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PubMed1987

Magnetic resonance imaging versus computed tomography in the evaluation of soft tissue tumors of the extremities.

Twenty patients with extremity soft tissue tumors were prospectively evaluated with magnetic resonance imaging (MRI) and computed tomography (CT) scans with subsequent anatomic correlation of surgical findings. MRI and CT had a similar percentage of accuracy in assessing tumor relationship with major neurovascular (80% and 70%, respectively) and skeletal (80% and 75%, respectively) structures. MRI was significantly better than CT in displaying contrast between tumor and muscle when using the T2 weighted spin echo (SE) (p2 less than 0.002) and inversion recovery (IR) (p2 less than 0.005) pulse sequences. MRI and CT were comparable in demonstrating contrast between tumor and fat. The contrast between tumor and vessel was better displayed by MRI compared with CT when using the T1 weighted SE (p2 less than 0.001) and T2 weighted SE (p2 less than 0.001) pulse sequences. T1 and T2 values were measured on fresh tumor and normal tissue samples and were used to predict relative contrast on different MRI pulse sequences using isosignal contour plots. MRI appears to offer several advantages over CT in the evaluation of extremity soft tissue tumors.

Arm↗
PubMed1979

Use of computed tomography in diagnosis of soft-tissue tumors.

Soft-tissue tumors can evade the usual diagnostic methods. In the case presented, computed tomography proved to be the best diagnostic tool in demonstrating a malignant extranodal lymphoma. Computed tomography is useful not only in detecting a tumor but also in determining its extent. This aids the surgeon in selecting and conducting the most appropriate operation.

Abdominal Neoplasms↗
PubMedDate not supplied

Computed tomography of pelvic lipomatosis. Report of a case.

Pelvic lipomatosis is a non-malignant condition of unknown etiology characterized by an overgrowth of non-encapsulated fatty tissue in the perirectal and perivesical spaces of the pelvis. The symptoms are generally vague and the condition is often diagnosed accidentally. It may cause obstruction of the ureters, inferior vena cava and pelvic veins and may be associated with cystitis glandularis. The typical conventional radiographic findings, though not pathognomonic, are a high-positioned and pear-shaped bladder, tubular narrowing of the rectum and distal sigmoid colon and reduced attenuation of the pelvic soft tissues. Computed tomography demonstrates a non-encapsulated fatty mass surrounding the pelvic organs symmetrically and with an attenuation similar to that of subcutaneous fat. The fatty tissue may contain strands with a higher attenuation than that of fat. The findings at computed tomography seem to be pathognomonic for this condition and eliminate the need for routine surgical biopsies.

Adult↗
PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗
PubMed1988

[Malignant fibrous histiocytoma of soft tissue. Possibilities and limitations of computed tomography].

Computed tomography was performed in 27 patients with confirmed malignant fibrous histiocytoma of the soft tissue. Compared with the CT results obtained in other tumours of the soft tissues. CT accuracy in malignant fibrous histiocytoma was only 74%. Non-typical attenuation values, diffuse tumour growth and non-typical contrast enhancement are discussed as reasons for the diagnostic understaging or overstaging of these soft tissue tumours. A higher accuracy was found on ultrasound examination with regard to tumour extension. Bone destruction was better visualised by CT. A base-line CT is recommended postoperatively for the earlier detection of recurrent tumours in further investigations.

Adult↗
PubMed1985

Magnetic resonance imaging of bone and soft tissue tumors: early experience in 31 patients compared with computed tomography.

In 31 patients with 21 soft tissue and 10 bone tumors, magnetic resonance imaging (MRI) and computed tomography (CT) were equally effective in delineating the margins of most soft tissue tumors, and the margins of bone tumors from fat and adjacent normal bone. However, MRI was superior to CT in delineating bone tumors from adjacent muscle, and in showing the relationships to bone of the deep margins of some soft tissue tumors. This was true because the quality of CT images around thick cortical bone often was severely degraded by streak artifact, which does not occur in MRI. Excellent anatomic detail was achieved on MRI by spin echo pulse sequences with short repetition times. Bone tumors were delineated best by spin echo 1000/30 images, and soft tissue tumors by spin echo 1000/30 or inversion recovery images.

Bone Neoplasms↗
PubMed1986

[Diagnostic imaging of peripheral soft tissue tumors with special reference to computed tomography].

The further development of modern therapeutical procedures in the treatment of soft tissue tumors requires an improvement of radiological diagnostics. The diagnostic values of X-rays, angiography, sonography and computed tomography is estimated critically. The results of own CT examinations in 120 patients with soft tissue tumors and of 50 ultrasound examinations show the importance of these methods in the extent diagnosis of soft tissue tumors. Ultrasound or CT- guided biopsy are helpful to prove the definitive diagnosis. These methods have reduced the use of angiography.

Adolescent↗
PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗
PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗
PubMed1988

Evaluating glass, polystyrene, and polypropylene containers for semen collection and sperm washing.

