predictions related to specific cancers may be used to study the effect of changes in etiologic factors, diagnostic methods, or the definition of cancer, as well as the effect of mass screening or other intervention measures upon the cancer incidence. Extrapolation as a method of forecasting cancer incidence is adequate as far as the control of random error is concerned when only short-term forecasts, ranging from 5 to 10 years, are needed.

Aged↗
PubMed2003

Breast cancer risk prediction with a log-incidence model: evaluation of accuracy.

OBJECTIVE: We examined whether a breast cancer risk prediction model other than the Gail et al. model performs better at discriminating between women who will and who will not develop the disease. METHODS: We applied the two published versions of the Rosner and Colditz log-incidence model of breast cancer, developed on data from the Nurses' Health Study, to the estimation of 5-year risk for the period 1992 to 1997 in the same cohort. The first version contained reproductive factors only, and the second version contained a more extensive list of risk factors. RESULTS: Both versions of the model fit well. The ratio of expected to observed numbers of cases (E/O) in the first version was 1.00 (95% confidence interval [CI] 0.93-1.07); for the extended version the E/O was 1.01 (95% CI 0.94-1.09). The age-adjusted concordance statistic was 0.57 for the first model version and 0.63 for the extended version. CONCLUSION: The discriminatory accuracy of the two versions was modest, although the addition of the variables in the extended version meaningfully increased the discriminatory accuracy of risk prediction over that found with the more parsimonious model.

Aged↗
PubMed1998

Prostate specific antigen density of the transition zone for predicting pathological stage of localized prostate cancer in patients with serum prostate specific antigen less than 10 ng./ml.

PURPOSE: Prostate specific antigen (PSA) density of the transition zone, which is the density of serum PSA related to the volume of the transition zone, has been recently demonstrated to enhance prostate cancer prediction in patients with intermediate PSA levels. We further investigated the usefulness of PSA-transition zone for predicting extraprostatic extension in clinically localized prostate cancer. MATERIALS AND METHODS: Measuring the transition zone of the prostate by ultrasound using the prolate ellipsoid method, PSA-transition zone values were calculated prospectively in 198 patients with clinically localized prostate cancers and serum PSA less than 10.0 ng./ml. who underwent radical retropubic prostatectomy. The ability of PSA-transition zone to predict extracapsular disease in the surgical specimen was compared to Gleason score, serum PSA, PSA density of the total prostate and percent free PSA using univariate or multivariate analysis as well as receiver operating characteristics curves. RESULTS: A total of 104 patients (52.5%) had pathologically organ confined prostate cancer while 94 of 198 (47.5) had extracapsular disease. PSA-transition zone levels were significantly higher in extracapsular disease than organ confined cancers (0.84 versus 0.42 ng./ml./cc, p <0.00001). Using multivariate analyses PSA-transition zone and Gleason score were the most significant predictors of extracapsular disease. The area under the curve was larger for PSA-transition zone (0.825) than any other parameter (p <0.004 versus PSA density and p <0.001 versus PSA, percent free PSA or Gleason score). A cutoff of 1.00 ng./ml./cc for PSA-transition zone provided 95.1% specificity and 28.8% sensitivity for predicting extracapsular disease. Probability plots using the best combination of independent variables for predicting extraprostatic extension were developed. CONCLUSIONS: These data demonstrate that the use of the PSA-transition zone may be of additional value for indicating which patients with clinically localized prostate cancer and PSA less than 10.0 ng./ml. are at high risk for extracapsular disease.

Aged↗
PubMed1997

Artificial neural networks improve the accuracy of cancer survival prediction.

