Lung cancer ranks as the second most prevalent cancer globally and is the primary contributor to neoplastic-related deaths. The approach to its treatment relies on both tumour staging and histological type determination. Data indicate that the prognosis of lung cancer is strongly linked to its clinical stage, underscoring the importance of early diagnosis in enhancing patient outcomes. Consequently, the choice of an appropriate diagnostic method holds significant importance in elevating both the early detection rate and prognosis of lung cancer. This paper aims to assess computer tomography features specific to the most common lung cancer types (adenocarcinoma, squamous cell carcinomas and small cell lung cancer). Data were collected retrospectively from CT scans of 58 patients pathologically diagnosed with lung cancer. The following CT features were evaluated and recorded for each case: location, margins, structure, lymph node involvement, cavitation, vascular bundle-thickening, bronchial obstruction, and pleural involvement. Squamous cell carcinoma (SQCC) and small cell lung cancer (SCLC) showed a higher incidence of central location, while adenocarcinoma (ADC) showed a significant predilection for a peripheral location. Internal cavitation was mostly observed in SQCC, and a solid structure was observed in almost all cases of ADC. These features can provide information about the prognosis of the patient, considering that NSCLCs are more frequent but tend to demonstrate positive results for targetable driver mutations, such as EGFR, thereby increasing the overall survival. In addition, SCLC presents with early distant spreads, which limits the opportunity to investigate the evolution of tumorigenesis and gene alterations at early stages but can have a rapidly positively response to chemotherapy. The location of the lung cancer exhibits distinct forecasts, with several studies suggesting that peripheral lung tumours offer a more favourable prognosis. Cavity formation appears correlate with a poorer prognosis. Histopathological analysis is the gold standard for diagnosing the type of lung cancer; however, using CT scanning for the purpose of a rough, but fast, preliminary diagnosis has the potential to shorten the waiting time for treatment by helping clinicians and patients to know more about the diagnosis and prognosis.

Our study seeks to study the accuracy of the ellipsoidal formula in prostate MRI of different sizes and to establish the limits of its use. The study included 31 patients with a well-visualized, intact prostatic capsule, excluding malignantly transformed prostates, as well as treated prostates, in which the contrast between the prostatic capsule and parenchyma is reduced. Each patient's prostatic volume was recalculated according to the ellipsoidal formula, and then it was compared with the prostatic volume calculated by the segmentation method. The two calculated volumes were similar, in some cases almost identical, with a slight tendency to underestimate prostate volume below 100cm3, in total in 18 cases, on average by 7.6% (+/-6%), overestimation of those with a volume over 100cm3, a total of 13 cases, on average by 3.2% (+/-2.5%), and of all, in 4 cases the difference between the two formulas was below 1%. There was no statistical difference between the two variables, Student's t-test p-value=0.039. With a precision of 92% (+/-6%), the ellipsoidal formula can be considered accurate when it is correctly performed, but if we take into account the importance that PSA density is starting to have in diagnosis, treatment and follow-up, the calculation of a secondary value through the segmentation method or high-precision software can be motivated when the ellipsoidal formula returns a value close to a threshold.

The premise of this paper starts from the fact that a more precise definition related to the intensity of the lesions on the ADC sequence can lead to a new subclassification of PI-RADS 3 lesions in the transitional zone and to an improvement of the specificity of the PI-RADS classification. The study was retrospective and included only patients who, based on prostate MRI examinations, contained exclusively PI-RADS 3 lesions, without other PI-RADS 4 or 5 lesions. The number of cases that meet all these conditions was reduced to 18, where a series of characteristics were noted for each one: PI-RADS 3 lesion area on the ADC sequence, the minimum and average ADC value of the lesion, the average ADC value of the transitional zone outside the lesion, PSA, prostatic volume, PSA density and biopsy result. The average ADC value of the negative lesions was 865(165) m2/sec, and of the positive ones was 869(118) m2/sec, which denies the hypothesis that there could be a value limit ADC to delimit PI-RADS 3 from PI-RADS 2 or 4 in this sample of patients. Furthermore, we reported the average ADC value of each such lesion to the adjacent unchanged transitional zone and obtained a greater difference of 432(163) m2/sec between the negative lesions and their adjacent transitional zone, compared to the difference of 399(127) m2/sec between the positive lesions and their adjacent ones.

Background: Artificial intelligence (AI) is increasingly used as a decision-support tool in mammography, but its influence on radiology residents’ interpretive behavior remains insufficiently characterized. This study evaluated the impact of AI assistance on BI-RADS classification and diagnostic confidence among radiology residents. Methods: This retrospective, single-center, multi-reader paired study included 112 diagnostic mammography examinations, corresponding to 223 assessable breasts and 2230 resident-breast readings. Ten radiology residents interpreted 2D mammography and digital breast tomosynthesis examinations first without AI assistance and subsequently with access to AI output. Changes in the BI-RADS category, diagnostic confidence, reasons for modification, and agreement with an expert-consensus BI-RADS reference standard were analyzed. Results: AI-assisted reassessment changed the BI-RADS classification in 9.7% of readings and diagnostic confidence in 19.2%, with any AI-associated modification observed in 24.3% of instances. Upgrades were more frequent than downgrades, particularly for medium- and high-suspicion AI outputs. Confidence increased more often than it decreased. Expert-reference agreement improved modestly, and BI-RADS 4+ sensitivity increased from 72.0% to 82.3%, with stable negative agreement, but these metrics reflect agreement with expert BI-RADS consensus rather than pathology-confirmed cancer detection. Conclusions: AI assistance influenced both BI-RADS reassessment and diagnostic confidence among radiology residents, producing modest but directionally favorable changes. These findings support cautious, supervised integration of AI into breast imaging training, with attention to confidence calibration and potential overreliance.