The effects of glass, polystyrene, and polypropylene containers on semen parameters as measured by Cell Soft computer-assisted semen analysis and hypoosmotic swelling (HOS) tests were assessed in unwashed specimens and again after sperm washing and swim-up procedures. A superiority in unwashed specimens was observed in glass versus polystyrene concerning velocity, motility percentage, and HOS testing (p less than 0.01). Also, superiority of glass versus polystyrene was demonstrated for linearity and ALH (p less than 0.05). Glass was only superior to polypropylene in categories of motility and HOS testing (p less than 0.05 and p less than 0.01, respectively). There was no category in which plastic was statistically superior to glass in the unwashed specimens. Concerning washed and swim-up specimens, there were no semen parameters in which there was any superiority demonstrated of either glass or plastic. Unless some future proof that the differences in semen parameters demonstrated in this study have no clinical significance, these data suggest that the collection of semen samples for sperm analysis or therapeutic use should be performed in glass containers.

Glass↗
PubMed1990

Detection of soft-tissue foreign bodies by plain radiography, xerography, computed tomography, and ultrasonography.

Detection of a soft-tissue foreign body is common yet often difficult, particularly when the foreign material is not radiopaque. Various imaging modalities have been advocated for detecting foreign bodies that are not revealed by plain radiography. The abilities of plain radiography, xerography, computed tomography, and ultrasonography to detect glass, wooden, and plastic foreign bodies in an in vitro preparation are compared. While all of these imaging techniques demonstrated a glass foreign body, only ultrasonography clearly identified wooden and plastic foreign bodies.

Evaluation Studies as Topic↗
PubMedDate not supplied

[Computed tomography in the diagnosis of soft tissue neoplasms of the trunk and extremities].

Analysis of CT data on 213 patients with soft tissue and trunk tumors has shown that a majority of malignant and benign tumors have a similar picture (except lipoma). Features of the contours of a tumor and its inner structure do not permit the assessment of its nature. The only significant differential-diagnostic sign of malignant soft tissue tumors is destruction of an adjacent bone, noted in 17.6%. The majority of malignant and benign soft tissue tumors (70.9%) on CT scans look like a single node; recurrent tumors look multinodular (78.2%). Verification of soft tissue tumors, revealed by CT, should be done using morphological methods.

Adult↗
PubMed1985

CT of soft-tissue neoplasms.

The computed tomographic scans (CT) of 84 patients with untreated soft-tissue neoplasms were studied, 75 with primary and nine with secondary lesions. Each scan was evaluated using several criteria: homogeneity and density, presence and type of calcification, presence of bony destruction, involvement of multiple muscle groups, definition of adjacent fat, border definition, and vessel or nerve involvement. CT demonstrated the lesion in all 84 patients and showed excellent anatomic detail in 64 of the 75 patients with primary neoplasms. CT did not differentiate vessel or nerve entrapment from neurovascular structures that were simply applied to the pseudocapsule of the tumor. Blurring of adjacent fat was an infrequent finding, but when it was present, the tumor was malignant. The CT findings were characteristic enough to suggest the histology of the neoplasm in only 13 lesions (nine lipomas, three hemangiomas, one neurofibroma). No malignant neoplasm had CT characteristics specific enough to differentiate it from any other malignant tumor. However, malignant neoplasms could be differentiated from benign neoplasms in 88% of the cases.

Adipose Tissue↗
PubMed1990

The value of computed tomography in the diagnosis of soft-tissue swellings of the hand.

Twenty patients with palpable swellings of the hand were investigated by computed tomography. The results, when compared with the pathological findings, lead us to consider this a technique of considerable value in the assessment of this kind of disease. The importance of angiography and, occasionally of magnetic resonance imaging, are also stressed.

Adolescent↗
PubMed1992

[A comparison between echography and computed tomography in assessing neoplastic recurrences in superficial soft tissues].

Thirty-seven superficial soft-tissue recurrences were evaluated with ultrasonography (US) and computed tomography (CT) to assess the correct diagnostic approach. US and CT examinations were performed at the same time. High-frequency US probes and a third-generation CT scanner were employed; all the lesions underwent also histology or cytology. US correctly identified as recurrences or fibrous tissue all the 37 lesions, whereas CT diagnosed 30 lesions only. Seven of the 14 recurrences < 2 cm diameter were not demonstrated. In conclusion, US provides more reliable information than CT relative to small lesions, which suggests that US must be performed just after therapy. Nevertheless, when bone involvement is suspected, CT is required and its use is also suggested to monitor distant metastases.

Biopsy, Needle↗
PubMed1979

Computed tomography evaluation of fatty tumors of the somatic soft tissues: clinical utility and radiologic-pathologic correlation.

The role of computed tomography (CT) in the evaluation of fatty tumors of the somatic soft tissues was investigated. Six surgically proven cases of fatty tumors were studied preoperatively by CT and standard radiographic means--conventional radiographs, xeroradiography, and angiography. Our case material included a simple lipoma, two infiltrative lipomas, an angiolipoma, and two liposarcomas. The radiologic-pathologic correlation was evaluated with respect to the various imaging modalities. The unique tissue characteristics of fatty tumors makes them particularly adaptable to CT scanning. In addition to its ability to define accurately tissue densities, the facility of CT in depicting depth, size, and extent of the lesion in the axial plane was found to be most useful in the preoperative evaluation of our case material.

Adipose Tissue↗