BACKGROUND: The TNM staging system originated as a response to the need for an accurate, consistent, universal cancer outcome prediction system. Since the TNM staging system was introduced in the 1950s, new prognostic factors have been identified and new methods for integrating prognostic factors have been developed. This study compares the prediction accuracy of the TNM staging system with that of artificial neural network statistical models. METHODS: For 5-year survival of patients with breast or colorectal carcinoma, the authors compared the TNM staging system's predictive accuracy with that of artificial neural networks (ANN). The area under the receiver operating characteristic curve, as applied to an independent validation data set, was the measure of accuracy. RESULTS: For the American College of Surgeons' Patient Care Evaluation (PCE) data set, using only the TNM variables (tumor size, number of positive regional lymph nodes, and distant metastasis), the artificial neural network's predictions of the 5-year survival of patients with breast carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.720; ANN, 0.770; P < 0.001). For the National Cancer Institute's Surveillance, Epidemiology, and End Results breast carcinoma data set, using only the TNM variables, the artificial neural network's predictions of 10-year survival were significantly more accurate than those of the TNM staging system (TNM, 0.692; ANN, 0.730; P < 0.01). For the PCE colorectal data set, using only the TNM variables, the artificial neural network's predictions of the 5-year survival of patients with colorectal carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.737; ANN, 0.815; P < 0.001). Adding commonly collected demographic and anatomic variables to the TNM variables further increased the accuracy of the artificial neural network's predictions of breast carcinoma survival (0.784) and colorectal carcinoma survival (0.869). CONCLUSIONS: Artificial neural networks are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to artificial neural networks to increase prognostic accuracy further. These results are robust across different data sets and cancer sites.

Breast Neoplasms↗
PubMed2006

Tyrosinemia type I treated by NTBC: how does AFP predict liver cancer?

BACKGROUND: Tyrosinemia type I is associated with an increased risk of liver cancer development. The formation of the pathogenic fumarylacetoacetate is prevented by 2-(2-nitro-4-3 trifluoro-methylbenzoyl)-1,3-cyclohexanedione (NTBC). Still, some patients with NTBC treatment develop liver cancer. A rise of alpha-fetoprotein (AFP) is an indicator of liver cancer. AIM: To study the predictive value of AFP in tyrosinemia type I patients for the discrimination between patients at high and low risk of liver cancer development. METHODS: We examined the course of AFP values of 11 Dutch patients with tyrosinemia type I treated by NTBC, of whom four were diagnosed with liver cancer. RESULTS: The four patients with liver cancer had a course of AFP different from the other patients in either velocity of the decrease of AFP, achieving normal AFP and/or having a rise of AFP concentrations. CONCLUSION: Apart from a rise of AFP, a slow AFP decrease, and never normalizing levels of AFP are important predictors of liver cancer development in further life.

Child↗
PubMed2006

How well does the Gleason score predict prostate cancer death? A 20-year followup of a population based cohort in Sweden.

PURPOSE: Adenocarcinoma of the prostate is the most common cancer among men in Western countries. Although the prognostic heterogeneity of prostate cancer is enormous, clinically insignificant aggressive prostate cancers cannot be reliably distinguished. Therefore, identifying prognostic factors is increasingly important, notably among men diagnosed with localized prostate cancer, because many of them may not require aggressive treatment. MATERIALS AND METHODS: We analyzed a population based cohort of 253 men with early stage (T1a-b, Nx, M0) initially untreated prostate cancer diagnosed between 1977 and 1991, before PSA screening was available. Tissue samples were available for 240 patients diagnosed with transurethral resection. During complete followup through September 2003, standardized criteria were used to classify histopathological characteristics, progression and causes of death. RESULTS: Higher Gleason grade, higher nuclear grade and larger tumor volume were independent predictors of death in prostate cancer with monotonous and statistically significant trends (p <0.05). In contrast, the level of Ki-67 - strongly correlated to Gleason score - was not an independent predictor of prostate cancer death. Given a Gleason score of 7 or greater, the probability of dying of prostate cancer was 29%. The corresponding predictive value for Gleason score 8 or greater was 48%. CONCLUSIONS: Although a high Gleason score is a determinant of prostate cancer death, its PPV is relatively low. Thus, further efforts in finding other or complementary indicators of prostate cancer outcome are needed.

Adenocarcinoma↗
PubMed2026

MyGeneRisk Colon: A Web-Based Tool for Personalized Colorectal Cancer Risk Prediction Based on Genetics and Lifestyle.

Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colo n, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portal's architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.

Journal Article↗
PubMed2005

Combined genetic assessment of transforming growth factor-beta signaling pathway variants may predict breast cancer risk.

There is growing evidence that common variants of the transforming growth factor-beta (TGF-beta) signaling pathway may modify breast cancer risk. In vitro studies have shown that some variants increase TGF-beta signaling, whereas others have an opposite effect. We tested the hypothesis that a combined genetic assessment of two well-characterized variants may predict breast cancer risk. Consecutive patients (n = 660) with breast cancer from the Memorial Sloan-Kettering Cancer Center (New York, NY) and healthy females (n = 880) from New York City were genotyped for the hypomorphic TGFBR1*6A allele and for the TGFB1 T29C variant that results in increased TGF-beta circulating levels. Cases and controls were of similar ethnicity and geographic location. Thirty percent of cases were identified as high or low TGF-beta signalers based on TGFB1 and TGFBR1 genotypes. There was a significantly higher proportion of high signalers (TGFBR1/TGFBR1 and TGFB1*CC) among controls (21.6%) than cases (15.7%; P = 0.003). The odds ratio [OR; 95% confidence interval (95% CI)] for individuals with the lowest expected TGF-beta signaling level (TGFB1*TT or TGFB1*TC and TGFBR1*6A) was 1.69 (1.08-2.66) when compared with individuals with the highest expected TGF-signaling levels. Breast cancer risk incurred by low signalers was most pronounced among women after age 50 years (OR, 2.05; 95% CI, 1.01-4.16). TGFBR1*6A was associated with a significantly increased risk for breast cancer (OR, 1.46; 95% CI, 1.04-2.06), but the TGFB1*CC genotype was not associated with any appreciable risk (OR, 0.89; 95% CI, 0.63-1.21). TGFBR1*6A effect was most pronounced among women diagnosed after age 50 years (OR, 2.20; 95% CI, 1.25-3.87). This is the first study assessing the TGF-beta signaling pathway through two common and functionally relevant TGFBR1 and TGFB1 variants. This approach may predict breast cancer risk in a large subset of the population.

Activin Receptors, Type I↗
PubMed2006

Gene expression profiling on lung cancer outcome prediction: present clinical value and future premise.

DNA microarray has been widely used in cancer research to better predict clinical outcomes and potentially improve patient management. The new approach provides accurate tumor classification and outcome predictions, such as tumor stage, metastatic status, and patient survival, and offers some hope for individualized medicine. However, growing evidence suggests that gene-based prediction is not stable and little is known about the prediction power of gene expression profiles compared with well-known clinical and pathologic predictors. This review summarized up-to-date publications in microarray-based lung cancer clinical outcome prediction and conducted secondary analyses for those with sufficient sample sizes and associated clinical information. Among the most commonly used analytic approaches, unsupervised clustering mainly recaptures tumor histology and provides variable degrees of prediction for tumor stage, lymph node status, or survival. Overall, most studies lack an independent validation. Supervised learning and testing generally offer a better prediction. Noted is that when conventional predictors of age, gender, stage, cell type, and tumor grade are considered collectively, the predictive advantage of the gene expression profiles diminishes. We conclude that outcome prediction from gene expression signatures selected by current analytic approaches can be mostly explained by well-known conventional predictors, particularly histologic subtype and grade of differentiation. A strategy for establishing independent or more accurate signatures is commented.

Cell Differentiation↗
PubMed1995

Failure of focal prostate cancer on biopsy to predict focal prostate cancer: the importance of prevalence.