Purpose: To evaluate whether proton MR spectroscopy (¹H-MRS) improves the differentiation of tumoral from non-tumoral intracranial lesions beyond conventional MRI, and to quantify its effect on diagnostic confidence, in a consecutive real-world bicentric cohort of diagnostically ambiguous lesions. Methods: This retrospective, bicentric observational study screened 122 consecutive patients who underwent brain MRI with ¹H-MRS at two imaging centers, of whom 99 were eligible for final analysis after excluding 23 patients due to insufficient follow-up (<12 months) or incomplete reference standard. The reference standard was histopathological confirmation in 43 cases (43.4%) and structured clinico-radiological follow-up of at least 12 months in 56 cases (56.6%). The reporting radiologist’s binary diagnostic impression and confidence score were recorded before and after ¹H-MRS. Conventional MRI alone was compared to MRI plus ¹H-MRS using the exact McNemar test; Cho/NAA discrimination was assessed by ROC analysis with bootstrap confidence intervals, and confidence change by the Wilcoxon signed-rank test. Secondary analyses comprised an intention-to-diagnose analysis of all 99 examinations, counting non-diagnostic spectra as test failures, and an evaluable-case sensitivity analysis restricted to histopathologically confirmed patients with interpretable spectra. Results: Of 99 examinations, 89 (89.9%) yielded interpretable spectra. In the primary analysis, qualitative MRS interpretation demonstrated sensitivity of 76.0% (95% CI: 61.8–86.9), specificity of 94.9% (95% CI: 82.7–99.4), and accuracy of 84.3% (CI: 75.0–91.1), compared with 54.0%, 89.7% and 69.7% (59.0–79.0) for conventional MRI alone (exact McNemar p = 0.024). ROC analysis of the Cho/NAA ratio (n = 89) yielded an AUC of 0.858 (0.774–0.931); the exploratory, cohort-specific Youden-optimal cut-off was 1.41 (sensitivity 78.0%, specificity 87.2%). In the intention-to-diagnose analysis including all 99 examinations, accuracy was 78.8% (69.4–86.4). Diagnostic confidence increased significantly after ¹H-MRS (median 1 to 2; Wilcoxon p < 0.001; effect size r = 0.81), with moderate or major added value in 71 of 99 examinations (71.7%). Conclusions: In a diagnostically heterogeneous real-world cohort, ¹H-MRS significantly improved the accuracy achievable with conventional MRI alone and substantially increased reported diagnostic confidence, with high specificity but only moderate sensitivity. A zone-based Cho/NAA interpretative framework better reflects biological overlap than rigid binary thresholds. As this study assessed diagnostic accuracy and reported confidence rather than therapeutic decisions or patient outcomes, ¹H-MRS should be regarded as an adjunctive decision-support modality rather than a standalone classifier in routine neuro-oncologic workflows.

Background: Breast cancer is the most prevalent malignancy among women globally. In Romania, it is the most frequent form of cancer affecting women, with approximately 12,000 new cases diagnosed annually, and the second most common cause of cancer-related mortality, second only to lung cancer. Methods: This study looked at 79 breast cancer patients from Oltenia, concentrating on epidemiology, histology, diagnostic features, and treatments. Patients were chosen based on inclusion criteria such as histopathologically verified diagnosis, availability of clinical and treatment data, and follow-up information. The analyzed biological material consisted of tissue samples taken from the breast parenchyma and axillary lymph nodes. Even though not the primary subject of this paper, all patients underwent immunohistochemical (IHC) evaluation both preoperatively and postoperatively. Results: We found invasive ductal carcinoma to be the predominant type, while ductal carcinoma in situ (DCIS) and mixed types were rare. We performed cross-tabulations of metastasis versus nodal status and age versus therapy type; none reached significance (all p > 0.05), suggesting observed differences were likely due to chance. A chi-square test comparing surgical interventions (breast-conserving vs. mastectomy) in patients who did or did not receive chemotherapy showed, χ² = 3.17, p = 0.367, indicating that chemotherapy did not significantly influence surgical choice. Importantly, adjuvant chemotherapy and radiotherapy were used at similar rates across age groups, whereas neoadjuvant hormonal (endocrine) therapy was more common in older patients (but without statistical significance). Conclusions: Finally, we discussed the consequences of individualized care and early detection. Romania’s shockingly low screening rate, which contributes to delayed diagnosis, emphasizes the importance of improved population medical examination and tailored treatment options. Also, the country has one of the lowest rates of mammography uptake in Europe and no systematic population screening program.