PURPOSE: We investigated whether the reported correlation between focal prostate cancer on biopsy and the presence of clinically insignificant cancer applies to men with an elevated serum prostate specific antigen level or a positive biopsy from an area of palpable abnormality. MATERIALS AND METHODS: A total of 33 men with these findings and focal cancer on biopsy underwent radical perineal prostatectomy, with step-section pathological analysis of all specimens and volume determination of all stage pT2a tumors. RESULTS: Pathological examination revealed that 17 tumors (52%) were stage T3, 10 (30%) stage T2b to c and 6 (18%) stage T2a, with 4 (12%) significant (volumes 0.8 to 1.2 ml.) and 2 (6%) insignificant (volumes 0.1 and 0.3 ml.) neoplasms. Thus, focal prostate cancer on biopsy correlated with significant cancer in 94% of the cases. CONCLUSIONS: In patients with another variable controlling for a high prevalence of significant prostate cancer, the focal nature of a positive biopsy probably reflects only sampling limitations and should not influence treatment. To restrict prostate biopsy to men with a high prevalence of significant cancer, targeted area biopsy alone must replace sextant biopsy in men with a palpable prostatic abnormality but a normal prostate specific antigen level.

Adult↗
PubMed1999

Pretreatment evaluation of adnexal tumors predicting ovarian cancer.

Kobal B, Rakar S, Ribic-Pucelj M, Tomazevie T, Zaletel-Kragelj L. Pretreatment evaluation of adnexal tumors predicting ovarian cancer. The objective of this study was to determine the ability of tumor marker assessment, gray-scale transvaginal with color Doppler ultrasonography to predict ovarian malignancy. One hundred thirty-four subjects with ovarian masses who entered the study prospectively underwent pelvic examination, tumor marker assessment and gray-scale transvaginal with color flow Doppler ultrasonography preoperatively. Malignancy predictors were statistically evaluated with stepwise multiple logistic regression, and the scores from the model were transformed to probability for having a malignant disease. The presence of neovascularization, intracystic papillary projections, elevated serum CA 125, and age over 45 years were significant predictors for malignancy. Positive predictive value (PPV) for the regression model was 89.0%, and negative predictive value (NPV) was 96.8%. Probability for malignancy ranged from 0.004 to 0.991 depending on which covariates were included. Logistic regression analysis of pretreatment diagnostic gray-scale and color Doppler ultrasonographic characteristics, together with CA 125 enabled a creation of probability assessment scale for individual estimation of ovarian mass, which may contribute to final clinical decision.

Journal Article↗
PubMed2004

Characteristics of childhood cancer survivors predicted their successful tracing.

OBJECTIVE: The Childhood Cancer Survivor Study is a retrospective cohort study that was initiated to explore late effects of childhood cancer and its therapies. We evaluated the characteristics of those requiring tracing and factors that influenced tracing success. STUDY DESIGN AND SETTING: Medical record review identified 20,051 eligible individuals from 25 institutions in the United States and Canada. Of these, 13,021 had a current address in the medical record at the treating institution, and 7,030 had an incorrect address and required tracing by a commercial firm. RESULTS: Tracing was successful for 4,188 persons (60%). Younger age at contact, shorter length of time since last contact, having a middle initial available, an uncommon last name, and socioeconomic factors were found to predict successful tracing. Compared to those with a current address available in medical records, subjects successfully traced were less likely to have accessed health care during the previous 2 years; and more likely to be current smokers, obese, and to report moderate to severe impairments (pain, functional status, and activity). CONCLUSION: These findings provide an empirical basis concerning determinants and predictors of tracing success. If tracing had not been performed in this cohort, spurious associations may have been obtained for some health outcomes of interest.

Adolescent↗
PubMed2004

Gene expression profiling for molecular characterization of inflammatory breast cancer and prediction of response to chemotherapy.

Inflammatory breast cancer (IBC) is a rare but aggressive form of breast cancer with a 5-year survival limited to approximately 40%. Diagnosis, based on clinical and/or pathological criteria, may be difficult. Optimal systemic neoadjuvant therapy and accurate predictors of pathological response have yet to be defined for increasing response rate and survival. Using DNA microarrrays containing approximately 8,000 genes, we profiled breast cancer samples from 81 patients, including 37 with IBC and 44 with noninflammatory breast cancer (NIBC). Global unsupervised hierarchical clustering was able to some extent to distinguish IBC and NIBC cases and revealed subclasses of IBC. Supervised analysis identified a 109-gene set the expression of which discriminated IBC from NIBC samples. This molecular signature was validated in an independent series of 26 samples, with an overall performance accuracy of 85%. Discriminator genes were associated with various cellular processes possibly related to the aggressiveness of IBC, including signal transduction, cell motility, adhesion, and angiogenesis. A similar approach, with leave-one-out cross-validation, identified an 85-gene set that divided IBC patients with significantly different pathological complete response rate (70% in one group and 0% in the other group). These results show the potential of gene expression profiling to contribute to a better understanding of IBC, and to provide new diagnostic and predictive factors for IBC, as well as for potential therapeutic targets.

Adult↗
PubMed2001

Nipple aspirate cytology and pathologic parameters predict residual cancer and nodal involvement after excisional breast biopsy.

We previously demonstrated that abnormal nipple aspirate fluid (NAF) cytology predicted residual breast cancer (RC) and tumour size after excisional biopsy (EB), although normal NAF cytology did not exclude RC. Tumour size correlates with the risk of lymph node (LN) metastases. LN metastases provide prognostic information allowing medical and radiation oncologists to determine the need for adjuvant therapy. We hypothesized that pathologic factors known after EB, combined with NAF cytology, would predict with a high degree of accuracy the presence of RC and LN spread. NAF cytology and pathologic parameters: tumour distance from biopsy margins, multifocal and multicentric disease, sub-type of ductal carcinoma in situ (DCIS) or invasive cancer (IC), grade of DCIS or IC, tumour and specimen size, tumour and biopsy cavity location, presence or absence of extensive DCIS, and biopsy scar distance from the nipple were evaluated bivariately and then by logistic regression (LR) for their association with RC and involved LN (> or = 1 (+) LN, useful to determine chemotherapy need, and > or = 4 (+) LN, useful to determine radiation need to the chest and axilla). Data were analysed using NAF cytology alone, pathologic parameters alone, and NAF cytology and pathologic parameters combined. The combined LR model was superior in predicting residual cancer (94%) to LR models using NAF cytology (36%) or pathologic parameters (75%) alone. When only subjects with normal NAF cytology were evaluated by LR, the model was 92% sensitive in predicting RC. Tumour size and NAF cytology predicted which patients had > or = 1 (+) LN, whereas tumour and specimen size predicted which patients had > or = 4 (+) LN. We propose an algorithm which, if confirmed in a larger study, may allow clinicians to be more selective in their recommendations of re-excision breast biopsy or mastectomy.

Adult↗
PubMed2001

Utility of health belief model as a guide for explaining or predicting breast cancer screening behaviours.

AIM: The purpose of this study was to assess the utility of HBM as a theoretical guide for predicting breast cancer screening and therefore for guiding intervention studies. BACKGROUND: Breast cancer is the leading cause of death for middle age women (35-50) and the second leading cause of cancer deaths in all women in the United States (US). Early detection of breast cancer through screening is the only option available to women. However, less than half of all women in the US participate in screening. The health belief model (HBM), which specifies interactions of values and beliefs about health and their influence on choices, has been widely used to explain screening behaviour. METHODS: An integrative research review analysed 16 published descriptive studies employing HBM. Literature was located through a search of research based studies listed in Cumulative Index of Nursing and Allied Health (CINAHL), Medline, and cancer literature databases and studies cited in other references between 1990 and 1999. FINDINGS: Application of HBM was inconsistent. No study tested nonlinear relationships between variables as specified in the model. At best, the model explained 47% of the variance in screening behaviour when socioeconomic status was included. Otherwise predictive power was low, ranging from 15 to 27%. CONCLUSIONS: While the model provides some description of the values, beliefs and behaviours of middle-aged women primarily, HBM does not appear to have the power to consistently predict behaviours. Further research is needed to provide more thorough depiction of the social, nonhealth care meaning of breast cancer.

Adult↗